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docs/reference/elasticsearch/mapping-reference/semantic-text.md

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navigation_title: "Semantic text"
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mapped_pages:
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- https://www.elastic.co/guide/en/elasticsearch/reference/current/semantic-text.html
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applies_to:
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stack: ga 9.0
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serverless: ga
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---
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# Semantic text field type [semantic-text]
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Using `semantic_text`, you won’t need to specify how to generate embeddings for
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your data, or how to index it. The {{infer}} endpoint automatically determines
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the embedding generation, indexing, and query to use.
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Newly created indices with `semantic_text` fields using dense embeddings will be
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{applies_to}`stack: ga 9.1` Newly created indices with `semantic_text` fields using dense embeddings will be
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[quantized](/reference/elasticsearch/mapping-reference/dense-vector.md#dense-vector-quantization)
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to `bbq_hnsw` automatically.
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see [Configuring chunking](https://www.elastic.co/docs/api/doc/elasticsearch/group/endpoint-inference)
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in the Inference API documentation.
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Refer
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to [this tutorial](docs-content://solutions/search/semantic-search/semantic-search-semantic-text.md)
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to learn more about semantic search using `semantic_text`.
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### Pre-chunking [pre-chunking]
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```{applies_to}
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stack: ga 9.1
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```
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You can pre-chunk the input by sending it to Elasticsearch as an array of
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strings.
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Example:
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* Others (such as `elastic` and `elasticsearch`) will automatically truncate
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the input.
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Refer
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to [this tutorial](docs-content://solutions/search/semantic-search/semantic-search-semantic-text.md)
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to learn more about semantic search using `semantic_text`.
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## Extracting relevant fragments from semantic text [semantic-text-highlighting]
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You can extract the most relevant fragments from a semantic text field by using
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automatic {{infer}} and a dedicated query so you don’t need to provide further
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details.
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### Customizing using `semantic_text` parameters [custom-by-parameters]
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```{applies_to}
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stack: ga 9.1
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```
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If you want to override those defaults and customize the embeddings that
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`semantic_text` indexes, you can do so by
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modifying [parameters](#semantic-text-params):
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}
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```
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### Customizing using ingest pipelines [custom-by-pipelines]
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```{applies_to}
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stack: ga 9.0
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```
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In case you want to customize data indexing, use the
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[`sparse_vector`](/reference/elasticsearch/mapping-reference/sparse-vector.md)
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or [`dense_vector`](/reference/elasticsearch/mapping-reference/dense-vector.md)
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field types and create an ingest pipeline with an
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[{{infer}} processor](/reference/enrich-processor/inference-processor.md) to
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generate the embeddings.
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[This tutorial](docs-content://solutions/search/semantic-search/semantic-search-inference.md)
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walks you through the process. In these cases - when you use `sparse_vector` or
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`dense_vector` field types instead of the `semantic_text` field type to
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customize indexing - using the
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[`semantic_query`](/reference/query-languages/query-dsl/query-dsl-semantic-query.md)
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is not supported for querying the field data.
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## Updates to `semantic_text` fields [update-script]
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For indices containing `semantic_text` fields, updates that use scripts have the

docs/reference/query-languages/query-dsl/query-dsl-semantic-query.md

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navigation_title: "Semantic"
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mapped_pages:
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- https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-semantic-query.html
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applies_to:
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stack: ga 9.0
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serverless: ga
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# Semantic query [query-dsl-semantic-query]

muted-tests.yml

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- class: org.elasticsearch.packaging.test.DockerTests
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method: test130JavaHasCorrectOwnership
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issue: https://github.com/elastic/elasticsearch/issues/131369
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- class: org.elasticsearch.packaging.test.DockerTests
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method: test151MachineDependentHeapWithSizeOverride
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issue: https://github.com/elastic/elasticsearch/issues/123437
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# Examples:
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#

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