@@ -89,7 +89,7 @@ To set up your request file:
     @token = PUT-YOUR-PERSONAL-IDENTITY-TOKEN-HERE
 
     ### List existing indexes by name
-        GET {{baseUrl}}/indexes?api-version=2024-07-01
+    GET {{baseUrl}}/indexes?api-version=2024-07-01  HTTP/1.1
         Authorization: Bearer {{token}}
     ```
 

@@ -0,0 +1,362 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 06/05/2025
+---
+## Prerequisites
+
+- An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F).
+
+- An [Azure OpenAI resource](/azure/ai-services/openai/how-to/create-resource).
+  - [Choose a region](/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#global-standard-model-availability) that supports the chat completion model you want to use (gpt-4o, gpt-4o-mini, or an equivalent model).
+  - [Deploy the chat completion model](/azure/ai-foundry/how-to/deploy-models-openai) in Azure AI Foundry or [use another approach](/azure/ai-services/openai/how-to/working-with-models).
+- An [Azure AI Search resource](../../search-create-service-portal.md).
+  - We recommend using the Basic tier or higher.
+  - [Enable semantic ranking](../../semantic-how-to-enable-disable.md).
+- [Visual Studio Code](https://code.visualstudio.com/download) or [Visual Studio](https://visualstudio.com).
+- [.NET 9.0](https://dotnet.microsoft.com/download) installed.
+
+## Configure access
+
+Requests to the search endpoint must be authenticated and authorized. You can use API keys or roles for this task. Keys are easier to start with, but roles are more secure. This quickstart assumes roles.
+
+You're setting up two clients, so you need permissions on both resources.
+
+Azure AI Search is receiving the query request from your local system. Assign yourself the **Search Index Data Reader** role assignment if the hotels sample index already exists. If it doesn't exist, assign yourself **Search Service Contributor** and **Search Index Data Contributor** roles so that you can create and query the index.
+
+Azure OpenAI is receiving the query and the search results from your local system. Assign yourself the **Cognitive Services OpenAI User** role on Azure OpenAI.
+
+1. Sign in to the [Azure portal](https://portal.azure.com).
+
+1. Configure Azure AI Search for role-based access:
+
+    1. In the Azure portal, find your Azure AI Search service.
+
+    1. On the left menu, select **Settings** > **Keys**, and then select either **Role-based access control** or **Both**.
+
+1. Assign roles:
+
+    1. On the left menu, select **Access control (IAM)**.
+
+    1. On Azure AI Search, select these roles to create, load, and query a search index, and then assign them to your Microsoft Entra ID user identity:
+
+       - **Search Index Data Contributor**
+       - **Search Service Contributor**
+
+    1. On Azure OpenAI, select **Access control (IAM)** to assign this role to yourself on Azure OpenAI:
+
+       - **Cognitive Services OpenAI User**
+
+It can take several minutes for permissions to take effect.
+
+## Create an index
+
+A search index provides grounding data for the chat model. We recommend the hotels-sample-index, which can be created in minutes and runs on any search service tier. This index is created using built-in sample data.
+
+1. In the Azure portal, [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices).
+
+1. On the **Overview** home page, select [**Import data**](../../search-get-started-portal.md) to start the wizard.
+
+1. On the **Connect to your data** page, select **Samples** from the dropdown list.
+
+1. Choose the **hotels-sample**.
+
+1. Select **Next** through the remaining pages, accepting the default values.
+
+1. Once the index is created, select **Search management** > **Indexes** from the left menu to open the index.
+
+1. Select **Edit JSON**. 
+
+1. Scroll to the end of the index, where you can find placeholders for constructs that can be added to an index.
+
+   ```json
+   "analyzers": [],
+   "tokenizers": [],
+   "tokenFilters": [],
+   "charFilters": [],
+   "normalizers": [],
+   ```
+
+1. On a new line after "normalizers", paste in the following semantic configuration. This example specifies a `"defaultConfiguration"`, which is important to the running of this quickstart.
+
+    ```json
+    "semantic":{
+       "defaultConfiguration":"semantic-config",
+       "configurations":[
+          {
+             "name":"semantic-config",
+             "prioritizedFields":{
+                "titleField":{
+                   "fieldName":"HotelName"
+                },
+                "prioritizedContentFields":[
+                   {
+                      "fieldName":"Description"
+                   }
+                ],
+                "prioritizedKeywordsFields":[
+                   {
+                      "fieldName":"Category"
+                   },
+                   {
+                      "fieldName":"Tags"
+                   }
+                ]
+             }
+          }
+       ]
+    },
+    ```
+
+1. **Save** your changes.
