@@ -395,6 +395,26 @@
             "source_path_from_root": "/articles/search/search-data-sources-terms-of-use.md",
             "redirect_url": "https://partner.microsoft.com/partnership/find-a-partner",
             "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/search/tutorial-multimodal-indexing-with-embedding-and-doc-extraction.md",
+            "redirect_url": "/azure/search/tutorial-document-extraction-multimodal-embeddings",
+            "redirect_document_id": true
+        },
+        {
+            "source_path_from_root": "/articles/search/tutorial-multimodal-indexing-with-image-verbalization-and-doc-extraction.md",
+            "redirect_url": "/azure/search/tutorial-document-extraction-image-verbalization",
+            "redirect_document_id": true
+        },
+        {
+            "source_path_from_root": "/articles/search/tutorial-multimodal-index-embeddings-skill.md",
+            "redirect_url": "/azure/search/tutorial-document-layout-multimodal-embeddings",
+            "redirect_document_id": true
+        },
+        {
+            "source_path_from_root": "/articles/search/tutorial-multimodal-index-image-verbalization-skill.md",
+            "redirect_url": "/azure/search/tutorial-document-layout-image-verbalization",
+            "redirect_document_id": true
         }
     ]
   }
\ No newline at end of file

@@ -7,7 +7,7 @@ ms.author: heidist
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
-ms.topic: how-to
+ms.topic: reference
 ms.date: 05/27/2025
 ---
 

@@ -41,7 +41,7 @@ Parameters are case-sensitive.
 
 | Inputs | Description |
 |---------------------|-------------|
-| `resourceUri` | The URI of the model provider, in this case, an Azure OpenAI resource. This parameter only supports URLs with domain `openai.azure.com`, such as `https://<resourcename>.openai.azure.com`. If the Azure OpenAI endpoint has a URL with domain `cognitiveservices.azure.com`, like `https://<resourcename>.cognitiveservices.azure.com`, a [custom subdomain](/azure/ai-services/openai/how-to/use-your-data-securely#enabled-custom-subdomain) with `openai.azure.com` must be created first for the Azure OpenAI resource and use `https://<resourcename>.openai.azure.com` instead.  |
+| `resourceUri` | The URI of the model provider, in this case, an Azure OpenAI resource. This parameter only supports URLs with domain `openai.azure.com`, such as `https://<resourcename>.openai.azure.com`. If the Azure OpenAI endpoint has a URL with domain `cognitiveservices.azure.com`, like `https://<resourcename>.cognitiveservices.azure.com`, a [custom subdomain](/azure/ai-services/openai/how-to/use-your-data-securely#enabled-custom-subdomain) with `openai.azure.com` must be created first for the Azure OpenAI resource and use `https://<resourcename>.openai.azure.com` instead. This field is required if your Azure OpenAI resource is deployed behind a Private Endpoint or uses Virtual Network (VNet) integration.  |
 | `apiKey`   |  The secret key used to access the model. If you provide a key, leave `authIdentity` empty. If you set both the `apiKey` and `authIdentity`, the `apiKey` is used on the connection. |
 | `deploymentId`   | The name of the deployed Azure OpenAI embedding model. The model should be an embedding model, such as text-embedding-ada-002. See the [List of Azure OpenAI models](/azure/ai-services/openai/concepts/models) for supported models.|
 | `authIdentity`   | A user-managed identity used by the search service for connecting to Azure OpenAI. You can use either a [system or user managed identity](search-howto-managed-identities-data-sources.md). To use a system managed identity, leave `apiKey` and `authIdentity` blank. The system-managed identity is used automatically. A managed identity must have [Cognitive Services OpenAI User](/azure/ai-services/openai/how-to/role-based-access-control#azure-openai-roles) permissions to send text to Azure OpenAI. |

@@ -19,9 +19,9 @@ The **Document Extraction** skill extracts content from a file within the enrich
 
 For [vector](vector-search-overview.md) and [multimodal search](multimodal-search-overview.md), Document Extraction combined with the [Text Split skill](cognitive-search-skill-textsplit.md) is more affordable than other [data chunking approaches](vector-search-how-to-chunk-documents.md). The following tutorials demonstrate skill usage for different scenarios:
 
-+ [Tutorial: Index mixed content using multimodal embeddings and the Document Extraction skill](tutorial-multimodal-indexing-with-embedding-and-doc-extraction.md)
++ [Tutorial: Index mixed content using multimodal embeddings and the Document Extraction skill](tutorial-document-extraction-multimodal-embeddings.md)
 
-+ [Tutorial: Index mixed content using image verbalizations and the Document Extraction skill](tutorial-multimodal-indexing-with-image-verbalization-and-doc-extraction.md)
++ [Tutorial: Index mixed content using image verbalizations and the Document Extraction skill](tutorial-document-extraction-image-verbalization.md)
 
 > [!NOTE]
 > This skill isn't bound to Azure AI services and has no Azure AI services key requirement.

@@ -24,9 +24,9 @@ This article is the reference documentation for the Document Layout skill. For u
 
 It's common to use this skill on content such as PDFs that have structure and images. The following tutorials demonstrate several scenarios: 
 
-+ [Tutorial: Index mixed content using image verbalizations and the Document Layout skill](tutorial-multimodal-index-image-verbalization-skill.md)
++ [Tutorial: Index mixed content using image verbalizations and the Document Layout skill](tutorial-document-layout-image-verbalization.md)
 
-+ [Tutorial: Index mixed content using multimodal embeddings and the Document Layout skill](tutorial-multimodal-index-embeddings-skill.md)
++ [Tutorial: Index mixed content using multimodal embeddings and the Document Layout skill](tutorial-document-layout-multimodal-embeddings.md)
 
 > [!NOTE]
 > This skill uses the [Document Intelligence layout model](/azure/ai-services/document-intelligence/concept-layout) provided in [Azure AI Document Intelligence](/azure/ai-services/document-intelligence/overview).

@@ -19,9 +19,9 @@ The **GenAI (Generative AI) Prompt** skill executes a *chat completion* request
 
 Use this capability to create new information that can be indexed and stored as searchable content. Examples include verbalize images, summarize larger passages, simplify complex content, or any other task that an LLM can perform. The skill supports text, image, and multimodal content such as a PDF that contains text and images. It's common to use this skill combined with a data chunking skill. The following tutorials demonstrate the image verbalization scenarios with two different data chunking techniques: 
 
-- [Tutorial: Index mixed content using image verbalizations and the Document Layout skill](tutorial-multimodal-index-image-verbalization-skill.md)
+- [Tutorial: Index mixed content using image verbalizations and the Document Layout skill](tutorial-document-layout-image-verbalization.md)
 
-- [Tutorial: Index mixed content using image verbalizations and the Document Extraction skill](tutorial-multimodal-indexing-with-image-verbalization-and-doc-extraction.md)
+- [Tutorial: Index mixed content using image verbalizations and the Document Extraction skill](tutorial-document-extraction-image-verbalization.md)
 
 The GenAI Prompt skill is available in the [2025-05-01-preview REST API](/rest/api/searchservice/skillsets/create?view=rest-searchservice-2025-05-01-preview&preserve-view=true) only. 
 

@@ -4,7 +4,7 @@ author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: include
-ms.date: 05/12/2025
+ms.date: 05/29/2025
 ---
 
 [!INCLUDE [Feature preview](../previews/preview-generic.md)]
@@ -27,7 +27,7 @@ This quickstart is based on the [Quickstart-Agentic-Retrieval](https://github.co
 
 ## Deploy models
 
-To run agentic retrieval, you must deploy three models to your Azure OpenAI resource:
+To run agentic retrieval, you must deploy the following models to your Azure OpenAI resource:
 
 + An LLM for query planning.
 
@@ -100,7 +100,7 @@ To obtain your service endpoints:
 
 You configured role-based access to interact with Azure AI Search and Azure OpenAI. From the command line, use the Azure CLI to sign in to the same subscription and tenant for both services. For more information, see [Quickstart: Connect without keys](../../search-get-started-rbac.md).
 
-```Azure CLI
+```azurecli
 az account show
 
 az account set --subscription <PUT YOUR SUBSCRIPTION ID HERE>
@@ -505,3 +505,25 @@ The output should be similar to the following example.
 ```
 To find lava at night, you can look for the following signs:  1. **Active Volcanoes**: Research volcanoes that are currently active. Notable examples include Mount Etna in Italy and Kilauea in Hawaii. Both have had significant eruptions that can be observed at night due to the glow of lava. 2. **Satellite Imagery**: Use satellite imagery, especially those from sources like VIIRS (Visible Infrared Imaging Radiometer Suite) on the Suomi NPP satellite, which captures nighttime images of active lava flows. During eruptions, lava glows brightly in thermal infrared images, making it detectable from space.  3. **Safe Viewing Locations**: If you’re near an active volcano, find designated viewing areas for safety. Many national parks with volcanoes offer nighttime lava viewing experiences.  4. **Moonlight**: The presence of moonlight can enhance visibility, allowing you to spot lava flows more easily against the backdrop of the dark landscape.  5. **Monitoring Reports**: Follow updates from geological services or local authorities that monitor volcanic activity, which often provide real-time information about eruptions and visible lava flows at night.  6. **Photography**: If you're an enthusiast, consider using long-exposure photography techniques to capture the glow of lava flows at night.  For more information on observing volcanic activity, satellite imagery can provide vital data for detecting lava flows and volcanic eruptions.
 ```
+
+## Clean up resources
+
+When working in your own subscription, it's a good idea to finish a project by determining whether you still need the resources you created. Resources that are left running can cost you money. You can delete resources individually, or you can delete the resource group to delete the entire set of resources.
+
+In the Azure portal, you can find and manage resources by selecting **All resources** or **Resource groups** from the left pane. You can also run the following code to delete the objects you created in this quickstart.
+
+### Delete the knowledge agent
+
+```Python
+index_client = SearchIndexClient(endpoint=endpoint, credential=credential)
+index_client.delete_agent(agent_name)
+print(f"Knowledge agent '{agent_name}' deleted successfully")
+```
+
+### Delete the search index
+
+```Python
+index_client = SearchIndexClient(endpoint=endpoint, credential=credential)
+index_client.delete_index(index_name)
+print(f"Index '{index_name}' deleted successfully")
+```

@@ -4,7 +4,7 @@ author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: include
-ms.date: 05/12/2025
+ms.date: 05/29/2025
 ---
 
 [!INCLUDE [Feature preview](../previews/preview-generic.md)]
@@ -28,7 +28,7 @@ Although you can provide your own data, this quickstart uses [sample JSON docume
 
 ## Deploy models
 
-To run agentic retrieval, you must deploy three models to your Azure OpenAI resource:
+To run agentic retrieval, you must deploy the following models to your Azure OpenAI resource:
 
 + An LLM for query planning.
 
