UUF updates

Author: HeidiSteen

articles/search/search-api-migration.md

@@ -11,7 +11,7 @@ ms.custom:
   - ignite-2023
   - build-2024
 ms.topic: conceptual
-ms.date: 06/24/2024
+ms.date: 07/19/2024
 ---
 
 # Upgrade to the latest REST API in Azure AI Search
@@ -41,6 +41,12 @@ Azure AI Search breaks backward compatibility as a last resort. Upgrade is neces
 
 + Your code persists API requests and tries to resend them to the new API version. For example, this might happen if your application persists continuation tokens returned from the Search API (for more information, look for `@search.nextPageParameters` in the [Search API Reference](/rest/api/searchservice/Search-Documents)).
 
+## How to upgrade
+
+In your application code that makes direct calls to the REST APIs, modify the `api-version` parameter on the header of the request. For more information about formulating REST calls, see [Quickstart: using REST](search-get-started-rest.md).
+
+If you're using an Azure SDK, those packages target specific versions of the REST API. Package updates might coincide with a REST API update, but each SDK is on it's own release schedule that ships independently of Azure AI Search REST API versions. Check the change log of the SDK package to determine whether a package release ships new API version features.
+
 ## Breaking change for client code that reads connection information
 
 Effective March 29, 2024 and applicable to all [supported REST APIs](/rest/api/searchservice/search-service-api-versions): 

articles/search/search-get-started-portal-import-vectors.md

@@ -9,20 +9,20 @@ ms.service: cognitive-search
 ms.custom:
   - build-2024
 ms.topic: quickstart
-ms.date: 06/17/2024
+ms.date: 07/19/2024
 ---
 
 # Quickstart: Vectorize text and images by using the Azure portal
 
 > [!IMPORTANT]
 > The **Import and vectorize data** wizard is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). By default, it targets the [2024-05-01-Preview REST API](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true).
 
-This quickstart helps you get started with [integrated vectorization (preview)](vector-search-integrated-vectorization.md) by using the **Import and vectorize data** wizard in the Azure portal. This wizard calls a user-specified embedding model to vectorize content during indexing and for queries.
+This quickstart helps you get started with [integrated vectorization (preview)](vector-search-integrated-vectorization.md) by using the **Import and vectorize data** wizard in the Azure portal. This wizard chunks your content and calls a user-specified embedding model to vectorize content during indexing and for queries.
 
 ## Preview limitations
 
 + Source data is either Azure Blob Storage or OneLake files and shortcuts, using the default parsing mode (one search document per blob or file).
-+ The index schema is nonconfigurable. Source fields include `content` (chunked and vectorized), `metadata_storage_name` for the title, and `metadata_storage_path` for the document key. This key is represented as `parent_id` in the index.
++ The index schema is nonconfigurable. 
 + Chunking is nonconfigurable. The effective settings are:
 
   ```json
@@ -31,25 +31,23 @@ This quickstart helps you get started with [integrated vectorization (preview)](
   pageOverlapLength: 500
   ```
 
-For fewer limitations or more data source options, try a code-base approach. For more information, see the [integrated vectorization sample](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/integrated-vectorization/azure-search-integrated-vectorization-sample.ipynb).
+For fewer limitations or more data source options, try a code-based approach. For more information, see the [integrated vectorization sample](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/integrated-vectorization/azure-search-integrated-vectorization-sample.ipynb).
 
 ## Prerequisites
 
 + An Azure subscription. [Create one for free](https://azure.microsoft.com/free/).
 
 + For data, either [Azure Blob Storage](/azure/storage/common/storage-account-overview) or a [OneLake lakehouse](search-how-to-index-onelake-files.md).
 
-  Azure Storage must be a standard performance (general-purpose v2) account. Access tiers can be hot, cool, and cold.
-  
-  Don't use Azure Data Lake Storage Gen2 (a storage account with a hierarchical namespace). This version of the wizard doesn't support Data Lake Storage Gen2.
+  Azure Storage must be a standard performance (general-purpose v2) account. Access tiers can be hot, cool, and cold. Don't use Azure Data Lake Storage Gen2 (a storage account with a hierarchical namespace). This version of the wizard doesn't support Data Lake Storage Gen2.
 
-+ For vectorization, an [Azure AI services multiservice account](/azure/ai-services/multi-service-resource) or [Azure OpenAI Service](https://aka.ms/oai/access) endpoint with deployments.
++ For vectorization, have an [Azure AI services multiservice account](/azure/ai-services/multi-service-resource) or [Azure OpenAI Service](https://aka.ms/oai/access) endpoint with deployments.
 
   For [multimodal with Azure AI Vision](/azure/ai-services/computer-vision/how-to/image-retrieval), create an Azure AI service in SwedenCentral, EastUS, NorthEurope, WestEurope, WestUS, SoutheastAsia, KoreaCentral, FranceCentral, AustraliaEast, WestUS2, SwitzerlandNorth, or JapanEast. [Check the documentation](/azure/ai-services/computer-vision/how-to/image-retrieval?tabs=csharp) for an updated list.
 
   You can also use an [Azure AI Studio model catalog](/azure/ai-studio/what-is-ai-studio) (and hub and project) with model deployments.
 
-+ For indexing and queries, Azure AI Search. It must be in the same region as your Azure AI service. We recommend the Basic tier or higher.
++ For indexing and queries, have an [Azure AI Search service](search-create-service-portal.md). It must be in the same region as your Azure AI service. We recommend the Basic tier or higher.
 
 + Role assignments or API keys for connections to embedding models and data sources. This article provides instructions for role-based access control (RBAC).