+
+1. Run the following query in [Search Explorer](../../search-explorer.md) to test your index: `complimentary breakfast`.
+
+   Output should look similar to the following example. Results that are returned directly from the search engine consist of fields and their verbatim values, along with metadata like a search score and a semantic ranking score and caption if you use semantic ranker. We used a [select statement](../../search-query-odata-select.md) to return just the HotelName, Description, and Tags fields.
+
+   ```
+   {
+   "@odata.count": 18,
+   "@search.answers": [],
+   "value": [
+      {
+         "@search.score": 2.2896252,
+         "@search.rerankerScore": 2.506816864013672,
+         "@search.captions": [
+         {
+            "text": "Head Wind Resort. Suite. coffee in lobby\r\nfree wifi\r\nview. The best of old town hospitality combined with views of the river and cool breezes off the prairie. Our penthouse suites offer views for miles and the rooftop plaza is open to all guests from sunset to 10 p.m. Enjoy a **complimentary continental breakfast** in the lobby, and free Wi-Fi throughout the hotel..",
+            "highlights": ""
+         }
+         ],
+         "HotelName": "Head Wind Resort",
+         "Description": "The best of old town hospitality combined with views of the river and cool breezes off the prairie. Our penthouse suites offer views for miles and the rooftop plaza is open to all guests from sunset to 10 p.m. Enjoy a complimentary continental breakfast in the lobby, and free Wi-Fi throughout the hotel.",
+         "Tags": [
+         "coffee in lobby",
+         "free wifi",
+         "view"
+         ]
+      },
+      {
+         "@search.score": 2.2158256,
+         "@search.rerankerScore": 2.288334846496582,
+         "@search.captions": [
+         {
+            "text": "Swan Bird Lake Inn. Budget. continental breakfast\r\nfree wifi\r\n24-hour front desk service. We serve a continental-style breakfast each morning, featuring a variety of food and drinks. Our locally made, oh-so-soft, caramel cinnamon rolls are a favorite with our guests. Other breakfast items include coffee, orange juice, milk, cereal, instant oatmeal, bagels, and muffins..",
+            "highlights": ""
+         }
+         ],
+         "HotelName": "Swan Bird Lake Inn",
+         "Description": "We serve a continental-style breakfast each morning, featuring a variety of food and drinks. Our locally made, oh-so-soft, caramel cinnamon rolls are a favorite with our guests. Other breakfast items include coffee, orange juice, milk, cereal, instant oatmeal, bagels, and muffins.",
+         "Tags": [
+         "continental breakfast",
+         "free wifi",
+         "24-hour front desk service"
+         ]
+      },
+      {
+         "@search.score": 0.92481667,
+         "@search.rerankerScore": 2.221315860748291,
+         "@search.captions": [
+         {
+            "text": "White Mountain Lodge & Suites. Resort and Spa. continental breakfast\r\npool\r\nrestaurant. Live amongst the trees in the heart of the forest. Hike along our extensive trail system. Visit the Natural Hot Springs, or enjoy our signature hot stone massage in the Cathedral of Firs. Relax in the meditation gardens, or join new friends around the communal firepit. Weekend evening entertainment on the patio features special guest musicians or poetry readings..",
+            "highlights": ""
+         }
+         ],
+         "HotelName": "White Mountain Lodge & Suites",
+         "Description": "Live amongst the trees in the heart of the forest. Hike along our extensive trail system. Visit the Natural Hot Springs, or enjoy our signature hot stone massage in the Cathedral of Firs. Relax in the meditation gardens, or join new friends around the communal firepit. Weekend evening entertainment on the patio features special guest musicians or poetry readings.",
+         "Tags": [
+         "continental breakfast",
+         "pool",
+         "restaurant"
+         ]
+      },
+      . . .
+   ]}
+   ```
+
+## Get service endpoints
+
+In the remaining sections, you set up API calls to Azure OpenAI and Azure AI Search. Get the service endpoints so that you can provide them as variables in your code.
+
+1. Sign in to the [Azure portal](https://portal.azure.com).
+
+1. [Find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices).
+
+1. On the **Overview** home page, copy the URL. An example endpoint might look like `https://example.search.windows.net`. 
+
+1. [Find your Azure OpenAI service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.CognitiveServices%2Faccounts).
+
+1. On the **Overview** home page, select the link to view the endpoints. Copy the URL. An example endpoint might look like `https://example.openai.azure.com/`.
+
+## Sign in to Azure
+
+You're using Microsoft Entra ID and role assignments for the connection. Make sure you're logged in to the same tenant and subscription as Azure AI Search and Azure OpenAI. You can use the Azure CLI on the command line to show current properties, change properties, and to sign in. For more information, see [Connect without keys](../../search-get-started-rbac.md). 
