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This PR updates the documentation regarding the 'semantic_text' field type.
Goals:

  • Link to new overview page from all relevant documentation pages
  • Audit docs-content repo for semantic query examples and replace with appropriate query typeses

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@kderusso kderusso left a comment

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LGTM

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@leemthompo leemthompo left a comment

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Just need to fix some links, thanks!


After reindexing the data into the `semantic-embeddings` index, you can perform hybrid search to combine semantic and lexical search results. Choose between [retrievers](retrievers-overview.md) or [{{esql}}](elasticsearch://reference/query-languages/esql.md) syntax to execute the query.

For an overview of all query types supported by `semantic_text` fields and guidance on when to use them, see [Querying `semantic_text` fields](https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/semantic-text.md#querying-semantic-text-fields).
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Suggested change
For an overview of all query types supported by `semantic_text` fields and guidance on when to use them, see [Querying `semantic_text` fields](https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/semantic-text.md#querying-semantic-text-fields).
For an overview of all query types supported by `semantic_text` fields and guidance on when to use them, see [Querying `semantic_text` fields](elasticsearch://reference/elasticsearch/mapping-reference/semantic-text.md#querying-semantic-text-fields).

(please double check the path :))

### Option 1: `semantic_text` [_semantic_text_workflow]

The simplest way to use NLP models in the {{stack}} is through the [`semantic_text` workflow](semantic-search/semantic-search-semantic-text.md). We recommend using this approach because it abstracts away a lot of manual work. All you need to do is create an index mapping to start ingesting, embedding, and querying data. There is no need to define model-related settings and parameters, or to create {{infer}} ingest pipelines.
The simplest way to use NLP models in the {{stack}} is through the [`semantic_text` workflow](semantic-search/semantic-search-semantic-text.md). We recommend using this approach because it abstracts away a lot of manual work. All you need to do is create an index mapping to start ingesting, embedding, and querying data. There is no need to define model-related settings and parameters, or to create {{infer}} ingest pipelines. For guidance on the available query types for `semantic_text`, see [Querying `semantic_text` fields](https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/semantic-text.md#querying-semantic-text-fields).
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Need to use cross-repo link syntax here


## Further examples and reading [semantic-text-further-examples]

* For an overview of all query types supported by `semantic_text` fields and guidance on when to use them, see [Querying `semantic_text` fields](https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/semantic-text.md#querying-semantic-text-fields).
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Need to use cross-repo link syntax here

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