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Author: ditaccms-bot

docs/sap-ai-core/apis-and-api-extensions-0cb7275.md

@@ -89,7 +89,7 @@ Enhance content generation with additional capabilities for business AI scenario
 </td>
 <td valign="top">
 
-[Orchestration API](https://api.sap.com/api/ORCHESTRATION_API/overview) 
+[Orchestration API](https://api.sap.com/api/ORCHESTRATION_API_v2/overview) 
 
 </td>
 </tr>

docs/sap-ai-core/choose-an-instance-57f4f19.md

@@ -18,12 +18,14 @@ Within SAP AI Core, the instances are selected via the `ai.sap.com/resourcePlan`
 
 There are limits to the default disk storage size for all these nodes. Datasets that are loaded to the nodes consume disk space. If you have large data sets \(larger than 30 GB\), or have large models, you may have to increase the disk size. To do so, use the persistent volume claim in Argo Workflows to specify the required disk size \(see [Volumes](https://argoproj.github.io/argo-workflows/walk-through/volumes/)\).
 
+Instance types are recommended because they provide access to newer hardware and higher performance specifications compared to resource plans.
+
 
 
 ## Instance Types
 
 > ### Restriction:  
-> Instance types are only available as part of the “extended” service plan. For more information on instances that can be used with the “standard” service plan.
+> Instance types are only available as part of the “extended” service plan. For more information on instances that can be used with the “standard” service plan, see [Resource Plans](https://help.sap.com/docs/sap-ai-core/sap-ai-core-service-guide/choose-resource-plan-train?version=CLOUD&q=instance+types#resource-plans).
 
 Within SAP AI Core, the instances are selected via the `ai.sap.com/instanceType` label in your workflow templates. It maps the selected resource and takes a string value, which is the ID of the instance
 

docs/sap-ai-core/choose-an-instance-abd672f.md

@@ -18,12 +18,14 @@ Within SAP AI Core, the instances are selected via the `ai.sap.com/resourcePlan`
 
 There are limits to the default disk storage size for all these nodes. Datasets that are loaded to the nodes consume disk space. If you have large data sets \(larger than 30 GB\), or have large models, you may have to increase the disk size. To do so, use the persistent volume claim in Argo Workflows to specify the required disk size \(see [Volumes](https://argoproj.github.io/argo-workflows/walk-through/volumes/)\).
 
+Instance types are recommended because they provide access to newer hardware and higher performance specifications compared to resource plans.
+
 
 
 ## Instance Types
 
 > ### Restriction:  
-> Instance types are only available as part of the “extended” service plan. For more information on instances that can be used with the “standard” service plan.
+> Instance types are only available as part of the “extended” service plan. For more information on instances that can be used with the “standard” service plan, see [Resource Plans](https://help.sap.com/docs/sap-ai-core/sap-ai-core-service-guide/choose-resource-plan-train?version=CLOUD&q=instance+types#resource-plans).
 
 Within SAP AI Core, the instances are selected via the `ai.sap.com/instanceType` label in your workflow templates. It maps the selected resource and takes a string value, which is the ID of the instance
 

docs/sap-ai-core/choose-an-instance-c58d4e5.md

@@ -4,13 +4,9 @@
 
 Use cloud storage with SAP AI Core to store AI assets such as datasets and model files. You use Artifacts in SAP AI Core to reference to your AI Assets.
 
--   **[Manage Files](manage-files-386ba71.md "An artifact
-      refers
-      to data or a file that is produced or consumed by
-      executions or
-      deployments.
-      They are managed through SAP AI Core and your connected object
-      store.")**  
+-   **[Manage Files](manage-files-386ba71.md "An artifact refers to data or a file that is produced or consumed by executions or
+      deployments. They are managed through SAP AI Core and your
+      connected object store.")**  
 An artifact refers to data or a file that is produced or consumed by executions or deployments. They are managed through SAP AI Core and your connected object store.
 -   **[Manage Files Using the Dataset API](manage-files-using-the-dataset-api-ba8ac5c.md "Where direct access to files in the object store is not possible or desirable (for example, in Content as a Service Scenarios, where the
 		Service Consumers might not be the owners of the object store) you can upload, download, and delete files from the pre-registered object store

docs/sap-ai-core/connect-your-data-9508bdb.md

@@ -23,6 +23,8 @@ This API call creates a pipeline for indexing documents for a resource group.
 
