@@ -3,8 +3,8 @@ title: Document Extraction cognitive skill
 titleSuffix: Azure AI Search
 description: Extracts content from a file within the enrichment pipeline.
 
-author: careyjmac
-ms.author: chalton
+author: gmndrg
+ms.author: gimondra
 
 ms.service: azure-ai-search
 ms.custom:

@@ -3,8 +3,8 @@ title: Image Analysis cognitive skill
 titleSuffix: Azure AI Search
 description: Extract semantic text through image analysis using the Image Analysis cognitive skill in an AI enrichment pipeline in Azure AI Search.
 
-author: careyjmac
-ms.author: chalton
+author: gmndrg
+ms.author: gimondra
 
 ms.service: azure-ai-search
 ms.custom:

@@ -3,8 +3,8 @@ title: OCR skill
 titleSuffix: Azure AI Search
 description: Extract text from image files using optical character recognition (OCR) in an enrichment pipeline in Azure AI Search.
 
-author: careyjmac
-ms.author: chalton
+author: gmndrg
+ms.author: gimondra
 
 ms.service: azure-ai-search
 ms.custom:

@@ -4,8 +4,8 @@ titleSuffix: Azure AI Search
 description: Extract and mask personal information from text in an enrichment pipeline in Azure AI Search.
 
 manager: nitinme
-author: careyjmac
-ms.author: chalton
+author: gmndrg
+ms.author: gimondra
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023

@@ -3,8 +3,8 @@ title: Sentiment cognitive skill (v3)
 titleSuffix: Azure AI Search
 description: Provides sentiment labels for text in an AI enrichment pipeline in Azure AI Search.
 
-author: careyjmac
-ms.author: chalton
+author: gmndrg
+ms.author: gimondra
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023

@@ -4,8 +4,8 @@ titleSuffix: Azure AI Search
 description: Evaluates text and, for each record, returns text translated to the specified target language in an  AI enrichment pipeline in Azure AI Search.
 
 manager: nitinme
-author: careyjmac
-ms.author: chalton
+author: gmndrg
+ms.author: gimondra
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023

@@ -2,8 +2,8 @@
 title: Text split skill
 titleSuffix: Azure AI Search
 description: Break text into chunks or pages of text based on length in an AI enrichment pipeline in Azure AI Search.
-author: careyjmac
-ms.author: chalton
+author: gmndrg
+ms.author: gimondra
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023

@@ -2,8 +2,8 @@
 title: Azure AI Vision multimodal embeddings skill
 titleSuffix: Azure AI Search
 description: Vectorize images or text using the Azure AI Vision multimodal embeddings API.
-author: careyjmac
-ms.author: chalton
+author: gmndrg
+ms.author: gimondra
 ms.service: azure-ai-search
 ms.custom:
   - build-2024

@@ -11,7 +11,7 @@ ms.custom:
   - references_regions
   - build-2024
 ms.topic: conceptual
-ms.date: 09/19/2024
+ms.date: 10/17/2024
 ---
 
 # Create an Azure AI Search service in the portal
@@ -93,37 +93,46 @@ If you use multiple Azure services, putting all of them in the same region minim
 
 Generally, choose a region near you, unless the following considerations apply:
 
-+ Your nearest region is capacity constrained. For example, West Europe is at capacity and unavailable for new instances. Other regions are [at capacity for specific tiers](search-sku-tier.md#region-availability-by-tier). One advantage to using the Azure portal for resource setup is that it provides only those regions and tiers that are available.
++ Your nearest region is [at capacity](search-sku-tier.md#region-availability-by-tier). One advantage to using the Azure portal for resource setup is that it provides only those regions and tiers that are available.
 
-+ You want to use integrated data chunking and vectorization or built-in skills for AI enrichment. Azure OpenAI and Azure AI services multiservice accounts must be in the same region as Azure AI Search for integration purposes. [Choose a region](search-region-support.md) that provides all necessary resources.
++ You want to use integrated data chunking and vectorization or built-in skills for AI enrichment. Integrated operations have region requirements.
 
