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Replace 'ent-search-generic' with 'search-default' pipeline
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docs/reference/connector/docs/connectors-content-extraction.asciidoc

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@@ -8,7 +8,7 @@ The logic for content extraction is defined in {connectors-python}/connectors/ut
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While intended primarily for PDF and Microsoft Office formats, you can use any of the <<es-connectors-content-extraction-supported-file-types, supported formats>>.
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Enterprise Search uses an {ref}/ingest.html[Elasticsearch ingest pipeline^] to power the web crawler's binary content extraction.
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The default pipeline, `ent-search-generic-ingestion`, is automatically created when Enterprise Search first starts.
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The default pipeline, `content-generic-ingestion`, is automatically created when Enterprise Search first starts.
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You can {ref}/ingest.html#create-manage-ingest-pipelines[view^] this pipeline in Kibana.
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Customizing your pipeline usage is also an option.

docs/reference/connector/docs/connectors-filter-extract-transform.asciidoc

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@@ -13,7 +13,7 @@ The following diagram provides an overview of how content extraction, sync rules
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[.screenshot]
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image::images/pipelines-extraction-sync-rules.png[Architecture diagram of data pipeline with content extraction, sync rules, and ingest pipelines]
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By default, only the connector specific logic (2) and the default `ent-search-generic-ingestion` pipeline (6) extract and transform your data, as configured in your deployment.
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By default, only the connector specific logic (2) and the default `search-default-ingestion` pipeline (6) extract and transform your data, as configured in your deployment.
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The following tools are available for more advanced use cases:
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@@ -50,4 +50,4 @@ Use ingest pipelines for data enrichment, normalization, and more.
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Elastic connectors use a default ingest pipeline, which you can copy and customize to meet your needs.
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Refer to {ref}/ingest-pipeline-search.html[ingest pipelines in Search] in the {es} documentation.
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Refer to {ref}/ingest-pipeline-search.html[ingest pipelines in Search] in the {es} documentation.

docs/reference/ingest/search-inference-processing.asciidoc

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@@ -88,7 +88,7 @@ The `monitor_ml` <<security-privileges, Elasticsearch cluster privilege>> is req
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To create the index-specific ML inference pipeline, go to *Search -> Content -> Indices -> <your index> -> Pipelines* in the Kibana UI.
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If you only see the `ent-search-generic-ingestion` pipeline, you will need to click *Copy and customize* to create index-specific pipelines.
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If you only see the `search-default-ingestion` pipeline, you will need to click *Copy and customize* to create index-specific pipelines.
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This will create the `{index_name}@ml-inference` pipeline.
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Once your index-specific ML inference pipeline is ready, you can add inference processors that use your ML trained models.