@@ -69,7 +69,7 @@ To configure the recommended role-based access:
 
 1. On your Azure AI Search service, [assign the following roles](../../search-security-rbac.md#how-to-assign-roles-in-the-azure-portal) to yourself.
 
-    + **Owner/Contributor** or **Search Service Contributor**
+    + **Search Service Contributor**
 
     + **Search Index Data Contributor**
 
@@ -105,7 +105,7 @@ To connect from your local system:
 
 1. Run the following commands in sequence.
 
-    ```Azure CLI
+    ```azurecli
     az account show
 
     az account set --subscription <PUT YOUR SUBSCRIPTION ID HERE>
@@ -115,7 +115,7 @@ To connect from your local system:
 
 1. To obtain your Microsoft Entra token, run the following command. You specify this value in the next section.
 
-   ```Azure CLI
+   ```azurecli
    az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv
    ```
 
@@ -426,3 +426,27 @@ The output should be similar to the following JSON, where:
   ]
 }
 ```
+
+## Clean up resources
+
+When working in your own subscription, it's a good idea to finish a project by determining whether you still need the resources you created. Resources that are left running can cost you money. You can delete resources individually, or you can delete the resource group to delete the entire set of resources.
+
+In the Azure portal, you can find and manage resources by selecting **All resources** or **Resource groups** from the left pane. You can also run the following code to delete the objects you created in this quickstart.
+
+### Delete the knowledge agent
+
+```HTTP
+### Delete the agent
+DELETE {{baseUrl}}/agents/{{agent-name}}?api-version={{api-version}}
+    Content-Type: application/json
+    Authorization: Bearer {{token}}
+```
+
+### Delete the search index
+
+```HTTP
+### Delete the index
+DELETE {{baseUrl}}/indexes/{{index-name}}?api-version={{api-version}}
+    Content-Type: application/json
+    Authorization: Bearer {{token}}
+```

@@ -1,17 +1,17 @@
 ---
-title: Multimodal search concepts and guidance
+title: Multimodal Search Concepts and Guidance
 titleSuffix: Azure AI Search
 description: Learn what multimodal search is, how Azure AI Search supports it for text and image content, and where to find detailed concepts, tutorials, and samples.
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 05/28/2025
+ms.date: 05/29/2025
 author: gmndrg
 ms.author: gimondra
 ---
 
 # Multimodal search in Azure AI Search
 
-Multimodal search refers to the ability to ingest, understand, and retrieve content across multiple data types, including text, images, video, and audio. In Azure AI Search, multimodal search natively supports the ingestion of documents containing text and images and the retrieval of their content, enabling you to perform searches that combine both modalities.
+Multimodal search refers to the ability to ingest, understand, and retrieve information across multiple content types, including text, images, video, and audio. In Azure AI Search, multimodal search natively supports the ingestion of documents containing text and images and the retrieval of their content, enabling you to perform searches that combine both modalities.
 
 Building a robust multimodal pipeline typically involves:
 
@@ -115,8 +115,8 @@ To help you get started with multimodal search in Azure AI Search, here's a coll
 | Content | Description |
 |--|--|
 | [Quickstart: Multimodal search in the Azure portal](search-get-started-portal-image-search.md) | Create and test a multimodal index in the Azure portal using the wizard and Search Explorer. |
-| [Tutorial: Image verbalization and Document Extraction skill](tutorial-multimodal-indexing-with-image-verbalization-and-doc-extraction.md) | Extract text and images, verbalize diagrams, and embed the resulting descriptions and text into a searchable index. |
-| [Tutorial: Multimodal embeddings and Document Extraction skill](tutorial-multimodal-indexing-with-embedding-and-doc-extraction.md) | Use a vision-text model to embed both text and images directly, enabling visual-similarity search over scanned PDFs. |
-| [Tutorial: Image verbalization and Document Layout skill](tutorial-multimodal-index-image-verbalization-skill.md) | Apply layout-aware chunking and diagram verbalization, capture location metadata, and store cropped images for precise citations and page highlights. |
-| [Tutorial: Multimodal embeddings and Document Layout skill](tutorial-multimodal-index-embeddings-skill.md) | Combine layout-aware chunking with unified embeddings for hybrid semantic and keyword search that returns exact hit locations. |
+| [Tutorial: Image verbalization and Document Extraction skill](tutorial-document-extraction-image-verbalization.md) | Extract text and images, verbalize diagrams, and embed the resulting descriptions and text into a searchable index. |
+| [Tutorial: Multimodal embeddings and Document Extraction skill](tutorial-document-extraction-multimodal-embeddings.md) | Use a vision-text model to embed both text and images directly, enabling visual-similarity search over scanned PDFs. |
+| [Tutorial: Image verbalization and Document Layout skill](tutorial-document-layout-image-verbalization.md) | Apply layout-aware chunking and diagram verbalization, capture location metadata, and store cropped images for precise citations and page highlights. |
+| [Tutorial: Multimodal embeddings and Document Layout skill](tutorial-document-layout-multimodal-embeddings.md) | Combine layout-aware chunking with unified embeddings for hybrid semantic and keyword search that returns exact hit locations. |
 | [Sample app: Multimodal RAG GitHub repository](https://aka.ms/azs-multimodal-sample-app-repo) | An end-to-end, code-ready RAG application with multimodal capabilities that surfaces both text snippets and image annotations. Ideal for jump-starting enterprise copilots. |

@@ -9,7 +9,7 @@ ms.author: beloh
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
-ms.topic: concept-article
+ms.topic: reference
 ms.date: 12/11/2024
 ---
 

@@ -6,7 +6,7 @@ description: OData language overview for filters, select, and order-by for Azure
 author: bevloh
 ms.author: beloh
 ms.service: azure-ai-search
-ms.topic: conceptual
+ms.topic: reference
 ms.date: 12/10/2024
 ---
 

@@ -9,7 +9,7 @@ ms.author: beloh
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
-ms.topic: conceptual
+ms.topic: reference
 ms.date: 12/10/2024
 ---
 

@@ -28,7 +28,7 @@ Productivity tools are built by engineers at Microsoft, but aren't part of the A
 
 | Accelerator | Description | Source code |
 |-----------|------------ |-------------|
-| [Build your own copilot Solution Accelerator](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator) | Code and docs to build a copilot for specific use cases.| [Client advisor](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator/blob/main/ClientAdvisor/README.md) <br>[Generic document generator](https://github.com/microsoft/Generic-Build-your-own-copilot-Solution-Accelerator) <br>[Research assistant](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator/blob/main/ResearchAssistant/README.md) |
+| [Build your own copilot Solution Accelerator](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator) | Code and docs to build a copilot for specific use cases.| [Client advisor](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator/tree/main) <br>[Generic document generator](https://github.com/microsoft/Generic-Build-your-own-copilot-Solution-Accelerator) <br>[Research assistant](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator/tree/main) |
 | [Chat with your data solution accelerator](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator/blob/main/README.md) |  Code and docs to create interactive search solution in production environments.  | [https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator) |
 | [Document knowledge mining solution accelerator](https://github.com/microsoft/Document-Knowledge-Mining-Solution-Accelerator/blob/main/README.md) |  Code and docs built on Azure OpenAI in Azure AI Foundry Models and Azure AI Document Intelligence to process and extract summaries, entities, and metadata from unstructured, multimodal documents and enable searching and chatting over this data.  | [https://github.com/microsoft/Document-Knowledge-Mining-Solution-Accelerator](https://github.com/microsoft/Document-Knowledge-Mining-Solution-Accelerator) |
 | [Knowledge Mining accelerator](https://github.com/Azure-Samples/azure-search-knowledge-mining/blob/main/README.md) | Code and docs to jump start a knowledge store using your data. | [https://github.com/Azure-Samples/azure-search-knowledge-mining](https://github.com/Azure-Samples/azure-search-knowledge-mining) |

@@ -10,7 +10,7 @@ ms.custom:
   - devx-track-dotnet
   - ignite-2023
 ms.topic: concept-article
-ms.date: 03/10/2025
+ms.date: 05/29/2025
 ---
 
 # C# samples for Azure AI Search
@@ -51,7 +51,8 @@ Code samples from the Azure AI Search team demonstrate features and workflows. A
 | Code sample | Related article  | Purpose |
 |-------------|------------------|---------|
 | [create-mvc-app](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/main/create-mvc-app) |  [Tutorial: Add search to an ASP.NET Core (MVC) app](tutorial-csharp-create-mvc-app.md) | While most samples are console applications, this MVC sample uses a web page to front the sample Hotels index, demonstrating basic search, pagination, and other server-side behaviors.|
-| [quickstart](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/main/quickstart/v11) | [Quickstart: Full text search using the Azure SDKs](search-get-started-text.md) | Covers the basic workflow for creating, loading, and querying a search index in C# using sample data. |
+| [quickstart](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/main/quickstart/v11) | [Quickstart: Full-text search using the Azure SDKs](search-get-started-text.md) | Covers the basic workflow for creating, loading, and querying a search index in C# using sample data. |
+| [quickstart-agentic-retrieval](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/main/quickstart-agentic-retrieval) | [Quickstart: Run agentic retrieval in Azure AI Search](search-get-started-agentic-retrieval.md) | Creates an LLM-powered knowledge agent that retrieves and synthesizes information from your search index. |
 | [quickstart-semantic-search](https://github.com/Azure-Samples/azure-search-dotnet-samples/blob/main/quickstart-semantic-search/) | [Quickstart: Semantic ranking using the Azure SDKs](search-get-started-semantic.md) | Shows the index schema and query request for invoking semantic ranker. |
 | [search-website](https://github.com/Azure-Samples/azure-search-static-web-app) | [Tutorial: Add search to web apps](tutorial-csharp-overview.md) | Demonstrates an end-to-end search app that includes bulk upload using the push APIs and a rich client for hosting the app and handling search requests.|
 | [tutorial-ai-enrichment](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/main/tutorial-ai-enrichment)  | [Tutorial: AI-generated searchable content from Azure blobs](cognitive-search-tutorial-blob-dotnet.md) | Shows how to configure an indexer and skillset. |

@@ -11,7 +11,7 @@ ms.custom:
   - devx-track-python
   - ignite-2023
 ms.topic: concept-article
-ms.date: 03/10/2025
+ms.date: 05/29/2025
 ---
 
 # Python samples for Azure AI Search
@@ -36,12 +36,14 @@ Code samples from the Azure AI Search team demonstrate features and workflows. M
 
 | Samples | Article |
 |---------|---------|
-| [Tutorial-RAG](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Tutorial-RAG) | Source code for the Python portion of [How to build a RAG solution using Azure AI Search](tutorial-rag-build-solution.md).|
-| [Quickstart](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart) | Source code for the Python portion of [Quickstart: Full text search using the Azure SDKs](search-get-started-text.md). This article covers the basic workflow for creating, loading, and querying a search index using sample data. |
+| [Quickstart](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart) | Source code for the Python portion of [Quickstart: Full-text search using the Azure SDKs](search-get-started-text.md). This sample covers the basic workflow for creating, loading, and querying a search index using sample data. |
+| [Quickstart-Agentic-Retrieval](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Agentic-Retrieval) | Source code for the Python portion of [Quickstart: Run agentic retrieval in Azure AI Search](search-get-started-agentic-retrieval.md). |