-+ You want to use Azure Storage for indexer-based indexing or you need to store application data that isn't in an index. Debug session state, enrichment caches, and knowledge stores are Azure AI Search features that have a dependency on Azure Storage. The region you choose for Azure Storage has implications for network security. Specifically, if you're setting up a firewall, you should place the resources in separate regions. For more information, see [Outbound connections from Azure AI Search to Azure Storage](search-indexer-securing-resources.md).
++ You want to use Azure Storage for indexer-based indexing or you need to store application data that isn't in an index. Debug session state, enrichment caches, and knowledge stores are Azure AI Search features that have a dependency on Azure Storage. The region you choose for Azure Storage has implications for network security. Specifically, if you're setting up a firewall, you should place the resources in *separate regions*. For more information, see [Outbound connections from Azure AI Search to Azure Storage](search-indexer-securing-resources.md).
 
-Here's a checklist for choosing a region:
+### Checklist for choosing a region
 
 1. Is Azure AI Search available in a nearby region? Check the [supported regions list](search-region-support.md).
 
-1. Do you know which tier you want to use? Tiers are covered in the next step. Check [region availability by tier](search-sku-tier.md#region-availability-by-tier) to determine if you can create a search service at the desired tier in your region of choice.
+1. Do you have a specific tier in mind? Check [region availability by tier](search-sku-tier.md#region-availability-by-tier).
 
-1. Do you need [AI enrichment](cognitive-search-concept-intro.md) or [integrated data chunking and vectorization](vector-search-integrated-vectorization.md)? Verify that Azure OpenAI and Azure AI multiservice are [offered in the same region](search-region-support.md) as Azure AI Search. 
+1. Do you have business continuity and disaster recovery (BCDR) requirements? Create two or more search services in [regional pairs](/azure/availability-zones/cross-region-replication-azure#azure-paired-regions) within [availability zones](search-reliability.md#availability-zones). For example, if you're operating in North America, you might choose East US and West US, or North Central US and South Central US, for each search service.
 
-   Be aware that Azure AI Vision multimodal embeddings API, used for [integrated image vectorization](search-get-started-portal-image-search.md), must be accessed through an Azure AI multiservice account, but is available in a [smaller subset of regions](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
+1. Do you need [AI enrichment](cognitive-search-concept-intro.md), [integrated data chunking and vectorization](vector-search-integrated-vectorization.md), or [multimodal image search](search-get-started-portal-image-search.md)? Azure AI Search, Azure OpenAI, and Azure AI multiservice must coexist in the same region.
 
-1. Do you have business continuity and disaster recovery (BCDR) requirements? Such requirements dictate creating multiple search services in [regional pairs](/azure/availability-zones/cross-region-replication-azure#azure-paired-regions) in [availability zones](search-reliability.md#availability-zones). For example, if you're operating in North America, you might choose East US and West US, or North Central US and South Central US, for each search service.
+   + Start with [Azure OpenAI regions](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability) because it has the most variability. Azure OpenAI provides embedding models and chat models for RAG and integrated vectorization. 
+
+   + Check [Azure AI Search regions](search-region-support.md) for a match to your Azure OpenAI region. If you're using OCR, entity recognition, or other skills backed by Azure AI, the **AI Integration** column indicates whether Azure AI multiservice is in the same region as Azure AI Search.
+
+   + Check [multimodal embedding regions](/azure/ai-services/computer-vision/overview-image-analysis#region-availability) for multimodal APIs and image search. This API is accessed through an Azure AI multiservice account, but it's available in fewer regions than Azure AI multiservice in general.
+
+### Regions with the most overlap
+
+Currently, the following regions offer cross-region among all three services (Azure AI Search, Azure OpenAI, Azure AI Vision multimodal). This list isn't definitive, and there might be more choices beyond the regions listed here depending on the tier. Also, region status can change quickly, so be sure to confirm region choice before installing.
+
++ **Americas**: West US
++ **Europe**: France Central, North Europe, Sweden Central
 
 ## Choose a tier
 
 Azure AI Search is offered in [multiple pricing tiers](https://azure.microsoft.com/pricing/details/search/): Free, Basic, Standard, or Storage Optimized. Each tier has its own [capacity and limits](search-limits-quotas-capacity.md). There are also several features that are tier-dependent.
 
-Review the [tier descriptions](search-sku-tier.md) for computing characteristics and [feature availability](search-sku-tier.md#feature-availability-by-tier).
+Review the [tier descriptions](search-sku-tier.md) for computing characteristics, [feature availability](search-sku-tier.md#feature-availability-by-tier), and [region availability](search-sku-tier.md#region-availability-by-tier).
 
 Basic and Standard are the most common choices for production workloads, but many customers start with the Free service. Among the billable tiers, key differences are partition size and speed, and limits on the number of objects you can create.
 
