51 lines
2.1 KiB
Markdown
51 lines
2.1 KiB
Markdown
---
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layout: default
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title: Neural sparse
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parent: Specialized queries
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grand_parent: Query DSL
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nav_order: 55
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---
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# Neural sparse query
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Introduced 2.11
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{: .label .label-purple }
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Use the `neural_sparse` query for vector field search in [neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/).
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## Request fields
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Include the following request fields in the `neural_sparse` query:
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```json
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"neural_sparse": {
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"<vector_field>": {
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"query_text": "<query_text>",
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"model_id": "<model_id>"
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}
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}
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```
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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.
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Field | Data type | Required/Optional | Description
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:--- | :--- | :---
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`query_text` | String | Required | The query text from which to generate vector embeddings.
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`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/).
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`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]({{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`. This field has been deprecated as of OpenSearch 2.12.
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#### Example request
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```json
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GET my-nlp-index/_search
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{
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"query": {
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"neural_sparse": {
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"passage_embedding": {
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"query_text": "Hi world",
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"model_id": "aP2Q8ooBpBj3wT4HVS8a"
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}
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}
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}
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}
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```
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{% include copy-curl.html %} |