+
+Run each of the following commands in sequence.
+
+```azure-cli
+az account show
+
+az account set --subscription <PUT YOUR SUBSCRIPTION ID HERE>
+
+az login --tenant <PUT YOUR TENANT ID HERE>
+```
+
+You should now be logged in to Azure from your local device.
+
+## Set up the .NET app
+
+To follow along with the steps ahead, you can either clone the completed sample app from GitHub, or create the app yourself.
+
+### Clone the sample app
+
+To access the completed sample app for this article: 
+
+1. Clone the [azure-search-dotnet-samples](https://github.com/Azure-Samples/azure-search-dotnet-samples) repo from GitHub.
+
+    ```bash
+    git clone https://github.com/Azure-Samples/azure-search-dotnet-samples
+    ```
+
+1. Navigate into the `quickstart-rag` folder.
+1. Open the `quickstart-rag` folder in Visual Studio Code or open the solution file using Visual Studio.
+
+### Create the sample app
+
+Complete the following steps to create a .NET console app to connect to an AI model.
+
+1. In an empty directory on your computer, use the `dotnet new` command to create a new console app:
+
+    ```dotnetcli
+    dotnet new console -o AISearchRag
+    ```
+
+1. Change directory into the app folder:
+
+    ```dotnetcli
+    cd AISearchRag
+    ```
+
+1. Install the required packages:
+
+    ```bash
+    dotnet add package Azure.AI.OpenAI
+    dotnet add package Azure.Identity
+    dotnet add package Azure.Search.Documents
+    ```
+
+1. Open the app in Visual Studio Code (or your editor of choice).
+
+    ```bash
+    code .
+    ```
+
+## Set up the query and chat thread
+
+The following example demonstrates how to set up a minimal RAG scenario using Azure AI Search to provide an OpenAI model with contextual resources to improve the generated responses.
+
+1. In the `minimal-query` project of the sample repo, open the `Program.cs` file to view the first example. If you created the project yourself, add the following code to connect to and query the Azure AI Search and Azure OpenAI services.
+
+    > [!NOTE]
+    > Make sure to replace the placeholders for the Azure OpenAI endpoint and model name, as well as the Azure AI Search endpoint and index name.
+    
+    :::code language="csharp" source="~/azure-search-dotnet-samples/quickstart-rag/minimal-query/Program.cs" :::
+    
+    The preceding code accomplishes the following:
+    
+    - Searches an Azure Search index for hotels matching a user query about complimentary breakfast, retrieving hotel name, description, and tags.
+    - Formats the search results into a structured list to serve as contextual sources for the generative AI model.
+    - Constructs a prompt instructing the Azure OpenAI model to answer using only the provided sources.
+    - Sends the prompt to the AI model and streams the generated response.
+    - Outputs the AI’s response to the console, displaying both the role and content as it streams.
+
+1. Run the project to initiate a basic RAG scenario. The output from Azure OpenAI consists of recommendations for several hotels, such as the following example:
+    
+    ```output
+    Sure! Here are a few hotels that offer complimentary breakfast:
+    
+    - **Head Wind Resort**
+    - Complimentary continental breakfast in the lobby
+    - Free Wi-Fi throughout the hotel
+    
+    - **Double Sanctuary Resort**
+    - Continental breakfast included
+    
+    - **White Mountain Lodge & Suites**
+    - Continental breakfast available
+    
+    - **Swan Bird Lake Inn**
+    - Continental-style breakfast each morning with a variety of food and drinks 
+       such as caramel cinnamon rolls, coffee, orange juice, milk, cereal, 
+       instant oatmeal, bagels, and muffins
+    ```
+
+To experiment further, change the query and rerun the last step to better understand how the model works with the grounding data. You can also modify the prompt to change the tone or structure of the output.
+
+### Troubleshooting
+
+You might receive any of the following errors while testing:
+
+- **Forbidden**: Check Azure AI Search configuration to make sure role-based access is enabled.
+- **Authorization failed**: Wait a few minutes and try again. It can take several minutes for role assignments to become operational.
+- **Resource not found**: Check the resource URIs and make sure the API version on the chat model is valid.
+
+## Send a complex RAG query
+
+Azure AI Search supports [complex types](../../search-howto-complex-data-types.md) for nested JSON structures. In the hotels-sample-index, `Address` is an example of a complex type, consisting of `Address.StreetAddress`, `Address.City`, `Address.StateProvince`, `Address.PostalCode`, and `Address.Country`. The index also has complex collection of `Rooms` for each hotel. If your index has complex types, your query can provide those fields if you first convert the search results output to JSON, and then pass the JSON to the chat model.