 :::image type="content" source="media/search-create-service-portal/select-pricing-tier.png" lightbox="media/search-create-service-portal/select-pricing-tier.png" alt-text="Screenshot of Select a pricing tier page." border="true":::
 
-Search services created after April 3, 2024 have larger partitions and higher vector quotas.
-
-Currently, some regions are tier-constrained. For more information, see [region availability by tier](search-sku-tier.md#region-availability-by-tier).
+Search services created after April 3, 2024 have larger partitions and higher vector quotas at every billable tier.
 
 Remember, a pricing tier can't be changed once the service is created. If you need a higher or lower tier, you should re-create the service.
 

@@ -22,11 +22,9 @@ Sample data consists of image files in the [azure-search-sample-data](https://gi
 
 + An Azure subscription. [Create one for free](https://azure.microsoft.com/free/).
 
-+ An [Azure AI services multiservice account](/azure/ai-services/multi-service-resource) to use for image vectorization and optical character recognition (OCR). The account must be in a region that provides Azure AI Vision multimodal embeddings.
++ An [Azure AI services multiservice account](/azure/ai-services/multi-service-resource) to use for image vectorization and optical character recognition (OCR). Image vectorization requires Azure AI Vision multimodal embeddings. [Check the documentation](/azure/ai-services/computer-vision/overview-image-analysis#region-availability) for an updated list of regions.
 
-  Currently, those regions are: EastUS, WestUS, WestUS2, NorthEurope, WestEurope, FranceCentral, SwedenCentral, SwitzerlandNorth, SoutheastAsia, KoreaCentral, AustraliaEast, JapanEast. [Check the documentation](/azure/ai-services/computer-vision/overview-image-analysis#region-availability) for an updated list.
-
-+ Azure AI Search for indexing and queries. It can be on any tier, but it must be in the same region as Azure AI services.
++ Azure AI Search for indexing and queries. It can be on any tier, but it must be in the [same region as Azure AI multiservice](search-create-service-portal.md#regions-with-the-most-overlap).
 
   The service tier determines how many blobs you can index. We used the Free tier to create this walkthrough and limited the content to 10 JPG files.
 

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - build-2024
 ms.topic: quickstart
-ms.date: 10/16/2024
+ms.date: 10/17/2024
 ---
 
 # Quickstart: Vectorize text and images by using the Azure portal
@@ -38,7 +38,7 @@ Key points about the wizard:
 
   Azure Storage must be a standard performance (general-purpose v2) account. Access tiers can be hot, cool, and cold.
 
-+ An embedding model on an Azure AI platform. [Deployment instructions](#set-up-embedding-models) are in this article.
++ An embedding model on an Azure AI platform in the [same region as Azure AI Search](search-create-service-portal.md#regions-with-the-most-overlap). [Deployment instructions](#set-up-embedding-models) are in this article.
 
   | Provider | Supported models |
   |---|---|

@@ -52,15 +52,15 @@ Indexers load indexed data into a predefined index. How you define the schema an
 
 ## Create an index for one-to-many indexing
 
-Whether you create one index that combines parent-child fields, or multiple indexes for separating parent-child fields, the primary index used for searching is designed around data chunks. It must have the following fields:
+Whether you create one index for chunks that repeat parent values, or separate indexes for parent-child field placement, the primary index used for searching is designed around data chunks. It must have the following fields:
 
 - A document key field uniquely identifying each document. It must be defined as type `Edm.String` with the `keyword` analyzer.
 
 - A field associating each chunk with its parent. It must be of type `Edm.String`. It can't be the document key field, and must have `filterable` set to true. It's referred to as parent_id in the examples and as a [projected key value](#projected-key-value) in this article.
 
 - Other fields for content, such as text or vectorized chunk fields.
 
-An index must exist on the search service before you create the skillset or run the indexer
+An index must exist on the search service before you create the skillset or run the indexer.
 
 ### Single index schema inclusive of parent and child fields
 
@@ -140,20 +140,9 @@ This example is similar to the [RAG tutorial](tutorial-rag-build-solution-index-
 
 ## Add index projections to a skillset
 
-Index projections are defined inside a skillset definition and are primarily defined as an array of `selectors`, where each selector corresponds to a different target index on the search service. Each selector requires the following parameters as part of its definition:
+Index projections are defined inside a skillset definition and are primarily defined as an array of `selectors`, where each selector corresponds to a different target index on the search service. This section starts with syntax and examples for context, followed by [parameter reference](#parameter-reference). 
 
