Renaming search connectors to content connectors

deploy-manage/manage-connectors.md

@@ -15,7 +15,7 @@ products:
 Connectors serve as a central place to store connection information for both Elastic and third-party systems. They enable the linking of actions to rules, which execute as background tasks on the {{kib}} server when rule conditions are met. This allows rules to route actions to various destinations such as log files, ticketing systems, and messaging tools. Different {{kib}} apps may have their own rule types, but they typically share connectors. The **{{stack-manage-app}} > {{connectors-ui}}** provides a central location to view and manage all connectors in the current space.
 
 ::::{note}
-This page is about {{kib}} connectors that integrate with services like generative AI model providers. If you’re looking for Search connectors that synchronize third-party data into {{es}}, refer to [Connector clients](elasticsearch://reference/search-connectors/index.md).
+This page is about {{kib}} connectors that integrate with services like generative AI model providers. If you’re looking for Content connectors that synchronize third-party data into {{es}}, refer to [Connector clients](elasticsearch://reference/search-connectors/index.md).
 
 ::::
 

reference/ingestion-tools/index.md

@@ -10,7 +10,7 @@ This section contains reference information for ingestion tools, including:
 * Logstash Plugins
 * Logstash Versioned Plugin Reference
 * Elastic Serverless forwarder for AWS
-* Search connectors
+* Content connectors
 * ES for Apache Hadoop
 * Elastic in integrations
 

solutions/observability/observability-ai-assistant.md

@@ -51,7 +51,7 @@ The AI assistant requires the following:
 * The knowledge base requires a 4 GB {{ml}} node.
   - In {{ecloud}} or {{ece}}, if you have Machine Learning autoscaling enabled, Machine Learning nodes will be started when using the knowledge base and AI Assistant. Therefore using these features will incur additional costs.
 
-* A self-deployed connector service if [search connectors](elasticsearch://reference/search-connectors/index.md) are used to populate external data into the knowledge base.
+* A self-deployed connector service if [content connectors](elasticsearch://reference/search-connectors/index.md) are used to populate external data into the knowledge base.
 
 ## Your data and the AI Assistant [data-information]
 
@@ -107,7 +107,7 @@ The AI Assistant uses [ELSER](/explore-analyze/machine-learning/nlp/ml-nlp-elser
 Add data to the knowledge base with one or more of the following methods:
 
 * [Use the knowledge base UI](#obs-ai-kb-ui) available at [AI Assistant Settings](#obs-ai-settings) page.
-* [Use search connectors](#obs-ai-search-connectors)
+* [Use content connectors](#obs-ai-search-connectors)
 
 You can also add information to the knowledge base by asking the AI Assistant to remember something while chatting (for example, "remember this for next time"). The assistant will create a summary of the information and add it to the knowledge base.
 
@@ -131,9 +131,9 @@ To add external data to the knowledge base in {{kib}}:
         }
         ```
 
-### Use search connectors [obs-ai-search-connectors]
+### Use content connectors [obs-ai-search-connectors]
 
-[Search connectors](elasticsearch://reference/search-connectors/index.md) index content from external sources like GitHub, Confluence, Google Drive, Jira, S3, Teams, and Slack to improve the AI Assistant's responses.
+[Content connectors](elasticsearch://reference/search-connectors/index.md) index content from external sources like GitHub, Confluence, Google Drive, Jira, S3, Teams, and Slack to improve the AI Assistant's responses.
 
 #### Requirements and limitations
 
@@ -190,7 +190,7 @@ This is a more complex method that requires you to set up the ELSER model and in
 
 To create the embeddings needed by the AI Assistant (weights and tokens into a sparse vector field) using an **ML Inference Pipeline**:
 
-1. Open the previously created search connector in **Content / Connectors**, and select the **Pipelines** tab.
+1. Open the previously created content connector in **Content / Connectors**, and select the **Pipelines** tab.
 2. Select **Copy and customize** under `Unlock your custom pipelines`.
 3. Select **Add Inference Pipeline** under `Machine Learning Inference Pipelines`.
 4. Select the **ELSER (Elastic Learned Sparse EncodeR)** ML model to add the necessary embeddings to the data.
@@ -397,7 +397,7 @@ The AI Assistant Settings page contains the following tabs:
 
 * **Settings**: Configures the main AI Assistant settings, which are explained directly within the interface.
 * **Knowledge base**: Manages [knowledge base entries](#obs-ai-kb-ui).
-* **Search Connectors**: Provides a link to {{kib}} **Search** → **Content** → **Connectors** UI for connectors configuration.
+* **Content connectors**: Provides a link to {{kib}} **Search** → **Content** → **Connectors** UI for connectors configuration.
 
 ### Add Elastic documentation [obs-ai-product-documentation]
 

solutions/search/ingest-for-search.md

@@ -43,7 +43,7 @@ You can use these specialized tools to add general content to {{es}} indices.
 | 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**](https://github.com/elastic/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|
+| [**Content connectors**](https://github.com/elastic/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/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 |
 
 ### Process data at ingest time

solutions/search/search-pipelines.md

@@ -41,7 +41,7 @@ These tools can be particularly helpful by providing a layer of customization an
 
 It can be a lot of work to set up and manage production-ready pipelines from scratch. Considerations such as error handling, conditional execution, sequencing, versioning, and modularization must all be taken into account.
 
-To this end, when you create indices for search use cases, (including web crawler, search connectors and API indices), each index already has a pipeline set up with several processors that optimize your content for search.
+To this end, when you create indices for search use cases, (including web crawler, content connectors and API indices), each index already has a pipeline set up with several processors that optimize your content for search.
 
 This pipeline is called `search-default-ingestion`. 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. You can also [read more about its contents below](#ingest-pipeline-search-details-generic-reference).