+
+1. In the `complex-query` project of the sample repo, open the `Program.cs` file. If you created the project yourself, replace your code with the following:
+
+    :::code language="csharp" source="~/azure-search-dotnet-samples/quickstart-rag/complex-query/Program.cs" :::
+
+2. Run the project to initiate a basic RAG scenario. The output from Azure OpenAI consists of recommendations for several hotels, such as the following example:
+
+    ```output
+    1. **Double Sanctuary Resort**
+       - **Description**: 5-star luxury hotel with the biggest rooms in the city. Recognized as the #1 hotel in the area by Traveler magazine. Features include free WiFi, flexible check-in/out, a fitness center, and in-room espresso.
+       - **Address**: 2211 Elliott Ave, Seattle, WA, 98121, USA
+       - **Tags**: view, pool, restaurant, bar, continental breakfast
+       - **Room Rate for 4 People**: 
+         - Suite, 2 Queen Beds: $254.99 per night
+    
+    2. **Starlight Suites**
+       - **Description**: Spacious all-suite hotel with complimentary airport shuttle and WiFi. Facilities include an indoor/outdoor pool, fitness center, and Florida Green certification. Complimentary coffee and HDTV are also available.
+       - **Address**: 19575 Biscayne Blvd, Aventura, FL, 33180, USA
+       - **Tags**: pool, coffee in lobby, free wifi
+       - **Room Rate for 4 People**:
+         - Suite, 2 Queen Beds (Cityside): $231.99 per night
+         - Deluxe Room, 2 Queen Beds (Waterfront View): $148.99 per night
+    
+    3. **Good Business Hotel**
+       - **Description**: Located one mile from the airport with free WiFi, an outdoor pool, and a complimentary airport shuttle. Close proximity to Lake Lanier and downtown. The business center includes printers, a copy machine, fax, and a work area.
+       - **Address**: 4400 Ashford Dunwoody Rd NE, Atlanta, GA, 30346, USA
+       - **Tags**: pool, continental breakfast, free parking
+       - **Room Rate for 4 People**:
+         - Budget Room, 2 Queen Beds (Amenities): $60.99 per night
+         - Deluxe Room, 2 Queen Beds (Amenities): $139.99 per night
+    ```
+
+## Troubleshooting
+
+If you see output messages while debugging related to `ManagedIdentityCredential` and token acquisition failures, it could be that you have multiple tenants, and your Azure sign-in is using a tenant that doesn't have your search service. To get your tenant ID, search the Azure portal for "tenant properties" or run `az login tenant list`.
+
+Once you have your tenant ID, run `az login --tenant <YOUR-TENANT-ID>` at a command prompt, and then rerun the script.
+
+You can also log errors in your code by creating an instance of `ILogger`:
+
+```csharp
+using var loggerFactory = LoggerFactory.Create(builder =>
+{
+   builder.AddConsole();
+});
+ILogger logger = loggerFactory.CreateLogger<Program>();
+```
+
+## Clean up
+
+When you're working in your own subscription, it's a good idea at the end of a project to identify whether you still need the resources you created. Resources left running can cost you money. You can delete resources individually or delete the resource group to delete the entire set of resources.
+
+You can find and manage resources in the Azure portal by using the **All resources** or **Resource groups** link in the leftmost pane.

@@ -18,7 +18,6 @@ ms.date: 06/05/2025
   - [Enable semantic ranking](../../semantic-how-to-enable-disable.md).
 - [Visual Studio Code](https://code.visualstudio.com/download) with the [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) and the [Jupyter package](https://pypi.org/project/jupyter/). For more information, see [Python in Visual Studio Code](https://code.visualstudio.com/docs/languages/python).
 
-
 ## Download file
 
 [Download a Jupyter notebook](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-RAG) from GitHub to send the requests in this quickstart. For more information, see [Downloading files from GitHub](https://docs.github.com/get-started/start-your-journey/downloading-files-from-github).
@@ -58,7 +57,7 @@ Azure OpenAI is receiving the query and the search results from your local syste
 
 It can take several minutes for permissions to take effect.
 
-## Create an index
+## Update the hotels-sample-index
 
 A search index provides grounding data for the chat model. We recommend the hotels-sample-index, which can be created in minutes and runs on any search service tier. This index is created using built-in sample data.
 

@@ -0,0 +1,266 @@
+---
+manager: nitinme
+author: heidisteen
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 07/08/2025
+---
+
+## Prerequisites
+
+- An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F).