-| Parameter | Definition |
-|-----------|------------|
-| `targetIndexName` | The name of the index into which index data is projected. It's either the single chunked index with repeating parent fields, or it's the child index if you're using [separate indexes](#example-of-separate-parent-child-indexes) for parent-child content. |
-| `parentKeyFieldName` | The name of the field providing the key for the parent document.|
-| `sourceContext` | The enrichment annotation that defines the granularity at which to map data into individual search documents. For more information, see [Skill context and input annotation language](cognitive-search-skill-annotation-language.md). |
-| `mappings` | An array of mappings of enriched data to fields in the search index. Each mapping consists of: <br>`name`: The name of the field in the search index that the data should be indexed into. <br>`source`: The enrichment annotation path that the data should be pulled from. <br><br>Each `mapping` can also recursively define data with an optional `sourceContext` and `inputs` field, similar to the [knowledge store](knowledge-store-concept-intro.md) or [Shaper Skill](cognitive-search-skill-shaper.md). Depending on your application, these parameters allow you to shape data into fields of type `Edm.ComplexType` in the search index. Some LLMs don't accept a complex type in search results, so the LLM you're using determines whether a complex type mapping is helpful or not.|
-
-You must explicitly map every field in the child index, except for the ID fields such as document key and the parent ID. 
-
-This requirement is in contrast with other field mapping conventions in Azure AI Search. For some data source types, the indexer can implicitly map fields based on similar names, or known characteristics (for example, blob indexers use the unique metadata storage path as the default document key). However, for indexer projections, you must explicitly specify every field mapping on the "many" side of the relationship.
-
-<!-- Avoid creating a field mapping for the parent key field. Doing so disrupts change tracking and synchronized data refresh. -->
+Choose a tab for the various API syntax. There's currently no portal support for setting up projections, other than editing the skillset JSON definition. Refer to the REST example for JSON.
 
 #### [**REST**](#tab/rest-create-index-projection)
 
@@ -200,6 +189,8 @@ Here's an example payload for an index projections definition that you might use
 