 | [Quickstart-RAG](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-RAG) | Source code for the Python portion of [Quickstart: Generative search (RAG) with grounding data from Azure AI Search](search-get-started-rag.md). |
-| [Quickstart-Semantic-Search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Semantic-Search) | Source code for the Python portion of [Quickstart: Semantic ranking using the Azure SDKs](search-get-started-semantic.md). It shows the index schema and query request for invoking semantic ranker. |
-| [bulk-insert](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/bulk-insert) | Source code for the Python example of how to [use the push APIs](search-how-to-load-search-index.md) to upload and index documents. |
+| [Quickstart-Semantic-Search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Semantic-Search) | Source code for the Python portion of [Quickstart: Semantic ranking using the Azure SDKs](search-get-started-semantic.md). This sample shows the index schema and query request for invoking semantic ranker. |
+| [Tutorial-RAG](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Tutorial-RAG) | Source code for the Python portion of [How to build a RAG solution using Azure AI Search](tutorial-rag-build-solution.md).|
+| [agentic-retrieval-pipeline-example](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/agentic-retrieval-pipeline-example) | Source code for the Python portion of [Build an agent-to-agent retrieval solution using Azure AI Search](search-agentic-retrieval-how-to-pipeline.md). Unlike the [quickstart](search-get-started-agentic-retrieval.md), this sample incorporates Azure AI Foundry Agent Service for data retrieval and orchestration. |
 | [azure-function-search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/azure-function-search) | Source code for the Python example of an Azure function that sends queries to a search service. You can substitute this Python version of the `api` code used in the [Add search to web sites](tutorial-csharp-overview.md) C# sample. |
+| [bulk-insert](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/bulk-insert) | Source code for the Python example of how to [use the push APIs](search-how-to-load-search-index.md) to upload and index documents. |
 
 ## Demos
 

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 03/10/2025
+ms.date: 05/29/2025
 ---
 
 # REST samples for Azure AI Search
@@ -28,10 +28,11 @@ Code samples from the Azure AI Search team demonstrate features and workflows. M
 |---------|---------|
 | [quickstart](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart) | Source code for [Quickstart: Text search using REST](search-get-started-rest.md). This sample covers the basic workflow for creating, loading, and querying a search index using sample data. |
 | [quickstart-vectors](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-vectors) | Source code for [Quickstart: Vector search using REST APIs](search-get-started-vector.md). This sample covers the basic workflow for indexing and querying vector data. |
+| [quickstart-agentic-retrieval](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/agentic-retrieval) | Source code for the REST portion of [Quickstart: Run agentic retrieval in Azure AI Search](search-get-started-agentic-retrieval.md). This sample shows you how to create an LLM-powered knowledge agent that retrieves and synthesizes information from your search index. |
 | [skillset-tutorial](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/skillset-tutorial) | Source code for [Tutorial: Use REST and AI to generate searchable content from Azure blobs](cognitive-search-tutorial-blob.md). This sample shows you how to create a skillset that iterates over Azure blobs to extract information and infer structure.|
 | [skill examples](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/skill-examples) | Skillset examples in indexer pipelines that include indexes and indexers so that you can follow field mappings, output field mappings, and source paths. |
 | [debug-sessions](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Debug-sessions) | Source code for [Tutorial: Diagnose, repair, and commit changes to your skillset](cognitive-search-tutorial-debug-sessions.md). This sample shows you how to use a skillset debug session in the Azure portal. REST is used to create the objects used during debug.|
-| [custom-analyzers](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/custom-analyzers) | Source code for [Tutorial: Create a custom analyzer for phone numbers](tutorial-create-custom-analyzer.md). This sample  explains how to use analyzers to preserve patterns and special characters in searchable content.|
+| [custom-analyzers](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/custom-analyzers) | Source code for [Tutorial: Create a custom analyzer for phone numbers](tutorial-create-custom-analyzer.md). This sample explains how to use analyzers to preserve patterns and special characters in searchable content.|
 | [index-json-blobs](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/index-json-blobs) | Create an indexer, data source, and index for nested JSON within a JSON array. This sample demonstrates the jsonArray parsing model and documentRoot parameters. |
 | [knowledge-store](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/knowledge-store) | Source code for [Create a knowledge store using REST](knowledge-store-create-rest.md). This sample explains the necessary steps for populating a knowledge store used for knowledge mining workflows. |
 | [projections](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/projections) | Source code for [Define projections in a knowledge store](knowledge-store-projections-examples.md). This sample explains how to specify the physical data structures in a knowledge store.|

@@ -219,7 +219,7 @@ Content-Type: application/json
 {
     "messages" : [
             {
-                "role" : "system",
+                "role" : "assistant",
                 "content" : [
                   { "type" : "text", "text" : "You are a helpful assistant for Contoso Human Resources. You have access to a search index containing guidelines about health care coverage for Washington state. If you can't find the answer in the search, say you don't know." }
                 ]
@@ -235,8 +235,8 @@ Content-Type: application/json
         { 
             "indexName" : "{{index-name}}",
             "filterAddOn" : "State eq 'WA'",
-            "IncludeReferenceSourceData": true, 
-            "rerankerThreshold " : 2.5,
+            "IncludeReferenceSourceData": true,
+            "rerankerThreshold" : 2.5
             "maxDocsForReranker": 250
         } 
     ]

@@ -9,7 +9,7 @@ ms.author: heidist
 ms.service: azure-ai-search
 ms.devlang: csharp
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ms.custom:
   - devx-track-csharp
   - devx-track-dotnet

@@ -6,7 +6,7 @@ author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: quickstart
-ms.date: 05/08/2025
+ms.date: 05/29/2025
 zone_pivot_groups: search-get-started-agentic-retrieval
 ---
 
@@ -24,12 +24,6 @@ zone_pivot_groups: search-get-started-agentic-retrieval
 
 ::: zone-end
 
-## Clean up resources
-
-When working in your own subscription, it's a good idea to finish a project by determining whether you still need the resources you created. Resources that are left running can cost you money. You can delete resources individually, or you can delete the resource group to delete the entire set of resources.
-
-In the Azure portal, you can find and manage resources by selecting **All resources** or **Resource groups** from the left pane.
-
 ## Related content
 
 + [Agentic retrieval in Azure AI Search](search-agentic-retrieval-concept.md)

@@ -348,4 +348,4 @@ This quickstart uses billable Azure resources. If you no longer need the resourc
 
 ## Next step
 
-This quickstart introduced you to the **Import and vectorize data wizard**, which creates all of the necessary objects for multimodal search. To explore each step in detail, see [Tutorial: Index mixed content using image verbalizations and the Document Layout skill](tutorial-multimodal-index-image-verbalization-skill.md).
+This quickstart introduced you to the **Import and vectorize data wizard**, which creates all of the necessary objects for multimodal search. To explore each step in detail, see [Tutorial: Index mixed content using image verbalizations and the Document Layout skill](tutorial-document-layout-image-verbalization.md).

@@ -2,7 +2,7 @@
 title: 'Quickstart: Deploy using Terraform'
 description: 'In this article, you create an Azure AI Search service using Terraform.'
 ms.topic: quickstart
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ms.custom:
   - devx-track-terraform
   - ignite-2023

@@ -8,36 +8,54 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Create an index alias in Azure AI Search
 
 > [!IMPORTANT]
 > Index aliases are currently in public preview and available under [supplemental terms of use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/).
 
-In Azure AI Search, an index alias is a secondary name that can be used to refer to an index for querying, indexing, and other operations. You can create an alias that maps to a search index and substitute the alias name in places where you would otherwise reference an index name. An alias adds flexibility if you need to change which index your application is pointing to. Instead of updating the references in your application, you can just update the mapping for your alias.
+An index alias in Azure AI Search is an alternate name for an index. You can use the alias instead of the index name in your application, which minimizes future updates to production code. If you need to switch to a newer index, you can update the alias mapping.
 
-The main goal of index aliases is to make it easier to manage your production indexes. For example, if you need to make a change to your index definition, such as editing a field or adding a new analyzer, you'll have to create a new search index because all search indexes are immutable. This means you either need to [drop and rebuild your index](search-howto-reindex.md) or create a new index and then migrate your application over to that index.
+Before using an alias, your application sends requests directly to `hotel-samples-index`.
 
-Instead of dropping and rebuilding your index, you can use index aliases. A typical workflow would be to: 
+```http
+POST /indexes/hotel-samples-index/docs/search?api-version=2025-05-01-preview
+{
+    "search": "pool spa +airport",
+    "select": "HotelId, HotelName, Category, Description",
+    "count": true
+}
+```
 
-1. Create your search index
-1. Create an alias that maps to your search index
-1. Have your application send querying/indexing requests to the alias rather than the index name
-1. When you need to make a change to your index that requires a rebuild, create a new search index 
-1. When your new index is ready to go, update the alias to map to the new index and requests will automatically be routed to the new index
+After using an alias, your application sends requests to `my-alias`, which maps to `hotel-samples-index`.
+
+```http
+POST /indexes/my-alias/docs/search?api-version=2025-05-01-preview
+{
+    "search": "pool spa +airport",
+    "select": "HotelId, HotelName, Category, Description",
+    "count": true
+}
+```
+
+## Supported scenarios
+
+You can only use an alias with document operations or to get and update an index definition. 
+
+Aliases can't be used to [delete an index](/rest/api/searchservice/indexes/delete), or [test text tokenization](/rest/api/searchservice/indexes/analyze), or referenced as the `targetIndexName` on an [indexer](/rest/api/searchservice/indexers/create-or-update).
 
 ## Create an index alias
 
 You can create an alias using the preview REST API, the preview SDKs, or through the [Azure portal](https://portal.azure.com). An alias consists of the `name` of the alias and the name of the search index that the alias is mapped to. Only one index name can be specified in the `indexes` array.
 
 ### [**REST API**](#tab/rest)
 
-You can use the [Create or Update Alias (REST preview)](/rest/api/searchservice/aliases/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) to create an index alias.
+You can use the [Create or Update Alias (REST preview)](/rest/api/searchservice/aliases/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true) to create an index alias.
 