 You can schedule automatic content updates by using a `cronExpression` when you create your pipeline. For more information, see [Cron Expressions](cron-expressions-6175008.md).
 
+For repositories that support it, you can include metadata in your pipeline. Metadata is additional information associated with data that can help in identifying, categorizing, and retrieving relevant content.
+
 > ### Tip:  
 > If you use the pipelines API, you do not need to call the Vector API separately. After the data is embedded, you can directly use the Retrieval API to query the vector store for relevant sections.
 
@@ -62,4 +64,6 @@ You can manually restart a pipeline. For more information, see [Manually Restart
 
 -   **[Cron Expressions](cron-expressions-515e839.md "")**  
 
+-   **[Update Metadata](update-metadata-3df4881.md "")**  
+
 

docs/sap-ai-core/create-a-document-grounding-pipeline-using-the-pipelines-api-0a13e1c.md

@@ -23,6 +23,8 @@ This API call creates a pipeline for indexing documents for a resource group.
 
 You can schedule automatic content updates by using a `cronExpression` when you create your pipeline. For more information, see [Cron Expressions](cron-expressions-6175008.md).
 
+For repositories that support it, you can include metadata in your pipeline. Metadata is additional information associated with data that can help in identifying, categorizing, and retrieving relevant content.
+
 > ### Tip:  
 > If you use the pipelines API, you do not need to call the Vector API separately. After the data is embedded, you can directly use the Retrieval API to query the vector store for relevant sections.
 
@@ -62,4 +64,6 @@ You can manually restart a pipeline. For more information, see [Manually Restart
 
 -   **[Cron Expressions](cron-expressions-6175008.md "")**  
 
+-   **[Update Metadata](update-metadata-fb69180.md "")**  
+
 

docs/sap-ai-core/create-a-document-grounding-pipeline-using-the-pipelines-api-d32b146.md

@@ -120,6 +120,8 @@ Name of the generic secret created for AWS S3
 
 ## With Metadata
 
+Metadata is additional information associated with data that can help in identifying, categorizing, and retrieving relevant content.
+
 The metadata attribute is optional. It accepts the destination name, which is used to connect to the Microsoft SharePoint metadata server to retrieve metadata for document indexing.
 
 The metadata attribute should be used only if a metadata server is configured. To create a grounding pipeline without metadata, see [Create a Document Grounding Pipeline Using the Pipelines API](create-a-document-grounding-pipeline-using-the-pipelines-api-d32b146.md).
@@ -228,7 +230,7 @@ The name of the generic secret created for the AWS S3 Metadata Server
 > }'
 > ```
 
-To make your pipeline searchable, add `dataRepositoryMetadata` to the `metadata` field. For example:
+You can search a pipeline later in your workflow by using the `dataRepositoryMetadata` attribute in the `metadata` field. Metadata organizes information using categories \(keys\) paired with their related values. For example:
 
 ```
 ...
@@ -368,3 +370,5 @@ For more information, see [Cron Expressions](cron-expressions-6175008.md).
 
 After preparing your vectors, you can use the Retrieval API for chunks relevant to a query, or use the grounding module as part of an orchestration workflow for information retrieval and LLM interaction. For more information, see [Retrieval API](retrieval-api-281e8cf.md) or [Using the Grounding Module](using-the-grounding-module-e1c4dd1.md).
 
+You can add metadata to your pipeline, or update existing metadata by sending a patch request. For more information, see [Update Metadata](update-metadata-fb69180.md).
+