+
+- An [Azure OpenAI resource](/azure/ai-services/openai/how-to/create-resource).
+  - [Choose a region](/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#global-standard-model-availability) that supports the chat completion model you want to use (gpt-4o, gpt-4o-mini, or an equivalent model).
+  - [Deploy the chat completion model](/azure/ai-foundry/how-to/deploy-models-openai) in Azure AI Foundry or [use another approach](/azure/ai-services/openai/how-to/working-with-models).
+- An [Azure AI Search resource](../../search-create-service-portal.md).
+  - We recommend using the Basic tier or higher.
+  - [Enable semantic ranking](../../semantic-how-to-enable-disable.md).
+
+- A [new or existing index](../../search-how-to-create-search-index.md) with descriptive or verbose text fields, attributed as retrievable in your index. This quickstart assumes the [hotels-sample-index](../../search-get-started-portal.md).
+
+- The [Azure CLI](/cli/azure/install-azure-cli) for keyless authentication with Microsoft Entra ID.
+
+- [Visual Studio Code](https://code.visualstudio.com/download) with the [REST client extension](https://marketplace.visualstudio.com/items?itemName=humao.rest-client) .
+
+## Download file
+
+[Download a .rest file](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-RAG) from GitHub to send the requests in this quickstart. For more information, see [Downloading files from GitHub](https://docs.github.com/get-started/start-your-journey/downloading-files-from-github).
+
+You can also start a new file on your local system and create requests manually by using the instructions in this article.
+
+## Configure access
+
+Requests to the search endpoint must be authenticated and authorized. You can use [API keys](../../search-security-api-keys.md) or roles for this task. Keys are easier to start with, but roles are more secure. This quickstart assumes roles.
+
+You're setting up two clients, so you need permissions on both resources.
+
+Azure AI Search is receiving the query request from your local system. Assign yourself the **Search Index Data Reader** role assignment if the hotels sample index already exists. If it doesn't exist, assign yourself **Search Service Contributor** and **Search Index Data Contributor** roles so that you can create and query the index.
+
+Azure OpenAI is receiving the query and the search results from your local system. Assign yourself the **Cognitive Services OpenAI User** role on Azure OpenAI.
+
+1. Sign in to the [Azure portal](https://portal.azure.com).
+
+1. Configure Azure AI Search for role-based access:
+
+    1. In the Azure portal, find your Azure AI Search service.
+
+    1. On the left menu, select **Settings** > **Keys**, and then select either **Role-based access control** or **Both**.
+
+1. Assign roles:
+
+    1. On the left menu, select **Access control (IAM)**.
+
+    1. On Azure AI Search, select these roles to create, load, and query a search index, and then assign them to your Microsoft Entra ID user identity:
+
+       - **Search Index Data Contributor**
+       - **Search Service Contributor**
+
+    1. On Azure OpenAI, select **Access control (IAM)** to assign this role to yourself on Azure OpenAI:
+
+       - **Cognitive Services OpenAI User**
+
+It can take several minutes for permissions to take effect.
+
+## Get service endpoints and tokens
+
+In the remaining sections, you set up API calls to Azure OpenAI and Azure AI Search. Get the service endpoints and tokens so that you can provide them as variables in your code.
+
+1. Sign in to the [Azure portal](https://portal.azure.com).
+
+1. [Find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices).
+
+1. On the **Overview** home page, copy the URL. An example endpoint might look like `https://example.search.windows.net`. 
+
+1. [Find your Azure OpenAI service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.CognitiveServices%2Faccounts).
+
+1. On the **Overview** home page, select the link to view the endpoints. Copy the URL. An example endpoint might look like `https://example.openai.azure.com/`.
+
+1. Get personal access tokens from the Azure CLI on a command prompt. Here are the commands for each resource:
+
+   - `az account get-access-token --resource https://search.azure.com --query "accessToken" -o tsv`
+   - `az account get-access-token --resource https://cognitiveservices.azure.com --query "accessToken" -o tsv`
+
+## Set up the client
+
+In this quickstart, you use a REST client and the [Azure AI Search REST APIs](/rest/api/searchservice) to implement the RAG pattern.
+
+We recommend [Visual Studio Code](https://code.visualstudio.com/download) with a [REST client extension](https://marketplace.visualstudio.com/items?itemName=humao.rest-client) for this quickstart.
+
+> [!TIP]
+> You can [download the source code](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-rag) to start with a finished project or follow these steps to create your own. 
+
+1. Start Visual Studio Code and open the [quickstart-rag.rest](https://github.com/Azure-Samples/azure-search-rest-samples/blob/main/Quickstart-rag/quickstart-rag.rest) file or create a new file.