 ```http
-POST /aliases?api-version=2024-05-01-preview
+POST /aliases?api-version=2025-05-01-preview
 {
     "name": "my-alias",
     "indexes": ["hotel-samples-index"]
@@ -75,12 +93,12 @@ Index aliases are also supported in the latest preview SDKs for [Java](https://c
 
 ## Send requests to an index alias
 
-Once you've created your alias, you're ready to start using it. Aliases can be used for all document operations including querying, indexing, suggestions, and autocomplete.
+Aliases can be used for all document operations including querying, indexing, suggestions, and autocomplete.
 
-In the query below, instead of sending the request to `hotel-samples-index`, you can instead send the request to `my-alias` and it will be routed accordingly. 
+This query sends the request to `my-alias`, which is mapped to an actual index on your search service. 
 
 ```http
-POST /indexes/my-alias/docs/search?api-version=2024-05-01-preview
+POST /indexes/my-alias/docs/search?api-version=2025-05-01-preview
 {
     "search": "pool spa +airport",
     "searchMode": any,
@@ -90,29 +108,19 @@ POST /indexes/my-alias/docs/search?api-version=2024-05-01-preview
 }
 ```
 
-If you expect to make updates to a production index, specify an alias rather than the index name in your client-side application. Scenarios that require an index rebuild are outlined in [Drop and rebuild an index](search-howto-reindex.md).
-
-> [!NOTE]
-> You can only use an alias with document operations or to get and update an index definition. Aliases can't be used to delete an index, can't be used with the Analyze Text API, and can't be used as the `targetIndexName` on an indexer.
-> 
-> An update to an alias may take up to 10 seconds to propagate through the system so you should wait at least 10 seconds before performing any operation in the index that has been mapped or recently was mapped to the alias.
+## Update an alias
 
-## Swap indexes
-
-Now, whenever you need to update your application to point to a new index, all you need to do is update the mapping in your alias. PUT is required for updates as described in [Create or Update Alias (REST preview)](/rest/api/searchservice/aliases/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true).
+PUT is required for alias updates as described in [Create or Update Alias (REST preview)](/rest/api/searchservice/aliases/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true).
 
 ```http
-PUT /aliases/my-alias?api-version=2024-05-01-preview
+PUT /aliases/my-alias?api-version=2025-05-01-preview
 {
     "name": "my-alias",
     "indexes": ["hotel-samples-index2"]
 }
 ```
 
-After you make the update to the alias, requests will automatically start to be routed to the new index.
-
-> [!NOTE]
-> An update to an alias may take up to 10 seconds to propagate through the system so you should wait at least 10 seconds before deleting the index that the alias was previously mapped to.
+An update to an alias may take up to 10 seconds to propagate through the system so you should wait at least 10 seconds before deleting the index that the alias was previously mapped to.
 
 ## See also
 

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2024
 ms.topic: how-to
-ms.date: 11/20/2024
+ms.date: 05/29/2025
 ---
 
 # Index Markdown blobs and files in Azure AI Search
@@ -38,7 +38,7 @@ In Azure AI Search, indexers for Azure Blob Storage, Azure Files, and OneLake su
 Parsing mode parameters are specified in an indexer definition when you create or update an indexer.
 
 ```http
-POST https://[service name].search.windows.net/indexers?api-version=2024-11-01-preview
+POST https://[service name].search.windows.net/indexers?api-version=2025-05-01-preview
 Content-Type: application/json
 api-key: [admin key]
 
@@ -155,7 +155,7 @@ An example index configuration might look something like this:
 If field names and data types align, the blob indexer can infer the mapping without an explicit field mapping present in the request, so an indexer configuration corresponding to the provided index configuration might look like this:
 
 ```http
-POST https://[service name].search.windows.net/indexers?api-version=2024-11-01-preview
+POST https://[service name].search.windows.net/indexers?api-version=2025-05-01-preview
 Content-Type: application/json
 api-key: [admin key]
 
@@ -337,7 +337,7 @@ If you aren't utilizing field mappings, the shape of the index should reflect th
 ### Indexer definition for one-to-one parsing
 
 ```http
-POST https://[service name].search.windows.net/indexers?api-version=2024-11-01-preview
+POST https://[service name].search.windows.net/indexers?api-version=2025-05-01-preview
 Content-Type: application/json
 api-key: [admin key]
 

@@ -9,11 +9,11 @@ manager: nitinme
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
-ms.topic: conceptual
-ms.date: 12/10/2024
+ms.topic: how-to
+ms.date: 05/29/2025
 ---
 
-# Indexer connections to a SQL Server instance on an Azure virtual machine
+# Configure an indexer connection to a SQL Server instance on an Azure virtual machine
 
 When configuring an [Azure SQL indexer](search-how-to-index-sql-database.md) to extract content from a database on an Azure virtual machine, extra steps are required for secure connections. 
 
@@ -44,11 +44,13 @@ Azure AI Search requires an encrypted channel for all indexer requests over a pu
 
    1. In regedit, browse to this registry key: `HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Microsoft SQL Server\[MSSQL13.MSSQLSERVER]\MSSQLServer\SuperSocketNetLib\Certificate`. 
 
-      The `[MSSQL13.MSSQLSERVER]` part varies based on version and instance name. 
+      The `[MSSQL13.MSSQLSERVER]` part varies based on version and instance name.
 
    1. Set the value of the **Certificate** key to the **thumbprint** (without spaces) of the TLS/SSL certificate you imported to the VM.
 
-     There are several ways to get the thumbprint, some better than others. If you copy it from the **Certificates** snap-in in MMC, you might pick up an invisible leading character [as described in this support article](https://support.microsoft.com/kb/2023869/), which results in an error when you attempt a connection. Several workarounds exist for correcting this problem. The easiest is to backspace over and then retype the first character of the thumbprint to remove the leading character in the key value field in regedit. Alternatively, you can use a different tool to copy the thumbprint.
+      For example, copy the hexadecimal characters to text editor, such as Notepad. Delete all spaces from the thumbprint string. If the thumbprint is `c0 d0 f2 70 95 b0 3d 43 17 e2 19 84 10 24 32 8c ef 24 87 79`, then change it to `c0d0f27095b03d4317e219841024328cef248779`.
+
+      There are several ways to get the thumbprint, some better than others. If you copy it from the **Certificates** snap-in in MMC, you might pick up an invisible leading character, which results in an error when you attempt a connection. Several workarounds exist for correcting this problem. The easiest is to backspace over and then retype the first character of the thumbprint to remove the leading character in the key value field in regedit. Alternatively, you can use a different tool to copy the thumbprint. For more information, see [Certificate thumbprint displayed in MMC certificate snap-in has extra invisible unicode character](https://support.microsoft.com/help/2023835/certificate-thumbprint-displayed-in-mmc-certificate-snap-in-has-extra). 
 
 1. Grant permissions to the service account. 
 

@@ -86,7 +86,7 @@ In this section, you retrieve the endpoint and Microsoft Entra token for your Az
 
 1. To obtain your Microsoft Entra token, run the following command on your local system. This step requires completion of [Quickstart: Connect without keys](search-get-started-rbac.md).
 
-   ```Azure CLI
+   ```azurecli
    az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv
    ```
 

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Index data from Azure Data Lake Storage Gen2

@@ -11,7 +11,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Index data from Azure Cosmos DB for Apache Gremlin for queries in Azure AI Search

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Index data from Azure Cosmos DB for MongoDB for queries in Azure AI Search

@@ -12,15 +12,15 @@ ms.topic: how-to
 ms.custom:
   - kr2b-contr-experiment
   - ignite-2023
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Index data from Azure Database for MySQL Flexible Server
 
 > [!IMPORTANT]
-> MySQL support is currently in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). You can use 2020-06-30-preview or later to index your content. We recommend the latest preview API. There is currently no portal support.
+> MySQL support is currently in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). We recommend the latest preview API. There is currently no portal support.
 
-In this article, learn how to configure an [**indexer**](search-indexer-overview.md) that imports content from Azure Database for MySQL and makes it searchable in Azure AI Search. Inputs to the indexer are your row, in a single table or view. Output is a search index with searchable content in individual fields.
+In this article, learn how to configure an [**indexer**](search-indexer-overview.md) that imports content from Azure Database for MySQL and makes it searchable in Azure AI Search. Inputs to the indexer are rows from a single table or view. Output is a search index with searchable content in individual fields.
 
 This article supplements [**Create an indexer**](search-howto-create-indexers.md) with information that's specific to indexing from Azure Database for MySQL Flexible Server. It uses the REST APIs to demonstrate a three-part workflow common to all indexers: create a data source, create an index, create an indexer. Data extraction occurs when you submit the Create Indexer request.
 
@@ -50,7 +50,7 @@ As noted, there’s no portal support for indexer creation, but a MySQL indexer
 
 The data source definition specifies the data to index, credentials, and policies for identifying changes in the data. The data source is defined as an independent resource so that it can be used by multiple indexers.
 
-[Create or Update Data Source](/rest/api/searchservice/data-sources/create?view=rest-searchservice-2024-05-01-preview&preserve-view=true) specifies the definition. Be sure to use a preview REST API when creating the data source.
+[Create or Update Data Source](/rest/api/searchservice/data-sources/create?view=rest-searchservice-2025-05-01-preview&preserve-view=true) specifies the definition. Be sure to use a preview REST API when creating the data source.
 
