53 lines
1.8 KiB
Markdown
53 lines
1.8 KiB
Markdown
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---
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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 [sparse neural 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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"max_token_score": "<max_token_score>"
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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/ml-framework/) and [Semantic search]({{site.url}}{{site.baseurl}}/ml-commons-plugin/semantic-search/).
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`max_token_score` | Float | Optional | The theoretical upper bound of the score for all tokens in the vocabulary (required for performance optimization).
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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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"max_token_score": 2
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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 %}
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