+
+1. At the top, set environment variables for your search service, authorization, and index name.
+
+   - For @searchUrl, paste in the search endpoint.
+   - For @aoaiUrl, paste in the Azure OpenAI endpoint.
+   - For @searchAccessToken, paste in the access token scoped to `https://search.azure.com`.
+   - For @aoaiAccessToken, paste in the access token scoped to `https://cognitiveservices.azure.com`.
+
+1. To test the connection, send your first request.
+
+   ```http
+   ### List existing indexes by name (verify the connection)
+    GET  {{searchUrl}}/indexes?api-version=2025-05-01-preview&$select=name  HTTP/1.1
+    Authorization: Bearer {{personalAccessToken}}
+   ```
+
+1. Select **Sent request**.
+
+   :::image type="content" source="../../media/search-get-started-semantic/visual-studio-code-send-request.png" alt-text="Screenshot of the REST client send request link.":::
+
+1. Output for this GET request should be a list of indexes. You should see the **hotels-sample-index** among them.
+
+## Set up the query and chat thread
+
+This section uses Visual Studio Code and REST to call the chat completion APIs on Azure OpenAI.
+
+1. Set up a query request on the phrase *"Can you recommend a few hotels with complimentary breakfast?"*. This query uses semantic ranking to return relevant matches, even if the verbatim text isn't an exact match. Results are held in the **searchRequest** variable for reuse on the next request.
+
+   ```http
+   # @name searchRequest
+    POST {{searchUrl}}/indexes/{{index-name}}/docs/search?api-version={{api-version}} HTTP/1.1
+    Content-Type: application/json
+    Authorization: Bearer {{searchAccessToken}}
+    
+    {
+      "search": "Can you recommend a few hotels with complimentary breakfast?",
+      "queryType": "semantic",
+      "semanticConfiguration": "semantic-config",
+      "select": "Description,HotelName,Tags",
+      "top": 5
+    }
+    
+    ### 3 - Use search results in Azure OpenAI call to a chat completion model
+    POST {{aoaiUrl}}/openai/deployments/{{aoaiGptDeployment}}/chat/completions?api-version=2024-08-01-preview HTTP/1.1
+    Content-Type: application/json
+    Authorization: Bearer {{aoaiAccessToken}}
+    
+    {
+      "messages": [
+        {
+          "role": "system", 
+          "content": "You recommend hotels based on activities and amenities. Answer the query using only the search result. Answer in a friendly and concise manner. Answer ONLY with the facts provided. If there isn't enough information below, say you don't know."
+        },
+        {
+          "role": "user",
+          "content": "Based on the hotel search results, can you recommend hotels with breakfast? Here are all the hotels I found:\n\nHotel 1: {{searchRequest.response.body.value[0].HotelName}}\nDescription: {{searchRequest.response.body.value[0].Description}}\n\nHotel 2: {{searchRequest.response.body.value[1].HotelName}}\nDescription: {{searchRequest.response.body.value[1].Description}}\n\nHotel 3: {{searchRequest.response.body.value[2].HotelName}}\nDescription: {{searchRequest.response.body.value[2].Description}}\n\nHotel 4: {{searchRequest.response.body.value[3].HotelName}}\nDescription: {{searchRequest.response.body.value[3].Description}}\n\nHotel 5: {{searchRequest.response.body.value[4].HotelName}}\nDescription: {{searchRequest.response.body.value[4].Description}}\n\nPlease recommend which hotels offer breakfast based on their descriptions."
+        }
+      ],
+      "max_tokens": 1000,
+      "temperature": 0.7
+    }`
+    ```
+
+1. **Send** the request.