 ```http
 {   
@@ -88,7 +88,7 @@ The data source definition specifies the data to index, credentials, and policie
 
 ## Create an index
 
-[Create or Update Index](/rest/api/searchservice/indexes/create?view=rest-searchservice-2024-05-01-preview&preserve-view=true) specifies the index schema:
+[Create or Update Index](/rest/api/searchservice/indexes/create?view=rest-searchservice-2025-05-01-preview&preserve-view=true) specifies the index schema:
 
 ```http
 {
@@ -128,7 +128,7 @@ The following table maps the MySQL database to Azure AI Search equivalents. For
 
 Once the index and data source have been created, you're ready to create the indexer. Indexer configuration specifies the inputs, parameters, and properties controlling run time behaviors.
 
-[Create or update an indexer](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) by giving it a name and referencing the data source and target index:
+[Create or update an indexer](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true) by giving it a name and referencing the data source and target index:
 
 ```http
 {
@@ -157,10 +157,10 @@ Once the index and data source have been created, you're ready to create the ind
 
 ## Check indexer status
 
-Send a [Get Indexer Status](/rest/api/searchservice/indexers/get-status?view=rest-searchservice-2024-05-01-preview&preserve-view=true) request to monitor indexer execution:
+Send a [Get Indexer Status](/rest/api/searchservice/indexers/get-status?view=rest-searchservice-2025-05-01-preview&preserve-view=true) request to monitor indexer execution:
 
 ```http
-GET https://myservice.search.windows.net/indexers/myindexer/status?api-version=2024-05-01-preview
+GET https://myservice.search.windows.net/indexers/myindexer/status?api-version=2025-05-01-preview
   Content-Type: application/json  
   api-key: [admin key]
 ```

@@ -11,7 +11,7 @@ ms.custom:
   - ignite-2023
   - build-2024
 ms.topic: how-to
-ms.date: 11/22/2024
+ms.date: 05/29/2025
 ---
 
 # Configure a search service to connect using a managed identity in Azure AI Search

@@ -10,7 +10,7 @@ ms.custom:
   - ignite-2023
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Set up an indexer connection to Azure SQL using a managed identity
@@ -90,7 +90,7 @@ Create the data source and provide either a system-assigned managed identity or
 
 ### System-assigned managed identity
 
-The [REST API](/rest/api/searchservice/data-sources/create), Azure portal, and the [.NET SDK](/dotnet/api/azure.search.documents.indexes.models.searchindexerdatasourceconnection) support system-assigned managed identity. 
+The [REST API](/rest/api/searchservice/data-sources/create), Azure portal, and the Azure SDKs support system-assigned managed identity. 
 
 When you're connecting with a system-assigned managed identity, the only change to the data source definition is the format of the "credentials" property. You'll provide an Initial Catalog or Database name and a ResourceId that has no account key or password. The ResourceId must include the subscription ID of Azure SQL Database, the resource group of SQL Database, and the name of the SQL database.
 
@@ -115,16 +115,16 @@ api-key: [admin key]
 
 ### User-assigned managed identity (preview)
 
-The 2021-04-30-preview and later preview REST APIs support connections based on a user-assigned managed identity. When you're connecting with a user-assigned managed identity, there are two changes to the data source definition:
+Preview REST APIs support connections based on a user-assigned managed identity. When you're connecting with a user-assigned managed identity, there are two changes to the data source definition:
 
 * First, the format of the "credentials" property is an Initial Catalog or Database name and a ResourceId that has no account key or password. The ResourceId must include the subscription ID of Azure SQL Database, the resource group of SQL Database, and the name of the SQL database. This is the same format as the system-assigned managed identity.
 
 * Second, add an "identity" property that contains the collection of user-assigned managed identities. Only one user-assigned managed identity should be provided when creating the data source. Set it to type "userAssignedIdentities".
 
-Here's an example of how to create an indexer data source object using the most recent preview API version for [Create or Update Data Source](/rest/api/searchservice/data-sources/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true):
+Here's an example of how to create an indexer data source object using the most recent preview API version for [Create or Update Data Source](/rest/api/searchservice/data-sources/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true):
 
 ```http
-POST https://[service name].search.windows.net/datasources?api-version=2024-05-01-preview
+POST https://[service name].search.windows.net/datasources?api-version=2025-05-01-preview
 Content-Type: application/json
 api-key: [admin key]
 

@@ -11,7 +11,7 @@ ms.topic: how-to
 ms.custom:
   - subject-moving-resources
   - ignite-2023
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Move your Azure AI Search service to another Azure region

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Set up an indexer connection to Azure SQL Managed Instance using a managed identity
@@ -46,12 +46,11 @@ Follow these steps to assign the search service system managed identity permissi
 
    :::image type="content" source="./media/search-index-azure-sql-managed-instance-with-managed-identity/sql-login.png" alt-text="Showing screenshot of the Connect to Server dialog.":::
 
-3. From the left pane, locate the SQL database you are using as data source for indexing and right-click it. Select **New Query**. 
+1. From the left pane, locate the SQL database you are using as data source for indexing and right-click it. Select **New Query**. 
 
    :::image type="content" source="./media/search-index-azure-sql-managed-instance-with-managed-identity/new-sql-query.png" alt-text="Showing screenshot of new SQL query.":::
 
-
-4. In the T-SQL window, copy the following commands and include the brackets around your search service name. Click on **Execute**.
+1. In the T-SQL window, copy the following commands and include the brackets around your search service name. Click on **Execute**.
 
     
     ```sql
@@ -79,8 +78,7 @@ In this step, you'll give your Azure AI Search service permission to read data f
 
    :::image type="content" source="./media/search-index-azure-sql-managed-instance-with-managed-identity/access-control-add-role-assignment.png" alt-text="Showing screenshot of the Access Control page." lightbox="media/search-index-azure-sql-managed-instance-with-managed-identity/access-control-add-role-assignment.png":::
 
-
-4. Select **Reader** role.
+1. Select **Reader** role.
 1. Leave **Assign access to** as **Microsoft Entra user, group, or service principal**.
 1. If you're using a system-assigned managed identity, search for your search service, then select it. If you're using a user-assigned managed identity, search for the name of the user-assigned managed identity, then select it. Select **Save**.
 
@@ -101,7 +99,7 @@ When you're connecting with a system-assigned managed identity, the only change
 Here's an example of how to create a data source to index data from a storage account using the [Create Data Source](/rest/api/searchservice/data-sources/create) REST API and a managed identity connection string. The managed identity connection string format is the same for the REST API, .NET SDK, and the Azure portal.  
 
 ```http
-POST https://[service name].search.windows.net/datasources?api-version=2020-06-30
+POST https://[service name].search.windows.net/datasources?api-version=2024-07-01
 Content-Type: application/json
 api-key: [admin key]
 
@@ -124,7 +122,7 @@ The index specifies the fields in a document, attributes, and other constructs t
 Here's a [Create Index](/rest/api/searchservice/indexes/create) REST API call with a searchable `booktitle` field:   
 
 ```http
-POST https://[service name].search.windows.net/indexes?api-version=2020-06-30
+POST https://[service name].search.windows.net/indexes?api-version=2024-07-01
 Content-Type: application/json
 api-key: [admin key]
 
@@ -144,7 +142,7 @@ An indexer connects a data source with a target search index, and provides a sch
 Here's a [Create Indexer](/rest/api/searchservice/indexers/create) REST API call with an Azure SQL indexer definition. The indexer runs when you submit the request.
 
 ```http
-POST https://[service name].search.windows.net/indexers?api-version=2020-06-30
+POST https://[service name].search.windows.net/indexers?api-version=2024-07-01
 Content-Type: application/json
 api-key: [admin key]
 

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Configure IP firewall rules to allow indexer connections from Azure AI Search

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2024
 ms.topic: how-to
-ms.date: 02/07/2025
+ms.date: 05/28/2025
 ---
 
 # Make outbound connections through a shared private link
@@ -79,7 +79,7 @@ When evaluating shared private links for your scenario, remember these constrain
   |----------|-------------------|---------------------|---------------------|
   | Indexers without skillsets | Basic and higher | None | None |
   | Skillsets with embedding skills ([integrated vectorization](vector-search-integrated-vectorization.md)) | Basic and higher | [High capacity regions](search-limits-quotas-capacity.md#partition-storage-gb) | [After April 3, 2024](search-how-to-upgrade.md#check-your-service-creation-or-upgrade-date) |
-  | Skillsets using other [built-in](cognitive-search-predefined-skills.md) or custom skills | Standard 2 (S2) and higher | None | [After April 3, 2024](search-how-to-upgrade.md#check-your-service-creation-or-upgrade-date) |
+  | Skillsets using other [built-in](cognitive-search-predefined-skills.md) or custom skills | Standard 1 (S1) and higher | None | [After April 3, 2024](search-how-to-upgrade.md#check-your-service-creation-or-upgrade-date) |
 
 + Permissions on both Azure AI Search and the Azure resource:
 
@@ -90,7 +90,9 @@ When evaluating shared private links for your scenario, remember these constrain
 
 <!-- + For [integrated vectorization](vector-search-integrated-vectorization.md) only, outbound connections through shared private link are supported on all billable tiers, on services [created after April 3, 2024](search-how-to-upgrade.md#check-your-service-creation-or-upgrade-date), in regions providing [higher capacity](search-limits-quotas-capacity.md#partition-storage-gb).  -->
 
-<!-- + For [AI enrichment](cognitive-search-concept-intro.md) and skillset processing, shared private link  that doesn't include an embedding skill and in services [created before April 3, 2024](search-how-to-upgrade.md#check-your-service-creation-or-upgrade-date), Azure AI Search must be Standard 2 (S2) or higher. -->
+<!-- + For [AI enrichment](cognitive-search-concept-intro.md) and skillset processing, shared private link in services [created before April 3, 2024](search-how-to-upgrade.md#check-your-service-creation-or-upgrade-date), Azure AI Search must be Standard 2 (S2) or higher. -->
+
+<!-- + For [AI enrichment](cognitive-search-concept-intro.md) and skillset processing, shared private link in services [created after April 3, 2024](search-how-to-upgrade.md#check-your-service-creation-or-upgrade-date), Azure AI Search must be Standard 1 (S1) or higher. -->
 
 <!-- + For all other use cases, that don't involve skillsets, Azure AI Search can be Basic or higher. -->
 

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Make indexer connections to Azure Storage as a trusted service

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Indexer troubleshooting guidance for Azure AI Search

@@ -10,12 +10,12 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Create an index for multiple languages in Azure AI Search
 
-If you have strings in multiple languages, you can attach [language analyzers](index-add-language-analyzers.md#supported-language-analyzers) that analyze strings using linguistic rules of a specific language during indexing and query execution. With a language analyzer, you get better handling of diacritics, character variants, punctuation, and word root forms.
+If you have strings in multiple languages, you can use [vector search](vector-search-overview.md) to represent multilingual content mathematically, which is the more modern approach. Alternatively, if you aren't using vectors, you can attach [language analyzers](index-add-language-analyzers.md#supported-language-analyzers) that analyze strings using linguistic rules of a specific language during indexing and query execution. With a language analyzer, you get better handling of diacritics, character variants, punctuation, and word root forms.
 
