Merge pull request #278381 from gmndrg/main

SharePoint doc updates based on customer feedback

Author: prmerger-automator[bot]

articles/search/cognitive-search-skill-vision-vectorize.md

@@ -9,7 +9,7 @@ ms.custom:
   - build-2024
   - references_regions
 ms.topic: reference
-ms.date: 05/28/2024
+ms.date: 06/14/2024
 ---
 
 #	Azure AI Vision multimodal embeddings skill
@@ -19,7 +19,7 @@ ms.date: 05/28/2024
 
 The **Azure AI Vision multimodal embeddings** skill uses Azure AI Vision's [multimodal embeddings API](../ai-services/computer-vision/concept-image-retrieval.md) to generate embeddings for image or text input.
 
-The skill is only supported in search services located in a region that supports the [Azure AI Vision Multimodal embeddings API](../ai-services/computer-vision/how-to/image-retrieval.md). Currently this is East US, France Central, Korea Central, North Europe, Southeast Asia, West Europe, and West US. Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed. 
+The skill is only supported in search services located in a region that supports the [Azure AI Vision Multimodal embeddings API](../ai-services/computer-vision/how-to/image-retrieval.md). Review [region availability for multimodal embeddings](../ai-services/computer-vision/overview-image-analysis.md?tabs=4-0#region-availability). Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed. 
 
 > [!NOTE]
 > This skill is bound to Azure AI services and requires [a billable resource](cognitive-search-attach-cognitive-services.md) for transactions that exceed 20 documents per indexer per day. Execution of built-in skills is charged at the existing [Azure AI services pay-as-you go price](https://azure.microsoft.com/pricing/details/cognitive-services/).
@@ -41,7 +41,7 @@ Parameters are case-sensitive.
 
 | Inputs | Description |
 |---------------------|-------------|
-| `modelVersion` | (Required) The model version to be passed to the Azure AI Vision multimodal embeddings API for generating embeddings. It is important that all embeddings stored in a given index field are generated using the same `modelVersion`. |
+| `modelVersion` | (Required) The model version to be passed to the Azure AI Vision multimodal embeddings API for generating embeddings. It is important that all embeddings stored in a given index field are generated using the same `modelVersion`. For information about version support for this model refer to [multimodal embeddings](../ai-services/computer-vision/concept-image-retrieval.md#what-are-vector-embeddings).|
 
 ## Skill inputs
 

articles/search/search-howto-index-sharepoint-online.md

@@ -9,7 +9,7 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 06/11/2024
+ms.date: 06/14/2024
 ---
 
 # Index data from SharePoint document libraries
@@ -248,7 +248,7 @@ api-key: [admin key]
 
 An indexer connects a data source with a target search index and provides a schedule to automate the data refresh. Once the index and data source are created, you can create the indexer.
 
-During this step, you’re asked to sign in with organization credentials that have access to the SharePoint site. If possible, we recommend creating a new organizational user account and giving that new user the exact permissions that you want the indexer to have. 
+If you are using delegated permissions, during this step, you’re asked to sign in with organization credentials that have access to the SharePoint site. If possible, we recommend creating a new organizational user account and giving that new user the exact permissions that you want the indexer to have. 
 
 There are a few steps to creating the indexer:
 
@@ -287,6 +287,8 @@ There are a few steps to creating the indexer:
     }
     ```
 
+If you're using application permissions, it's necessary to wait until the initial run is complete before starting to query your index. The following instructions provided in this step pertain specifically to delegated permissions, and are not applicable to application permissions.
+
 1. When you create the indexer for the first time, the [Create Indexer (preview)](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2023-10-01-preview&tabs=HTTP&preserve-view=true) request waits until you complete the next step. You must call [Get Indexer Status](/rest/api/searchservice/indexers/get-status?view=rest-searchservice-2023-10-01-preview&tabs=HTTP&preserve-view=true) to get the link and enter your new device code. 
 