docs/reference/ingest/search-ingest-pipelines.asciidoc

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@@ -40,7 +40,7 @@ Considerations such as error handling, conditional execution, sequencing, versio
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To this end, when you create indices for search use cases, (including {enterprise-search-ref}/crawler.html[Elastic web crawler], <<es-connectors,connectors>>.
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, and API indices), each index already has a pipeline set up with several processors that optimize your content for search.
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This pipeline is called `ent-search-generic-ingestion`.
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This pipeline is called `search-default-ingestion`.
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While it is a "managed" pipeline (meaning it should not be tampered with), you can view its details via the Kibana UI or the Elasticsearch API.
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You can also <<ingest-pipeline-search-details-generic-reference,read more about its contents below>>.
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@@ -56,14 +56,14 @@ This will not effect existing indices.
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Each index also provides the capability to easily create index-specific ingest pipelines with customizable processing.
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If you need that extra flexibility, you can create a custom pipeline by going to your pipeline settings and choosing to "copy and customize".
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This will replace the index's use of `ent-search-generic-ingestion` with 3 newly generated pipelines:
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This will replace the index's use of `search-default-ingestion` with 3 newly generated pipelines:
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1. `<index-name>`
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2. `<index-name>@custom`
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3. `<index-name>@ml-inference`
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Like `ent-search-generic-ingestion`, the first of these is "managed", but the other two can and should be modified to fit your needs.
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You can view these pipelines using the platform tools (Kibana UI, Elasticsearch API), and can also
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Like `search-default-ingestion`, the first of these is "managed", but the other two can and should be modified to fit your needs.
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You can view these pipelines using the platform tools (Kibana UI, Elasticsearch API), and can also
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<<ingest-pipeline-search-details-specific,read more about their content below>>.
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[discrete#ingest-pipeline-search-pipeline-settings]
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=== Details
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[discrete#ingest-pipeline-search-details-generic-reference]
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==== `ent-search-generic-ingestion` Reference
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==== `search-default-ingestion` Reference
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You can access this pipeline with the <<get-pipeline-api, Elasticsearch Ingest Pipelines API>> or via Kibana's <<create-manage-ingest-pipelines,Stack Management > Ingest Pipelines>> UI.
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[discrete#ingest-pipeline-search-details-generic-reference-params]
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===== Control flow parameters
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The `ent-search-generic-ingestion` pipeline does not always run all processors.
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The `search-default-ingestion` pipeline does not always run all processors.
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It utilizes a feature of ingest pipelines to <<conditionally-run-processor,conditionally run processors>> based on the contents of each individual document.
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* `_extract_binary_content` - if this field is present and has a value of `true` on a source document, the pipeline will attempt to run the `attachment`, `set_body`, and `remove_replacement_chars` processors.
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==== Index-specific ingest pipelines
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In the Kibana UI for your index, by clicking on the Pipelines tab, then *Settings > Copy and customize*, you can quickly generate 3 pipelines which are specific to your index.
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These 3 pipelines replace `ent-search-generic-ingestion` for the index.
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There is nothing lost in this action, as the `<index-name>` pipeline is a superset of functionality over the `ent-search-generic-ingestion` pipeline.
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These 3 pipelines replace `search-default-ingestion` for the index.
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There is nothing lost in this action, as the `<index-name>` pipeline is a superset of functionality over the `search-default-ingestion` pipeline.
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[IMPORTANT]
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====
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[discrete#ingest-pipeline-search-details-specific-reference]
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===== `<index-name>` Reference
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This pipeline looks and behaves a lot like the <<ingest-pipeline-search-details-generic-reference,`ent-search-generic-ingestion` pipeline>>, but with <<ingest-pipeline-search-details-specific-reference-processors,two additional processors>>.
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This pipeline looks and behaves a lot like the <<ingest-pipeline-search-details-generic-reference,`search-default-ingestion` pipeline>>, but with <<ingest-pipeline-search-details-specific-reference-processors,two additional processors>>.
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[WARNING]
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=========================
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[discrete#ingest-pipeline-search-details-specific-reference-processors]
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====== Processors
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In addition to the processors inherited from the <<ingest-pipeline-search-details-generic-reference,`ent-search-generic-ingestion` pipeline>>, the index-specific pipeline also defines:
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In addition to the processors inherited from the <<ingest-pipeline-search-details-generic-reference,`search-default-ingestion` pipeline>>, the index-specific pipeline also defines:
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* `index_ml_inference_pipeline` - this uses the <<pipeline-processor, Pipeline>> processor to run the `<index-name>@ml-inference` pipeline.
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This processor will only be run if the source document includes a `_run_ml_inference` field with the value `true`.
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[discrete#ingest-pipeline-search-details-specific-reference-params]
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====== Control flow parameters
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Like the `ent-search-generic-ingestion` pipeline, the `<index-name>` pipeline does not always run all processors.
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Like the `search-default-ingestion` pipeline, the `<index-name>` pipeline does not always run all processors.
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In addition to the `_extract_binary_content` and `_reduce_whitespace` control flow parameters, the `<index-name>` pipeline also supports:
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* `_run_ml_inference` - if this field is present and has a value of `true` on a source document, the pipeline will attempt to run the `index_ml_inference_pipeline` processor.
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===== `<index-name>@ml-inference` Reference
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This pipeline is empty to start (no processors), but can be added to via the Kibana UI either through the Pipelines tab of your index, or from the *Stack Management > Ingest Pipelines* page.
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Unlike the `ent-search-generic-ingestion` pipeline and the `<index-name>` pipeline, this pipeline is NOT "managed".
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Unlike the `search-default-ingestion` pipeline and the `<index-name>` pipeline, this pipeline is NOT "managed".
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It's possible to add one or more ML inference pipelines to an index in the *Content* UI.
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This pipeline will serve as a container for all of the ML inference pipelines configured for the index.
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This pipeline is empty to start (no processors), but can be added to via the Kibana UI either through the Pipelines
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tab of your index, or from the *Stack Management > Ingest Pipelines* page.
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Unlike the `ent-search-generic-ingestion` pipeline and the `<index-name>` pipeline, this pipeline is NOT "managed".
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Unlike the `search-default-ingestion` pipeline and the `<index-name>` pipeline, this pipeline is NOT "managed".
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You are encouraged to make additions and edits to this pipeline, provided its name remains the same.
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This provides a convenient hook from which to add custom processing and transformations for your data.
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These changes should be re-applied to each index's `<index-name>@custom` pipeline in order to ensure a consistent data processing experience.
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In 8.5+, the <<ingest-pipeline-search-pipeline-settings, index setting to enable binary content>> is required *in addition* to the configurations mentioned in the {enterprise-search-ref}/crawler-managing.html#crawler-managing-binary-content[Elastic web crawler Guide].
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* `ent-search-generic-ingestion` - Since 8.5, Native Connectors, Connector Clients, and new (>8.4) Elastic web crawler indices will all make use of this pipeline by default.
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* `ent-search-generic-ingestion` - Since 8.5, Native Connectors, Connector Clients, and new (>8.4) Elastic web crawler indices all made use of this pipeline by default.
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This pipeline evolved into the `search-default-ingestion` pipeline.
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* `search-default-ingestion` - Since 9.0, Connectors have made use of this pipeline by default.
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You can <<ingest-pipeline-search-details-generic-reference, read more about this pipeline>> above.
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As this pipeline is "managed", any modifications that were made to `app_search_crawler` and/or `ent_search_crawler` should NOT be made to `ent-search-generic-ingestion`.
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As this pipeline is "managed", any modifications that were made to `app_search_crawler` and/or `ent_search_crawler` should NOT be made to `search-default-ingestion`.
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Instead, if such customizations are desired, you should utilize <<ingest-pipeline-search-details-specific>>, placing all modifications in the `<index-name>@custom` pipeline(s).
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=============
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docs/reference/ingest/search-nlp-tutorial.asciidoc

