2.0 KiB
layout | title | parent | grand_parent | nav_order |
---|---|---|---|---|
default | Neural | Specialized queries | Query DSL | 50 |
Neural query
Use the neural
query for vector field search in neural search.
Request fields
Include the following request fields in the neural
query:
"neural": {
"<vector_field>": {
"query_text": "<query_text>",
"query_image": "<image_binary>",
"model_id": "<model_id>",
"k": 100
}
}
The top-level vector_field
specifies the vector field against which to run a search query. The following table lists the other neural query fields.
Field | Data type | Required/Optional | Description
:--- | :--- | :---
query_text
| String | Optional | The query text from which to generate vector embeddings. You must specify at least one query_text
or query_image
.
query_image
| String | Optional | A base-64 encoded string that corresponds to the query image from which to generate vector embeddings. You must specify at least one query_text
or query_image
.
model_id
| String | Required if the default model ID is not set. For more information, see Setting a default model on an index or field. | The ID of the 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 neural search. For more information, see Using custom models within OpenSearch and Semantic search.
k
| Integer | Optional | The number of results returned by the k-NN search. Default is 10.
Example request
GET /my-nlp-index/_search
{
"query": {
"neural": {
"passage_embedding": {
"query_text": "Hi world",
"query_image": "iVBORw0KGgoAAAAN...",
"k": 100
}
}
}
}
{% include copy-curl.html %}