     ```http
@@ -438,7 +440,7 @@ The "query" parameter of the data source is made up of keyword/value pairs. The
 | Keyword | Value description and examples |
 | ------- | ------------------------ |
 | null | If null or empty, index either the default document library or all document libraries depending on the container name.	<br><br>Example: <br><br>``` "container" : { "name" : "defaultSiteLibrary", "query" : null } ``` |
-| includeLibrariesInSite | Index content from all libraries under the specified site in the connection string. The scope includes any subsites of your site. The value should be the URI of the site or subsite. <br><br>Example: <br><br>```"container" : { "name" : "useQuery", "query" : "includeLibrariesInSite=https://mycompany.sharepoint.com/mysite" }``` |
+| includeLibrariesInSite | Index content from all libraries under the specified site in the connection string. The value should be the URI of the site or subsite. <br><br>Example 1: <br><br>```"container" : { "name" : "useQuery", "query" : "includeLibrariesInSite=https://mycompany.sharepoint.com/mysite" }``` <br><br>Example 2 (include a few subsites only): <br><br>```"container" : { "name" : "useQuery", "query" : "includeLibrariesInSite=https://mycompany.sharepoint.com/sites/TopSite/SubSite1;includeLibrariesInSite=https://mycompany.sharepoint.com/sites/TopSite/SubSite2" }``` |
 | includeLibrary | Index all content from this library. The value is the fully qualified path to the library, which can be copied from your browser: <br><br>Example 1 (fully qualified path): <br><br>```"container" : { "name" : "useQuery", "query" : "includeLibrary=https://mycompany.sharepoint.com/mysite/MyDocumentLibrary" }``` <br><br>Example 2 (URI copied from your browser): <br><br>```"container" : { "name" : "useQuery", "query" : "includeLibrary=https://mycompany.sharepoint.com/teams/mysite/MyDocumentLibrary/Forms/AllItems.aspx" }``` |
 | excludeLibrary | Don't index content from this library. The value is the fully qualified path to the library, which can be copied from your browser: <br><br> Example 1 (fully qualified path): <br><br>```"container" : { "name" : "useQuery", "query" : "includeLibrariesInSite=https://mysite.sharepoint.com/subsite1; excludeLibrary=https://mysite.sharepoint.com/subsite1/MyDocumentLibrary" }``` <br><br> Example 2 (URI copied from your browser): <br><br>```"container" : { "name" : "useQuery", "query" : "includeLibrariesInSite=https://mycompany.sharepoint.com/teams/mysite; excludeLibrary=https://mycompany.sharepoint.com/teams/mysite/MyDocumentLibrary/Forms/AllItems.aspx" }``` |
 | additionalColumns | Index columns from the document library. The value is a comma-separated list of column names you want to index. Use a double backslash to escape semicolons and commas in column names: <br><br> Example 1 (additionalColumns=MyCustomColumn,MyCustomColumn2):  <br><br>```"container" : { "name" : "useQuery", "query" : "includeLibrary=https://mycompany.sharepoint.com/mysite/MyDocumentLibrary;additionalColumns=MyCustomColumn,MyCustomColumn2" }``` <br><br> Example 2 (escape characters using double backslash): <br><br> ```"container" : { "name" : "useQuery", "query" : "includeLibrary=https://mycompany.sharepoint.com/teams/mysite/MyDocumentLibrary/Forms/AllItems.aspx;additionalColumns=MyCustomColumnWith\\,,MyCustomColumnWith\\;" }``` |

articles/search/vector-search-vectorizer-ai-services-vision.md

@@ -8,15 +8,15 @@ ms.service: cognitive-search
 ms.custom:
   - build-2024
 ms.topic: reference
-ms.date: 05/28/2024
+ms.date: 06/14/2024
 ---
 
 # Azure AI Vision vectorizer
 
 > [!IMPORTANT] 
 > This feature is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). The [2024-05-01-Preview REST API](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-05-01-Preview&preserve-view=true) supports this feature.
 
-The **Azure AI Vision** vectorizer connects to an Azure AI Vision resource to generate embeddings at query time using [the Multimodal embeddings API](../ai-services/computer-vision/concept-image-retrieval.md). Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed. 
+The **Azure AI Vision** vectorizer connects to an Azure AI Vision resource to generate embeddings at query time using [the Multimodal embeddings API](../ai-services/computer-vision/concept-image-retrieval.md). Refer to the [region availability for multimodal embeddings](../ai-services/computer-vision/overview-image-analysis.md?tabs=4-0#region-availability) to determine the regions where this model is accessible. Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed. 
 
 > [!NOTE]
 > This vectorizer is bound to Azure AI services. Execution of the vectorizer is charged at the existing [Azure AI services pay-as-you go price](https://azure.microsoft.com/pricing/details/cognitive-services/).
@@ -29,7 +29,7 @@ Parameters are case-sensitive.
 |---------------------|-------------|
 | `resourceUri` | The URI of the AI Services resource.  |
 | `apiKey`   |  The API key of the AI Services resource. |
-| `modelVersion` | (Required) The model version to be passed to the Azure AI Vision API for generating embeddings. It's important that all embeddings stored in a given index field are generated using the same `modelVersion`. |
+| `modelVersion` | (Required) The model version to be passed to the Azure AI Vision API for generating embeddings. It's important that all embeddings stored in a given index field are generated using the same `modelVersion`. For information about version support for this model refer to [multimodal embeddings](../ai-services/computer-vision/concept-image-retrieval.md#what-are-vector-embeddings). |
 | `authIdentity`   | A user-managed identity used by the search service for connecting to AI Services. You can use either a [system or user managed identity](search-howto-managed-identities-data-sources.md). To use a system manged identity, leave `apiKey` and `authIdentity` blank. The system-managed identity is used automatically. A managed identity must have Cognitive Services User permissions to use this vectorizer. |
 
 ## Supported vector query types
@@ -61,4 +61,4 @@ A field configured with the Azure AI Vision vectorizer should have a dimensions
 
 + [Integrated vectorization](vector-search-integrated-vectorization.md)
 + [How to configure a vectorizer in a search index](vector-search-how-to-configure-vectorizer.md)
-+ [Azure AI Vision Vectorize skill](cognitive-search-skill-vision-vectorize.md)
++ [Azure AI Vision Vectorize skill](cognitive-search-skill-vision-vectorize.md)