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@@ -164,8 +164,8 @@ Now it's time to create an inference pipeline.
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1. From the overview page for your `search-photo-comments` index in "Search", click the *Pipelines* tab.
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By default, Elasticsearch does not create any index-specific ingest pipelines.
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2. Because we want to customize these pipelines, we need to *Copy and customize* the `ent-search-generic-ingestion` ingest pipeline.
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Find this option above the settings for the `ent-search-generic-ingestion` ingest pipeline.
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2. Because we want to customize these pipelines, we need to *Copy and customize* the `search-default-ingestion` ingest pipeline.
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Find this option above the settings for the `search-default-ingestion` ingest pipeline.
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This will create two new index-specific ingest pipelines.
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Next, we'll add an inference pipeline.

x-pack/plugin/core/template-resources/src/main/resources/entsearch/connector/elastic-connectors-mappings.json

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"dynamic": "false",
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"_meta": {
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"pipeline": {
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"default_name": "ent-search-generic-ingestion",
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"default_name": "search-default-ingestion",
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"default_extract_binary_content": true,
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"default_run_ml_inference": true,
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"default_reduce_whitespace": true

x-pack/plugin/core/template-resources/src/main/resources/entsearch/generic_ingestion_pipeline.json

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x-pack/plugin/ent-search/src/main/java/org/elasticsearch/xpack/application/connector/ConnectorTemplateRegistry.java

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public static final String ACCESS_CONTROL_TEMPLATE_NAME = "search-acl-filter";
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// Pipeline constants
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public static final String ENT_SEARCH_GENERIC_PIPELINE_NAME = "ent-search-generic-ingestion";
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public static final String ENT_SEARCH_GENERIC_PIPELINE_FILE = "generic_ingestion_pipeline";
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public static final String SEARCH_DEFAULT_PIPELINE_NAME = "search-default-ingestion";
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public static final String SEARCH_DEFAULT_PIPELINE_FILE = "search_default_pipeline";
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@Override
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protected List<IngestPipelineConfig> getIngestPipelines() {
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return List.of(
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new JsonIngestPipelineConfig(
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ENT_SEARCH_GENERIC_PIPELINE_NAME,
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ROOT_RESOURCE_PATH + ENT_SEARCH_GENERIC_PIPELINE_FILE + ".json",
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REGISTRY_VERSION,
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TEMPLATE_VERSION_VARIABLE
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),
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SEARCH_DEFAULT_PIPELINE_NAME,
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x-pack/plugin/ent-search/src/test/java/org/elasticsearch/xpack/application/connector/ConnectorIngestPipelineTests.java

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{
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"extract_binary_content": true,
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"name": "ent-search-generic-ingestion",
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"name": "search-default-ingestion",
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"reduce_whitespace": true,
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"run_ml_inference": false
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}

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