 #### [**Python**](#tab/python-create-index-projection)
 
+We recommend the [latest stable package](https://pypi.org/project/azure-search-documents/) for index projections.
+
 ```python
 index_projections = SearchIndexerIndexProjection(  
     selectors=[  
@@ -226,8 +217,27 @@ For .NET developers, use the [IndexProjections Class](/dotnet/api/azure.search.d
 
 ---
 
-> [!TIP]
-> We recommend setting the `skipIndexingParentDocuments` parameter for the consolidated schema scenario. If you don't set parameters for skipping parent document indexing, you get extra search documents in your index that are null for chunks, but populated with parent fields only. For example, if five documents contribute 100 chunks to the index, then the number of documents in the index is 105. The five documents created or parent fields have nulls for child fields, making them substantially different from the bulk of the documents in the index.
+### Parameter reference
+
+| Index projection parameters | Definition |
+|----------------------------|------------|
+| `selectors` | Parameters for the main search corpus, usually the one designed around chunks. |
+| `projectionMode` | An optional parameter providing instructions to the indexer. The only valid value for this parameter is `skipIndexingParentDocuments`, and it's used when the chunk index is the primary search corpus and you need to specify whether parent fields are indexed as extra search documents within the chunked index. If you don't set `skipIndexingParentDocuments`, you get extra search documents in your index that are null for chunks, but populated with parent fields only. For example, if five documents contribute 100 chunks to the index, then the number of documents in the index is 105. The five documents created or parent fields have nulls for chunk (child) fields, making them substantially different from the bulk of the documents in the index. We recommend `projectionMode` set to `skipIndexingParentDocument`. |
+
+Selectors have the following parameters as part of their definition.
+
+| Selector parameters | Definition |
+|-----------|------------|
+| `targetIndexName` | The name of the index into which index data is projected. It's either the single chunked index with repeating parent fields, or it's the child index if you're using [separate indexes](#example-of-separate-parent-child-indexes) for parent-child content. |
+| `parentKeyFieldName` | The name of the field providing the key for the parent document.|
+| `sourceContext` | The enrichment annotation that defines the granularity at which to map data into individual search documents. For more information, see [Skill context and input annotation language](cognitive-search-skill-annotation-language.md). |
+| `mappings` | An array of mappings of enriched data to fields in the search index. Each mapping consists of: <br>`name`: The name of the field in the search index that the data should be indexed into. <br>`source`: The enrichment annotation path that the data should be pulled from. <br><br>Each `mapping` can also recursively define data with an optional `sourceContext` and `inputs` field, similar to the [knowledge store](knowledge-store-concept-intro.md) or [Shaper Skill](cognitive-search-skill-shaper.md). Depending on your application, these parameters allow you to shape data into fields of type `Edm.ComplexType` in the search index. Some LLMs don't accept a complex type in search results, so the LLM you're using determines whether a complex type mapping is helpful or not.|
+
+The `mappings` parameter is important. You must explicitly map every field in the child index, except for the ID fields such as document key and the parent ID. 
+
+This requirement is in contrast with other field mapping conventions in Azure AI Search. For some data source types, the indexer can implicitly map fields based on similar names, or known characteristics (for example, blob indexers use the unique metadata storage path as the default document key). However, for indexer projections, you must explicitly specify every field mapping on the "many" side of the relationship.
+
+<!-- Avoid creating a field mapping for the parent key field. Doing so disrupts change tracking and synchronized data refresh. -->
 
 ## Handling parent documents
 

@@ -4,8 +4,8 @@ titleSuffix: Azure AI Search
 description: Learn how to index and extract text from encrypted documents in Azure Blob Storage with Azure AI Search.
 
 manager: nitinme
-author: careyjmac
-ms.author: chalton
+author: gmndrg
+ms.author: gimondra
 ms.devlang: rest-api
 ms.custom:
   - ignite-2023

@@ -8,7 +8,7 @@ author: mrcarter8
 ms.author: mcarter
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 09/17/2024
+ms.date: 10/16/2024
 ---
 
 # Make outbound connections through a shared private link
@@ -95,7 +95,7 @@ You can create a shared private link for the following resources.
 | Microsoft.DBforMySQL/servers (preview) | `mysqlServer`|
 | Microsoft.Web/sites <sup>4</sup> | `sites` |
 | Microsoft.Sql/managedInstances (preview) <sup>5</sup>| `managedInstance` |
-| Microsoft.CognitiveServices/accounts <sup>6</sup>| `openai_account` |
+| Microsoft.CognitiveServices/accounts <sup>6</sup> <sup>7</sup>| `openai_account` |
 
 <sup>1</sup> If Azure Storage and Azure AI Search are in the same region, the connection to storage is made over the Microsoft backbone network, which means a shared private link is redundant for this configuration. However, if you already set up a private endpoint for Azure Storage, you should also set up a shared private link or the connection is refused on the storage side. Also, if you're using multiple storage formats for various scenarios in search, make sure to create a separate shared private link for each subresource.
 