 Azure AI Search supports Microsoft and Lucene analyzers. By default, the search engine uses Standard Lucene, which is language agnostic. If testing indicates that the default analyzer is insufficient, replace it with a language analyzer.
 

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Design patterns for multitenant SaaS applications and Azure AI Search

@@ -9,15 +9,15 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Visualize Azure AI Search Logs and Metrics with Power BI
 
 Azure AI Search can send operation logs and service metrics to an Azure Storage account, which can then be visualized in Power BI. This article explains the steps and how to use a Power BI template app to visualize the data. The template covers information about queries, indexing, operations, and service metrics.
 
 > [!NOTE]
-> The Power BI template is currently using the previous product name, Azure Cognitive Search. The name change will be updated on the next template refresh.
+> The Power BI template currently uses a former product name, Azure Cognitive Search. The product name will be updated on the next template refresh.
 
 ## Set up logging and install the template
 

@@ -9,13 +9,13 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 12/10/2024
+ms.date: 05/19/2025
 ---
 
 # Text normalization for case-insensitive filtering, faceting and sorting
 
 > [!IMPORTANT] 
-> This feature is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). The [preview REST API](/rest/api/searchservice/index-preview) supports this feature.
+> This feature is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). We recommend the latest [preview REST API version](/rest/api/searchservice/search-service-api-versions) for this feature.
 
 In Azure AI Search, a *normalizer* is a component that pre-processes text for keyword matching over fields marked as "filterable", "facetable", or "sortable". In contrast with full text "searchable" fields that are paired with [text analyzers](search-analyzers.md), content that's created for filter-facet-sort operations doesn't undergo analysis or tokenization. Omission of text analysis can produce unexpected results when casing and character differences show up, which is why you need a normalizer to homogenize variations in your content.
 

@@ -10,12 +10,12 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 12/09/2024
+ms.date: 05/29/2025
 ---
 
 # Shape search results or modify search results composition in Azure AI Search
 
-This article explains search results composition and how to shape search results to fit your scenarios. Search results are returned in a query response. The shape of a response is determined by parameters in the query itself. These parameters include:
+This article explains search results composition and how to shape full text search results to fit your scenarios. Search results are returned in a query response. The shape of a response is determined by parameters in the query itself. These parameters include:
 
 + Number of matches found in the index (`count`)
 + Number of matches returned in the response (50 by default, configurable through `top`) or per page (`skip` and `top`)

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Querying in Azure AI Search
@@ -21,12 +21,13 @@ Azure AI Search supports query constructs for a broad range of scenarios, from f
 
 | Query form | Searchable content | Description |
 |------------|--------------------|-------------|
-| [full text search](search-lucene-query-architecture.md) | Inverted indexes of tokenized terms. | Full text queries iterate over inverted indexes that are structured for fast scans, where a match can be found in potentially any field, within any number of search documents. Text is analyzed and tokenized for full text search.|
+| [Full text search](search-lucene-query-architecture.md) | Inverted indexes of tokenized terms. | Full text queries iterate over inverted indexes that are structured for fast scans, where a match can be found in potentially any field, within any number of search documents. Text is analyzed and tokenized for full text search.|
 | [Vector search](vector-search-overview.md) | Vector indexes of generated embeddings. | Vector queries iterate over vector fields in a search index. |
 | [Hybrid search](hybrid-search-overview.md) | All of the above, in a single search index. | Combines text search and vector search in a single query request. Text search works on plain text content in "searchable" and "filterable" fields. Vector search works on content in vector fields. |
+| [Agentic retrieval (preview)](search-agentic-retrieval-concept.md) | All of the above, in a single search index. | This is an alternative retrieval path on Azure AI Search that leverages large language models for query planning. The response is designed for agent consumption, where the agent rather than search app client code coordinates the response delivered to the user. |
 | Others | Plain text and human-readable content.| Raw content, extracted verbatim from source documents, supporting filters and pattern matching queries like geo-spatial search, fuzzy search, and fielded search. |
 
-This article brings focus to the last category: queries that work on plain text and human-readable content, extracted intact from original source, used for filters and other specialized query forms.
+The remainder of this article brings focus to the last category: classic queries that work on plain text and human-readable content, extracted intact from original source, used for filters and other specialized query forms. If you're creating a traditional search application that isn't using AI, this section explains the query methods that you can implement in your client code.
 
 ## Autocomplete and suggested queries
 

@@ -8,8 +8,8 @@ ms.author: beloh
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
-ms.topic: conceptual
-ms.date: 12/10/2024
+ms.topic: reference
+ms.date: 05/29/2025
 ---
 # Troubleshooting OData collection filters in Azure AI Search
 

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Understand how OData collection filters work in Azure AI Search

@@ -9,7 +9,7 @@ ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: conceptual
 ms.custom: references_regions
-ms.date: 05/15/2025
+ms.date: 05/29/2025
 
 ---
 
@@ -39,36 +39,36 @@ You can create an Azure AI Search service in any of the following Azure public r
 
 | Region | AI enrichment | Availability zones | Agentic retrieval | Semantic ranker | Query rewrite |
 |--|--|--|--|--|--|
-| Brazil South​​ ​| ✅ |  | ✅ | ✅ |  |
-| Canada Central​​ | ✅ | ✅ | ✅ | ✅ |  |
+| Brazil South​​ ​| ✅ |  | ✅ | ✅ | ✅ |
+| Canada Central​​ | ✅ | ✅ | ✅ | ✅ | ✅ |
 | Canada East​​ ​|  |  | ✅ | ✅ |  |
-| ​Central US​​ | ✅ | ✅ | ✅ | ✅ |  |
+| ​Central US​​ | ✅ | ✅ | ✅ | ✅ | ✅ |
 | East US​ | ✅ | ✅ | ✅ | ✅ |  |
-| East US 2 ​ | ✅ | ✅ | ✅ | ✅ |  |
+| East US 2 ​ | ✅ | ✅ | ✅ | ✅ | ✅ |
 | Mexico Central |  | ✅ |  |  |  |
-| North Central US​ ​| ✅ |  | ✅ | ✅ |  |
-| South Central US​ | ✅ | ✅ | ✅ | ✅ |  |
-| West US​​ | ✅ |  | ✅ | ✅ |  |
-| West US 2​ <sup>1</sup>​| ✅ | ✅ | ✅ | ✅ |  |
-| West US 3​ | ✅ | ✅ | ✅ | ✅ |  |
+| North Central US​ ​| ✅ |  | ✅ | ✅ | ✅ |
+| South Central US​ | ✅ | ✅ | ✅ | ✅ | ✅ |
+| West US​​ | ✅ |  | ✅ | ✅ | ✅ |
+| West US 2​ <sup>1</sup>​| ✅ | ✅ | ✅ | ✅ | ✅ |
+| West US 3​ | ✅ | ✅ | ✅ | ✅ | ✅ |
 | West Central US​ ​ | ✅ |  | ✅ | ✅ |  |
 
 ### Europe
 
 | Region | AI enrichment | Availability zones | Agentic retrieval | Semantic ranker | Query rewrite |
 |--|--|--|--|--|--|
 | North Europe​​ | ✅ | ✅ | ✅ | ✅ | ✅ |
-| West Europe​​ | ✅ | ✅ | ✅ | ✅ |  |
-| France Central​​ | ✅ | ✅ | ✅ | ✅ |  |
-| Germany West Central​ ​| ✅ | ✅ |  |  |  |
-| Italy North​​ |  | ✅ |  |  |  |
+| West Europe​​ | ✅ | ✅ | ✅ | ✅ | ✅ |
+| France Central​​ | ✅ | ✅ | ✅ | ✅ | ✅ |
+| Germany West Central​ ​| ✅ | ✅ | ✅ | ✅ |  |
+| Italy North​​ |  | ✅ | ✅ | ✅ |  |
 | Norway East​​ | ✅ | ✅ |  |  |  |
-| Poland Central​​ |  |  |  |  |  |
+| Poland Central​​ |  |  | ✅ | ✅ |  |
 | Spain Central <sup>2</sup> |  | ✅ |  |  |  |
 | Sweden Central​​ | ✅ | ✅ | ✅ | ✅ | ✅ |
-| Switzerland North​ | ✅ | ✅ | ✅ | ✅ |  |
+| Switzerland North​ | ✅ | ✅ | ✅ | ✅ | ✅ |
 | Switzerland West​ | ✅ | ✅ | ✅ | ✅ |  |
-| UK South​ | ✅ | ✅ | ✅ | ✅ |  |
+| UK South​ | ✅ | ✅ | ✅ | ✅ | ✅ |
 | UK West​ ​|  |  | ✅ | ✅ |  |
 
 <sup>1</sup> This region has capacity constraints on the following tiers: S2, S3, L1, and L2.
@@ -80,31 +80,31 @@ You can create an Azure AI Search service in any of the following Azure public r
 | Region | AI enrichment | Availability zones | Agentic retrieval | Semantic ranker | Query rewrite |
 |--|--|--|--|--|--|
 | Israel Central​ <sup>1</sup> |  | ✅ |  |  |  |
-| Qatar Central​ <sup>1</sup> |  | ✅ |  |  |  |
-| UAE North​​ | ✅ | ✅ |  |  |  |
+| Qatar Central​ <sup>1</sup> |  | ✅ | ✅ | ✅ |  |
+| UAE North​​ | ✅ | ✅ | ✅ | ✅ |  |
 
 <sup>1</sup> [Higher storage limits](search-limits-quotas-capacity.md#service-limits) aren't available in this region. If you want higher limits, choose a different region.
 
