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Copy file name to clipboardExpand all lines: docs/overlays/elasticsearch-openapi-overlays.yaml
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title: Elasticsearch API
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description: >
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Elasticsearch provides REST APIs that are used by the UI components and can be called directly to configure and access Elasticsearch features.
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## Documentation source and versions
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This documentation is derived from the `main` branch of the [elasticsearch-specification](https://github.com/elastic/elasticsearch-specification) repository.
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It is provided under license [Attribution-NonCommercial-NoDerivatives 4.0 International](https://creativecommons.org/licenses/by-nc-nd/4.0/).
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This documentation contains work-in-progress information for future Elastic Stack releases.
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x-doc-license:
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name: Attribution-NonCommercial-NoDerivatives 4.0 International
Copy file name to clipboardExpand all lines: docs/overlays/elasticsearch-shared-overlays.yaml
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Connectors are Elasticsearch integrations that bring content from third-party data sources, which can be deployed on Elastic Cloud or hosted on your own infrastructure:
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* Native connectors are a managed service on Elastic Cloud
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* Connector clients are self-managed on your infrastructure
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* Elastic managed connectors (Native connectors) are a managed service on Elastic Cloud
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* Self-managed connectors (Connector clients) are self-managed on your infrastructure.
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This API provides an alternative to relying solely on Kibana UI for connector and sync job management. The API comes with a set of validations and assertions to ensure that the state representation in the internal index remains valid.
The Elasticsearch Query Language (ES|QL) provides a powerful way to filter, transform, and analyze data stored in Elasticsearch, and in the future in other runtimes.
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Additionally, the `ml_classic` tokenizer is available, which tokenizes in the same way as the non-customizable tokenizer in old versions of the product (before 6.2).
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`ml_classic` was the default categorization tokenizer in versions 6.2 to 7.13, so if you need categorization identical to the default for jobs created in these versions, specify `"tokenizer": "ml_classic"` in your `categorization_analyzer`.
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