+
+1. Output should look similar to the following example:
+
+   ```json
+      "value": [
+        {
+          "@search.score": 3.9269178,
+          "@search.rerankerScore": 2.380699872970581,
+          "HotelName": "Head Wind Resort",
+          "Description": "The best of old town hospitality combined with views of the river and cool breezes off the prairie. Our penthouse suites offer views for miles and the rooftop plaza is open to all guests from sunset to 10 p.m. Enjoy a complimentary continental breakfast in the lobby, and free Wi-Fi throughout the hotel.",
+          "Tags": [
+            "coffee in lobby",
+            "free wifi",
+            "view"
+          ]
+        },
+        {
+          "@search.score": 1.5450059,
+          "@search.rerankerScore": 2.1258809566497803,
+          "HotelName": "Thunderbird Motel",
+          "Description": "Book Now & Save. Clean, Comfortable rooms at the lowest price. Enjoy complimentary coffee and tea in common areas.",
+          "Tags": [
+            "coffee in lobby",
+            "free parking",
+            "free wifi"
+          ]
+        },
+        {
+          "@search.score": 2.2158256,
+          "@search.rerankerScore": 2.121671438217163,
+          "HotelName": "Swan Bird Lake Inn",
+          "Description": "We serve a continental-style breakfast each morning, featuring a variety of food and drinks. Our locally made, oh-so-soft, caramel cinnamon rolls are a favorite with our guests. Other breakfast items include coffee, orange juice, milk, cereal, instant oatmeal, bagels, and muffins.",
+          "Tags": [
+            "continental breakfast",
+            "free wifi",
+            "24-hour front desk service"
+          ]
+        },
+        {
+          "@search.score": 0.6395861,
+          "@search.rerankerScore": 2.116753339767456,
+          "HotelName": "Waterfront Scottish Inn",
+          "Description": "Newly Redesigned Rooms & airport shuttle. Minutes from the airport, enjoy lakeside amenities, a resort-style pool & stylish new guestrooms with Internet TVs.",
+          "Tags": [
+            "24-hour front desk service",
+            "continental breakfast",
+            "free wifi"
+          ]
+        },
+        {
+          "@search.score": 4.885111,
+          "@search.rerankerScore": 2.0008862018585205,
+          "HotelName": "Double Sanctuary Resort",
+          "Description": "5 star Luxury Hotel - Biggest Rooms in the city. #1 Hotel in the area listed by Traveler magazine. Free WiFi, Flexible check in/out, Fitness Center & espresso in room.",
+          "Tags": [
+            "view",
+            "pool",
+            "restaurant",
+            "bar",
+            "continental breakfast"
+          ]
+        }
+      ]
+    ```
+
+1. Set up a conversation turn with a chat completion model. This request includes a prompt that provides instructions for the response. The `max_tokens` value is large enough to accommodate the search results from the previous query.
+
+   ```http
+    POST {{aoaiUrl}}/openai/deployments/{{aoaiGptDeployment}}/chat/completions?api-version=2024-08-01-preview HTTP/1.1
+    Content-Type: application/json
+    Authorization: Bearer {{aoaiAccessToken}}
+    
+    {
+    "messages": [
+    {
+      "role": "system", 
+      "content": "You  are a friendly assistant that recommends hotels based on activities and amenities. Answer the query using only the search result. Answer in a friendly and concise manner. Answer ONLY with the facts provided. If there isn't enough information below, say you don't know."
+        },
+    {
+      "role": "user",
+      "content": "Based on the hotel search results, can you recommend hotels with breakfast? Here are all the hotels I found:\n\nHotel 1: {{searchRequest.response.body.value[0].HotelName}}\nDescription: {{searchRequest.response.body.value[0].Description}}\n\nHotel 2: {{searchRequest.response.body.value[1].HotelName}}\nDescription: {{searchRequest.response.body.value[1].Description}}\n\nHotel 3: {{searchRequest.response.body.value[2].HotelName}}\nDescription: {{searchRequest.response.body.value[2].Description}}\n\nHotel 4: {{searchRequest.response.body.value[3].HotelName}}\nDescription: {{searchRequest.response.body.value[3].Description}}\n\nHotel 5: {{searchRequest.response.body.value[4].HotelName}}\nDescription: {{searchRequest.response.body.value[4].Description}}\n\nPlease recommend which hotels offer breakfast based on their descriptions."
+    }
+    ],
+    "max_tokens": 1000,
+    "temperature": 0.7
+    }
+    ```
+
+1. **Send** the request.
+
+1. Output should be an HTTP 200 Success status message. Included in the output is content that answers the question:
+
+   ```json
+    "message": {
+      "annotations": [],
+      "content": "I recommend the following hotels that offer breakfast:\n\n1. **Head Wind Resort** - Offers a complimentary continental breakfast in the lobby.\n2. **Swan Bird Lake Inn** - Serves a continental-style breakfast each morning, including a variety of food and drinks. \n\nEnjoy your stay!",
+      "refusal": null,
+      "role": "assistant"
+    }
+    ```
+
+Notice that the output is missing several hotels that mention breakfast in the Tags field. The Tags field is an array, and including this field breaks the JSON structure in the results. Because there are no string conversion capabilities in the REST client, extra code for manually converting the JSON to a string is required if arrays are to be included. We omit this step for this quickstart.
+
+## Clean up
+
+When you're working in your own subscription, it's a good idea at the end of a project to identify whether you still need the resources you created. Resources left running can cost you money. You can delete resources individually or delete the resource group to delete the entire set of resources.