 ### Africa
 
 | Region | AI enrichment | Availability zones | Agentic retrieval | Semantic ranker | Query rewrite |
 |--|--|--|--|--|--|
-| South Africa North​ | ✅ | ✅ |  |  |  |
+| South Africa North​ | ✅ | ✅ | ✅ | ✅ |  |
 
 ### Asia Pacific
 
 | Region | AI enrichment | Availability zones | Agentic retrieval | Semantic ranker | Query rewrite |
 |--|--|--|--|--|--|
-| Australia East​ ​| ✅ | ✅ | ✅ | ✅ |  |
+| Australia East​ ​| ✅ | ✅ | ✅ | ✅ | ✅ |
 | Australia Southeast​​​ |  |  | ✅ | ✅ |  |
-| East Asia​ | ✅ | ✅ | ✅ | ✅ |  |
+| East Asia​ | ✅ | ✅ | ✅ | ✅ | ✅ |
 | Southeast Asia​​ | ✅ | ✅ | ✅ | ✅ | ✅ |
-| Central India | ✅ | ✅ | ✅ | ✅ |  |
-| Jio India West​​ | ✅ |  | ✅ | ✅ |  |
+| Central India | ✅ | ✅ | ✅ | ✅ | ✅ |
+| Jio India West​​ | ✅ |  | ✅ | ✅ | ✅ |
 | South India |  | ✅ |  |  |  |
-| Japan East <sup>1</sup> | ✅ | ✅ | ✅ | ✅ |  |
+| Japan East <sup>1</sup> | ✅ | ✅ | ✅ | ✅ | ✅ |
 | Japan West​ | ✅ |  | ✅ | ✅ |  |
-| Korea Central | ✅ | ✅ | ✅ | ✅ |  |
+| Korea Central | ✅ | ✅ | ✅ | ✅ | ✅ |
 | Korea South​​ |  |  | ✅ | ✅ |  |
 | Indonesia Central |  | ✅ |  |  |  |
 
@@ -114,9 +114,9 @@ You can create an Azure AI Search service in any of the following Azure public r
 
 | Region | AI enrichment | Availability zones | Agentic retrieval | Semantic ranker | Query rewrite |
 |--|--|--|--|--|--|
-| Arizona | ✅ |  | ✅ | ✅ |  |
+| Arizona | ✅ |  | ✅ | ✅ | ✅ |
 | Texas |  |  |  |  |  |
-| Virginia | ✅ | ✅ | ✅ | ✅ |  |
+| Virginia | ✅ | ✅ | ✅ | ✅ | ✅ |
 
 ## Azure operated by 21Vianet
 
@@ -127,7 +127,7 @@ You can create an Azure AI Search service in any of the following Azure public r
 | China East 3 |  |  |  |  |  |
 | China North |  |  |  |  |  |
 | China North 2 <sup>1</sup> |  |  |  |  |  |
-| China North 3 |  | ✅ | ✅ | ✅ |  |
+| China North 3 |  | ✅ | ✅ | ✅ | ✅ |
 
 <sup>1</sup> [Higher storage limits](search-limits-quotas-capacity.md#service-limits) aren't available in this region. If you want higher limits, choose a different region.
 

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2024
 ms.topic: how-to
-ms.date: 12/05/2024
+ms.date: 05/29/2025
 ---
 
 # Add a search service to a network security perimeter

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Migrate semantic ranking code from previous versions
@@ -42,6 +42,8 @@ Check your code for the REST API version or SDK package version to confirm which
 | stable | [2024-07-01](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-07-01&preserve-view=true) | No change |
 | preview | [2024-09-01-preview](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-09-01-preview&preserve-view=true) | No change |
 | preview | [2024-11-01-preview](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-11-01-preview&preserve-view=true) | Adds query rewrite. The `queryLanguage` property is now required if you use [query rewrite (preview)](semantic-how-to-query-rewrite.md).  |
+| preview | [2025-03-01-preview](/rest/api/searchservice/operation-groups?view=rest-searchservice-2025-03-01-preview&preserve-view=true) | Adds opt-in to prerelease versions of semantic models. |
+| preview | [2025-05-01-preview](/rest/api/searchservice/operation-groups?view=rest-searchservice-2025-05-01-preview&preserve-view=true) | No API updates in this preview, but semantic ranking now has [better integration with scoring profiles](semantic-how-to-enable-scoring-profiles.md). |
 
 ## Change logs for Azure SDKs
 

@@ -9,8 +9,8 @@ ms.author: heidist
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
-ms.topic: conceptual
-ms.date: 12/10/2024
+ms.topic: how-to
+ms.date: 05/29/2025
 ---
 
 # Add spell check to queries in Azure AI Search
@@ -32,18 +32,18 @@ Use a search client that supports preview APIs on the query request. You can use
 
 | Client library | Versions |
 |----------|----------|
-| REST API | Versions 2020-06-30-Preview and later. We recommend the latest preview API. [2024-05-01-preview](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&preserve-view=true)|
-| Azure SDK for .NET | [version 11.5.0-beta.5](https://www.nuget.org/packages/Azure.Search.Documents/11.5.0-beta.5) | 
-| Azure SDK for Java |  [version 11.6.0-beta.5](https://central.sonatype.com/artifact/com.azure/azure-search-documents) |
+| REST API | Versions 2020-06-30-Preview and later. We recommend the latest preview API. [2025-05-01-preview](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2025-05-01-preview&preserve-view=true)|
+| Azure SDK for .NET | [version 11.7.0-beta.4](https://www.nuget.org/packages/Azure.Search.Documents/11.7.0-beta.4) | 
+| Azure SDK for Java |  [version 11.8.0-beta.7](https://central.sonatype.com/artifact/com.azure/azure-search-documents/11.8.0-beta.7) |
 | Azure SDK for JavaScript | [version 11.3.0-beta.8](https://www.npmjs.com/package/@azure/search-documents/v/11.3.0-beta.8) |
-| Azure SDK for Python | [version 11.4.0b3](https://pypi.org/project/azure-search-documents/11.4.0b3/) |
+| Azure SDK for Python | [version 11.6.0b12](https://pypi.org/project/azure-search-documents/11.6.0b12/) |
 
 ## Spell correction with simple search
 
 The following example uses the built-in hotels-sample index to demonstrate spell correction on a simple text query. Without spell correction, the query returns zero results. With correction, the query returns one result for Johnson's family-oriented resort.
 
 ```http
-POST https://[service name].search.windows.net/indexes/hotels-sample-index/docs/search?api-version=2024-05-01-preview
+POST https://[service name].search.windows.net/indexes/hotels-sample-index/docs/search?api-version=2025-05-01-preview
 {
     "search": "famly acitvites",
     "speller": "lexicon",
@@ -64,7 +64,7 @@ Spelling correction occurs on individual query terms that undergo text analysis,
 This example uses fielded search over the Category field, with full Lucene syntax, and a misspelled query term. By including speller, the typo in "Suiite" is corrected and the query succeeds.
 
