[Search] Update/consolidate ingest section, move search pipelines content

explore-analyze/machine-learning/nlp/inference-processing.md

@@ -5,7 +5,7 @@ mapped_pages:
 
 # Inference processing [ingest-pipeline-search-inference]
 
-When you create an index through the **Content** UI, a set of default ingest pipelines are also created, including a ML inference pipeline. The [ML inference pipeline](../../../solutions/search/ingest-for-search.md#ingest-pipeline-search-details-specific-ml-reference) uses inference processors to analyze fields and enrich documents with the output. Inference processors use ML trained models, so you need to use a built-in model or [deploy a trained model in your cluster^](ml-nlp-deploy-models.md) to use this feature.
+When you create an index through the **Content** UI, a set of default ingest pipelines are also created, including a ML inference pipeline. The [ML inference pipeline](/solutions/search/search-pipelines.md#ingest-pipeline-search-details-specific-ml-reference) uses inference processors to analyze fields and enrich documents with the output. Inference processors use ML trained models, so you need to use a built-in model or [deploy a trained model in your cluster^](ml-nlp-deploy-models.md) to use this feature.
 
 This guide focuses on the ML inference pipeline, its use, and how to manage it.
 
@@ -129,7 +129,7 @@ To ensure the ML inference pipeline will be run when ingesting documents, you mu
 
 ## Learn More [ingest-pipeline-search-inference-learn-more]
 
-* See [Overview](../../../solutions/search/ingest-for-search.md#ingest-pipeline-search-in-enterprise-search) for information on the various pipelines that are created.
+* See [Overview](/solutions/search/search-pipelines.md#ingest-pipeline-search-in-enterprise-search) for information on the various pipelines that are created.
 * Learn about [ELSER](ml-nlp-elser.md), Elastic’s proprietary retrieval model for semantic search with sparse vectors.
 * [NER HuggingFace Models](https://huggingface.co/models?library=pytorch&pipeline_tag=token-classification&sort=downloads)
 * [Text Classification HuggingFace Models](https://huggingface.co/models?library=pytorch&pipeline_tag=text-classification&sort=downloads)

raw-migrated-files/toc.yml

@@ -603,7 +603,6 @@ toc:
       - file: elasticsearch/elasticsearch-reference/document-level-security.md
       - file: elasticsearch/elasticsearch-reference/documents-indices.md
       - file: elasticsearch/elasticsearch-reference/elasticsearch-intro-deploy.md
-      - file: elasticsearch/elasticsearch-reference/es-ingestion-overview.md
       - file: elasticsearch/elasticsearch-reference/es-security-principles.md
       - file: elasticsearch/elasticsearch-reference/esql-examples.md
       - file: elasticsearch/elasticsearch-reference/esql-getting-started.md
@@ -621,7 +620,6 @@ toc:
       - file: elasticsearch/elasticsearch-reference/index-modules-analysis.md
       - file: elasticsearch/elasticsearch-reference/index-modules-mapper.md
       - file: elasticsearch/elasticsearch-reference/ingest-enriching-data.md
-      - file: elasticsearch/elasticsearch-reference/ingest-pipeline-search.md
       - file: elasticsearch/elasticsearch-reference/ingest.md
       - file: elasticsearch/elasticsearch-reference/install-elasticsearch.md
       - file: elasticsearch/elasticsearch-reference/ip-filtering.md

solutions/search/ingest-for-search.md

@@ -6,35 +6,45 @@ mapped_urls:
   - https://www.elastic.co/guide/en/serverless/current/elasticsearch-ingest-your-data.html
 ---
 
-# Ingest for search
+# Ingest for search use cases
 
-% What needs to be done: Lift-and-shift
+% ----
+% navigation_title: "Ingest for search use cases"
+% ----
 
-% Scope notes: guidance on what ingest options you might want to use for search - connectors, crawler ...
+$$$elasticsearch-ingest-time-series-data$$$
+::::{note}
+This page covers ingest methods specifically for search use cases. If you're working with a different use case, refer to the [ingestion overview](/manage-data/ingest.md) for more options.
+::::
 
-% Use migrated content from existing pages that map to this page:
+Search use cases usually focus on general **content**, typically text-heavy data that does not have a timestamp. This could be data like knowledge bases, website content, product catalogs, and more.
 
-% - [ ] ./raw-migrated-files/elasticsearch/elasticsearch-reference/es-ingestion-overview.md
-% - [ ] ./raw-migrated-files/docs-content/serverless/elasticsearch-ingest-data-through-api.md
-% - [ ] ./raw-migrated-files/elasticsearch/elasticsearch-reference/ingest-pipeline-search.md
-% - [ ] ./raw-migrated-files/docs-content/serverless/elasticsearch-ingest-your-data.md
+Once you've decided how to [deploy Elastic](/deploy-manage/index.md), the next step is getting your content into {{es}}. Your choice of ingestion method depends on where your content lives and how you need to access it.
 
-% Internal links rely on the following IDs being on this page (e.g. as a heading ID, paragraph ID, etc):
+There are several methods to ingest data into {{es}} for search use cases. Choose one or more based on your requirements.
 
-$$$elasticsearch-ingest-time-series-data$$$
+::::{tip}
+If you just want to do a quick test, you can load [sample data](/manage-data/ingest/sample-data.md) into your {{es}} cluster using the UI.
+::::
+
+## Use APIs [es-ingestion-overview-apis] 
 
-$$$ingest-pipeline-search-details-specific-ml-reference$$$
+You can use the [`_bulk` API](https://www.elastic.co/docs/api/doc/elasticsearch/v8/group/endpoint-document) to add data to your {{es}} indices, using any HTTP client, including the [{{es}} client libraries](/solutions/search/site-or-app/clients.md).
 
-$$$ingest-pipeline-search-in-enterprise-search$$$
+While the {{es}} APIs can be used for any data type, Elastic provides specialized tools that optimize ingestion for specific use cases.
 
-$$$ingest-pipeline-search-details-generic-reference$$$
+## Use specialized tools [es-ingestion-overview-general-content]
 
-$$$ingest-pipeline-search-details-specific-custom-reference$$$
+You can use these specialized tools to add general content to {{es}} indices.
 
-$$$ingest-pipeline-search-details-specific-reference-processors$$$
+| Method | Description | Notes |
+|--------|-------------|-------|
+| [**Web crawler**](https://github.com/elastic/crawler) | Programmatically discover and index content from websites and knowledge bases | Crawl public-facing web content or internal sites accessible via HTTP proxy |
+| [**Search connectors**]() | Third-party integrations to popular content sources like databases, cloud storage, and business applications | Choose from a range of Elastic-built connectors or build your own in Python using the Elastic connector framework|
+| [**File upload**](/manage-data/ingest/tools/upload-data-files.md)| One-off manual uploads through the UI | Useful for testing or very small-scale use cases, but not recommended for production workflows |
 
-$$$ingest-pipeline-search-details-specific$$$
+### Process data at ingest time
 
-$$$ingest-pipeline-search-pipeline-settings-using-the-api$$$
+You can also transform and enrich your content at ingest time using [ingest pipelines](/manage-data/ingest/transform-enrich/ingest-pipelines.md).
 
-$$$ingest-pipeline-search-pipeline-settings$$$
+The Elastic UI has a set of tools for creating and managing indices optimized for search use cases. You can also manage your ingest pipelines in this UI. Learn more in [](search-pipelines.md).