@@ -109,6 +109,9 @@ You can create a shared private link for the following resources.
 
 <sup>6</sup> The `Microsoft.CognitiveServices/accounts` resource type is used for vectorizer and indexer connections to Azure OpenAI when implementing [integrated Vectorization](vector-search-integrated-vectorization.md). There's currently no support for shared private link to embedding models in the Azure AI Studio model catalog or to the Azure AI Vision multimodal API.
 
+<sup>7</sup> Shared Private Link for Azure OpenAI is only supported in public cloud. Other cloud offerings such as [Microsoft Azure Government](https://azure.microsoft.com/explore/global-infrastructure/government/) don't have support for Shared Private Links for `openai_account` Group ID.
+
+
 ## 1 - Create a shared private link
 
 Use the Azure portal, Management REST API, the Azure CLI, or Azure PowerShell to create a shared private link.

@@ -9,7 +9,7 @@ ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: conceptual
 ms.custom: references_regions
-ms.date: 09/29/2024
+ms.date: 10/17/2024
 
 ---
 
@@ -44,7 +44,7 @@ You can create an Azure AI Search resource in any of the following Azure public
 | East US 2 ​ | ✅ | ✅ | ✅ |
 | ​Central US​​ <sup>2</sup> | ✅ | ✅ | ✅ |
 | North Central US​ ​ | ✅ | ✅ | |
-| South Central US​ <sup>2</sup>​ | ✅ | ✅ | ✅ |
+| South Central US​ <sup>1</sup>​ | ✅ | ✅ | ✅ |
 | West US​ ​ | ✅ | ✅ | |
 | West US 2​ ​ | ✅ | ✅ | ✅ |
 | West US 3​ <sup>1</sup>​ | ✅ | ✅ |✅ |
@@ -102,7 +102,7 @@ You can create an Azure AI Search resource in any of the following Azure public
 | Australia Southeast​​​ |  | ✅ |  |
 | East Asia​ | ✅ | ✅ | ✅ |
 | Southeast Asia​ ​ ​ | ✅ | ✅ | ✅ |
-| Central India| ✅ | ✅ | ✅ |
+| Central India <sup>1</sup> | ✅ | ✅ | ✅ |
 | Jio India West​ ​ | ✅ | ✅ |  |
 | South India <sup>2</sup> |  | | ✅ |
 | Japan East <sup>1</sup> | ✅ | ✅ | ✅ |

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 09/19/2024
+ms.date: 10/17/2024
 
 ---
 
@@ -60,6 +60,7 @@ Currently, several regions are at capacity for specific tiers and can't be used
 | Region | Disabled tier (SKU) due to over-capacity |
 |--------|------------------------------------------|
 | Central US | S2, S3, S3HD, L1, L2 |
+| Central India | S2, S3, S3HD, L1, L2|
 | East US| All tiers|
 | Japan East | S2, S3, S3HD, L1, L2 |
 | Qatar Central | All tiers|

@@ -54,7 +54,7 @@ The [Azure portal](https://portal.azure.com/) is the easiest approach for first-
      We recommend Basic for larger data files and more indexes, or Free if your files are less than 50 MB. Basic has more features and storage, but it's billable for the lifetime of the service and it might consume about one third of your available credits if you retain it for the entire trial period.
 
 > [!TIP]
-> Azure AI Search and Azure OpenAI must be in the same region. Currently, these regions provide the most overlap and capacity: **East US**, **East US2**, and **South Central** in the Americas; **France Central** or **Switzerland North** in Europe; **Australia East** in Asia Pacific.
+> Azure AI Search and Azure OpenAI must be in the [same region](search-create-service-portal.md#regions-with-the-most-overlap).
 
 ## Step three: Have a plan for next steps
 

@@ -48,12 +48,10 @@ If you don't have an Azure subscription, create a [free account](https://azure.m
 
   - [Azure AI Studio](/azure/ai-studio/reference/region-support) regions. 
 
-  Azure AI Search is currently facing limited availability in some regions, such as West Europe and West US 2/3. To confirm region status, check the [Azure AI Search region list](search-region-support.md).
+  Azure AI Search is currently facing limited availability in some regions. To confirm region status, check the [Azure AI Search region list](search-region-support.md).
 
 > [!TIP]
-> Currently, the following regions provide the most overlap among the model providers and have the most capacity: **East US2** and **South Central** in the Americas; **France Central** or **Switzerland North** in Europe; **Australia East** in Asia Pacific.
->
-> For Azure AI Vision and AI Search interoperability, choose one of these regions: **France Central**, **Korea Central**, **North Europe**, **South East Asia**, or **West US**. 
+> Check [this article](search-create-service-portal.md#regions-with-the-most-overlap) for a list of overlapping regions.
 
 ## Review models supporting built-in vectorization
 

@@ -2,8 +2,8 @@
 title: Integrated vectorization with models from Azure AI Studio
 titleSuffix: Azure AI Search
 description: Learn  how to vectorize content during indexing on Azure AI Search with an AI Studio model.
-author: careyjmac
-ms.author: chalton
+author: gmndrg
+ms.author: gimondra
 ms.service: azure-ai-search
 ms.custom:
   - build-2024

@@ -2,8 +2,8 @@
 title: Azure AI Vision vectorizer
 titleSuffix: Azure AI Search
 description: Connects to an Azure AI Vision resource to generate embeddings at query time.
-author: careyjmac
-ms.author: chalton
+author: gmndrg
+ms.author: gimondra
 ms.service: azure-ai-search
 ms.custom:
   - build-2024

@@ -2,8 +2,8 @@
 title: Azure AI Studio model catalog vectorizer
 titleSuffix: Azure AI Search
 description: Connects to a deployed model from the Azure AI Studio model catalog at query time.
-author: careyjmac
-ms.author: chalton
+author: gmndrg
+ms.author: gimondra
 ms.service: azure-ai-search
 ms.custom:
   - build-2024

@@ -2,8 +2,8 @@
 title: Custom Web API vectorizer
 titleSuffix: Azure AI Search
 description: Use the Custom Web API vectorizer to integrate your custom code for generating embeddings at query time.
-author: careyjmac
-ms.author: chalton
+author: gmndrg
+ms.author: gimondra
 ms.service: azure-ai-search
 ms.custom:
   - build-2024