 ```http
-POST https://[service name].search.windows.net/indexes/hotels-sample-index/docs/search?api-version=2024-05-01-preview
+POST https://[service name].search.windows.net/indexes/hotels-sample-index/docs/search?api-version=2025-05-01-preview
 {
     "search": "Category:(Resort and Spa) OR Category:Suiite",
     "queryType": "full",
@@ -80,7 +80,7 @@ POST https://[service name].search.windows.net/indexes/hotels-sample-index/docs/
 This query, with typos in every term except one, undergoes spelling corrections to return relevant results. To learn more, see [Configure semantic ranker](semantic-how-to-query-request.md).
 
 ```http
-POST https://[service name].search.windows.net/indexes/hotels-sample-index/docs/search?api-version=2024-05-01-preview    
+POST https://[service name].search.windows.net/indexes/hotels-sample-index/docs/search?api-version=2025-05-01-preview    
 {
     "search": "hisotoric hotell wiht great restrant nad wiifi",
     "queryType": "semantic",
@@ -94,7 +94,7 @@ POST https://[service name].search.windows.net/indexes/hotels-sample-index/docs/
 
 ## Supported languages
 
-Valid values for `queryLanguage` can be found in the following table, copied from the list of [supported languages (REST API reference)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&tabs=HTTP#querylanguage&preserve-view=true).
+Valid values for `queryLanguage` can be found in the following table, copied from the list of [supported languages (REST API reference)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2025-05-01-preview&tabs=HTTP#querylanguage&preserve-view=true).
 
 | Language | queryLanguage |
 |----------|---------------|

@@ -105,13 +105,13 @@ items:
   - name: Multimodal indexing tutorials
     items:
     - name: Use document extraction and multimodal embeddings
-      href: tutorial-multimodal-indexing-with-embedding-and-doc-extraction.md
+      href: tutorial-document-extraction-multimodal-embeddings.md
     - name: Use document extraction and image verbalizations
-      href: tutorial-multimodal-indexing-with-image-verbalization-and-doc-extraction.md
+      href: tutorial-document-extraction-image-verbalization.md
     - name: Use semantic chunking and multimodal embeddings
-      href: tutorial-multimodal-index-embeddings-skill.md
+      href: tutorial-document-layout-multimodal-embeddings.md
     - name: Use semantic chunking and image verbalizations
-      href: tutorial-multimodal-index-image-verbalization-skill.md      
+      href: tutorial-document-layout-image-verbalization.md      
   - name: RAG tutorials
     items:
     - name: Build a RAG solution

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Troubleshoot issues with Shared Private Links in Azure AI Search

@@ -1,5 +1,5 @@
 ---
-title: 'Tutorial: Index multimodal content using image verbalization and Document Extraction skill'
+title: 'Tutorial: Use Image Verbalization and Document Extraction Skill for Multimodal Indexing'
 titleSuffix: Azure AI Search
 description: Learn how to extract, index, and search multimodal content using the Document Extraction skill for chunking and GenAI Prompt skill for image verbalizations.
 
@@ -9,7 +9,7 @@ ms.author: mdonovan
 ms.service: azure-ai-search
 ms.custom:
 ms.topic: tutorial
-ms.date: 05/28/2025
+ms.date: 05/29/2025
 
 ---
 
@@ -31,7 +31,7 @@ In this tutorial, you use:
 
 This tutorial demonstrates a lower-cost approach for indexing multimodal content using Document Extraction skill and image captioning. It enables extraction and search over both text and images from documents in Azure Blob Storage. However, it doesn't include locational metadata for text, such as page numbers or bounding regions.
 
-For a more comprehensive solution that includes structured text layout and spatial metadata, see [Indexing blobs with text and images for multimodal RAG scenarios using image verbalization and Document Layout skill](tutorial-multimodal-index-image-verbalization-skill.md).
+For a more comprehensive solution that includes structured text layout and spatial metadata, see [Indexing blobs with text and images for multimodal RAG scenarios using image verbalization and Document Layout skill](tutorial-document-layout-image-verbalization.md).
 
 > [!NOTE]
 > Setting `imageAction` to `generateNormalizedImages` is required for this tutorial and incurs an additional charge for image extraction according to [Azure AI Search pricing](https://azure.microsoft.com/pricing/details/search/).
@@ -751,4 +751,4 @@ Now that you're familiar with a sample implementation of a multimodal indexing s
 * [GenAI Prompt skill](cognitive-search-skill-genai-prompt.md)
 * [Vectors in Azure AI Search](vector-search-overview.md)
 * [Semantic ranking in Azure AI Search](semantic-search-overview.md)
-* [Indexing blobs with text and images for multimodal RAG scenarios using image verbalization and Document Layout skill](tutorial-multimodal-index-image-verbalization-skill.md)
+* [Indexing blobs with text and images for multimodal RAG scenarios using image verbalization and Document Layout skill](tutorial-document-layout-image-verbalization.md)

@@ -1,5 +1,5 @@
 ---
-title: 'Tutorial: Index multimodal content using embedding and Document Extraction skill'
+title: 'Tutorial: Use Multimodal Embeddings and Document Extraction Skill for Multimodal Indexing'
 titleSuffix: Azure AI Search
 description: Learn how to extract, index, and search multimodal content using the Document Extraction skill for chunking and Azure AI Vision for embeddings.
 
@@ -9,7 +9,7 @@ ms.author: mdonovan
 ms.service: azure-ai-search
 ms.custom:
 ms.topic: tutorial
-ms.date: 05/28/2025
+ms.date: 05/29/2025
 
 ---
 
@@ -29,7 +29,7 @@ In this tutorial, you use:
 
 This tutorial demonstrates a lower-cost approach for indexing multimodal content using Document Extraction skill and image captioning. It enables extraction and search over both text and images from documents in Azure Blob Storage. However, it doesn't include locational metadata for text, such as page numbers or bounding regions.
 
-For a more comprehensive solution that includes structured text layout and spatial metadata, see [Indexing blobs with text and images for multimodal RAG scenarios using image verbalization and Document Layout skill](tutorial-multimodal-index-image-verbalization-skill.md).
+For a more comprehensive solution that includes structured text layout and spatial metadata, see [Indexing blobs with text and images for multimodal RAG scenarios using image verbalization and Document Layout skill](tutorial-document-layout-image-verbalization.md).
 
 > [!NOTE]
 > Setting `imageAction` to `generateNormalizedImages` as is required for this tutorial incurs an additional charge for image extraction according to [Azure AI Search pricing](https://azure.microsoft.com/pricing/details/search/).
@@ -711,4 +711,4 @@ Now that you're familiar with a sample implementation of a multimodal indexing s
 * [AI Vision multimodal embeddings skill](cognitive-search-skill-vision-vectorize.md)
 * [Vectors in Azure AI Search](vector-search-overview.md)
 * [Semantic ranking in Azure AI Search](semantic-search-overview.md)
-* [Indexing blobs with text and images for multimodal RAG scenarios using image verbalization and Document Layout skill](tutorial-multimodal-index-image-verbalization-skill.md)
+* [Indexing blobs with text and images for multimodal RAG scenarios using image verbalization and Document Layout skill](tutorial-document-layout-image-verbalization.md)

@@ -1,5 +1,5 @@
 ---
-title: 'Tutorial: Index multimodal content using image verbalization and Document Layout skill'
+title: 'Tutorial: Use Image Verbalization and Document Layout Skill for Multimodal Indexing'
 titleSuffix: Azure AI Search
 description: Learn how to extract, index, and search multimodal content using the Document Layout skill for chunking and GenAI Prompt skill for image verbalizations.
 
@@ -9,7 +9,7 @@ ms.author: rawan
 ms.service: azure-ai-search
 ms.custom:
 ms.topic: tutorial
-ms.date: 05/28/2025
+ms.date: 05/29/2025
 
 ---
 
@@ -25,7 +25,7 @@ In this tutorial, you use:
 
 + The [Document Layout skill (preview)](cognitive-search-skill-document-intelligence-layout.md) for extracting text and normalized images with its locationMetadata from various documents, such as page numbers or bounding regions.
 
-  The [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md) has limited regional availability, 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. For a lower-cost solution to indexing multimodal content, see [Index multimodal content using image verbalization and Document Extraction skill](tutorial-multimodal-indexing-with-image-verbalization-and-doc-extraction.md).
+  The [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md) has limited regional availability, 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. For a lower-cost solution to indexing multimodal content, see [Index multimodal content using image verbalization and Document Extraction skill](tutorial-document-extraction-image-verbalization.md).
 
 + The [GenAI Prompt skill (preview)](cognitive-search-skill-genai-prompt.md) to generate image captions, which are text-based descriptions of visual content, for search and grounding.
 

@@ -1,5 +1,5 @@
 ---
-title: 'Tutorial: Index multimodal content using multimodal embedding and Document Layout skill'
+title: 'Tutorial: Use Multimodal Embeddings and Document Layout Skill for Multimodal Indexing'
 titleSuffix: Azure AI Search
 description: Learn how to extract, index, and search multimodal content using the Document Layout skill for chunking and Azure AI Vision for embeddings.
 
@@ -9,7 +9,7 @@ ms.author: rawan
 ms.service: azure-ai-search
 ms.custom:
 ms.topic: tutorial
-ms.date: 05/28/2025
+ms.date: 05/29/2025
 
 ---
 
@@ -24,7 +24,7 @@ In this tutorial, you use:
 
 + The [Document Layout skill (preview)](cognitive-search-skill-document-intelligence-layout.md) for extracting text and normalized images with its locationMetadata from various documents, such as page numbers or bounding regions.
 
-  The [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md) has limited regional availability, 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. For a lower-cost solution to indexing multimodal content, see [Index multimodal content using image verbalization and Document Extraction skill](tutorial-multimodal-indexing-with-image-verbalization-and-doc-extraction.md).
+  The [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md) has limited regional availability, 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. For a lower-cost solution to indexing multimodal content, see [Index multimodal content using image verbalization and Document Extraction skill](tutorial-document-extraction-image-verbalization.md).
 
 + Vectorization using the [Azure AI Vision multimodal embeddings skill](cognitive-search-skill-vision-vectorize.md), which generates embeddings for both text and images.
 
@@ -613,4 +613,4 @@ Now that you're familiar with a sample implementation of a multimodal indexing s
 + [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md)
 + [Vectors in Azure AI Search](vector-search-overview.md)
 + [Semantic ranking in Azure AI Search](semantic-search-overview.md)
-+ [Index multimodal content using embeddings and Document Extraction skill](tutorial-multimodal-indexing-with-embedding-and-doc-extraction.md)
++ [Index multimodal content using embeddings and Document Extraction skill](tutorial-document-extraction-multimodal-embeddings.md)

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: tutorial
-ms.date: 12/18/2024
+ms.date: 05/29/2025
 
 ---
 

@@ -8,19 +8,19 @@ ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: tutorial
 ms.custom: references_regions
-ms.date: 12/03/2024
+ms.date: 05/29/2025
 
 ---
 
 # Tutorial: Choose embedding and chat models for RAG in Azure AI Search
 
-A RAG solution built on Azure AI Search takes a dependency on embedding models for vectorization, and on chat models for conversational search over your data.
+A RAG solution built on Azure AI Search takes a dependency on embedding models for vectorization, and on chat completion models for conversational search over your data.
 
 In this tutorial, you:
 
 > [!div class="checklist"]
-> - Learn which models in the Azure cloud work with built-in integration
-> - Learn about the Azure models used for chat
+> - Learn about the Azure models supported for built-in vectorization
+> - Learn about the Azure models supported for chat completion
 > - Deploy models and collect model information for your code
 > - Configure search engine access to Azure models
 > - Learn about custom skills and vectorizers for attaching non-Azure models
@@ -45,12 +45,9 @@ If you don't have an Azure subscription, create a [free account](https://azure.m
 
   - [Azure AI Vision regions](/azure/ai-services/computer-vision/overview-image-analysis?tabs=4-0#region-availability)
 
-  - [Azure AI Foundry](/azure/ai-foundry/reference/region-support) regions. 
+  - [Azure AI Foundry regions](/azure/ai-foundry/reference/region-support)
 
-  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]
-> Check [this article](search-create-service-portal.md#regions-with-the-most-overlap) for a list of overlapping regions.
+  - [Azure AI Search regions](search-region-support.md)
 
 ## Review models supporting built-in vectorization
 

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: overview
-ms.date: 04/14/2024
+ms.date: 05/29/2025
 
 ---
 

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 12/05/2024
+ms.date: 05/29/2025
 ---
 
 # Relevance in vector search
@@ -22,7 +22,7 @@ This article explains the algorithms used to find relevant matches and the simil
 
 Vector search algorithms include exhaustive k-nearest neighbors (KNN) and Hierarchical Navigable Small World (HNSW). 
 
-+ Exhaustive KNN performs a brute-force search that scans the entire vector space.
++ Exhaustive KNN performs a brute-force scan of the entire vector space.
 
 + HNSW performs an [approximate nearest neighbor (ANN)](vector-search-overview.md#approximate-nearest-neighbors) search.