+
+You can find and manage resources in the Azure portal by using the **All resources** or **Resource groups** link in the leftmost pane.

@@ -10,6 +10,8 @@ ms.date: 06/05/2025
 
 - An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F).
 
+- An [Azure AI Search service](../../search-create-service-portal.md), any tier and region.
+
 - An [Azure OpenAI resource](/azure/ai-services/openai/how-to/create-resource).
   - [Choose a region](/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#global-standard-model-availability) that supports the chat completion model you want to use (gpt-4o, gpt-4o-mini, or an equivalent model).
   - [Deploy the chat completion model](/azure/ai-foundry/how-to/deploy-models-openai) in Azure AI Foundry or [use another approach](/azure/ai-services/openai/how-to/working-with-models).

@@ -0,0 +1,95 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 07/09/2025
+---
+
+In this quickstart, you use role-based access control (RBAC) and Microsoft Entra ID to establish a keyless connection to your Azure AI Search service. You then use Python in Visual Studio Code to interact with your service.
+
+Keyless connections provide enhanced security through granular permissions and identity-based authentication. We don't recommend hard-coded API keys, but if you prefer them, see [Connect to Azure AI Search using keys](../../search-security-api-keys.md).
+
+<!-- This quickstart is a prerequisite for other quickstarts that use Microsoft Entra ID with role assignments. -->
+
+## Prerequisites
+
++ An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F).
+
++ An [Azure AI Search service](../../search-create-service-portal.md) in any region or tier.
+
++ The [Azure CLI](/cli/azure/install-azure-cli) for keyless authentication with Microsoft Entra ID.
+
++ [Visual Studio Code](https://code.visualstudio.com/) with the [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) and [Jupyter package](https://jupyter.org/install).
+
+[!INCLUDE [Setup](./search-get-started-rbac-setup.md)]
+
+## Sign in to Azure
+
+Before you connect to your Azure AI Search service, use the Azure CLI to sign in to the subscription that contains your service. This step establishes your Microsoft Entra identity, which `DefaultAzureCredential` uses to authenticate requests in the next section.
+
+To sign in:
+
+1. On your local system, open a command-line tool.
+
+1. Sign in to Azure. If you have multiple subscriptions, select the one whose ID you obtained in [Get service information](#get-service-information).
+
+   ```azurecli
+   az login
+   ```
+
+## Connect to Azure AI Search
+
+> [!NOTE]
+> This section illustrates the basic Python pattern for keyless connections. For comprehensive guidance, see a specific quickstart or tutorial, such as [Quickstart: Run agentic retrieval in Azure AI Search](../../search-get-started-agentic-retrieval.md).
+
+You can use Python notebooks in Visual Studio Code to send requests to your Azure AI Search service. For request authentication, use the `DefaultAzureCredential` class from the Azure Identity library.
+
+To connect using Python:
+
+1. On your local system, open Visual Studio Code.
+
+1. Create a `.ipynb` file.
+
+1. Create a code cell to install the `azure-identity` and `azure-search-documents` libraries.
+
+   ```python
+   pip install azure-identity azure-search-documents
+   ```
+
+1. Create another code cell to authenticate and connect to your search service.
+
+   ```python
+   from azure.identity import DefaultAzureCredential
+   from azure.search.documents.indexes import SearchIndexClient
+    
+   service_endpoint = "PUT-YOUR-SEARCH-SERVICE-ENDPOINT-HERE"
+   credential = DefaultAzureCredential()
+   client = SearchIndexClient(endpoint=service_endpoint, credential=credential)
+    
+   # List existing indexes
+   indexes = client.list_indexes()
+    
+   for index in indexes:
+      index_dict = index.as_dict()
+      print(json.dumps(index_dict, indent=2))
+   ```
+
+1. Set `service_endpoint` to the value you obtained in [Get service information](#get-service-information).
+
+1. Select **Run All** to run both code cells.
+
+   The output should list the existing indexes (if any) on your search service, indicating a successful connection.
+
+### Troubleshoot 401 errors
+
+If you encounter a 401 error, follow these troubleshooting steps:
+
++ Revisit [Configure role-based access](#configure-role-based-access). Your search service must have **Role-based access control** or **Both** enabled. Policies at the subscription or resource group level might also override your role assignments.
+
++ Revisit [Sign in to Azure](#sign-in-to-azure). You must sign in to the subscription that contains your search service.
+
++ Make sure your endpoint variable has surrounding quotes.
+
++ If all else fails, restart your device to remove cached tokens and then repeat the steps in this quickstart, starting with [Sign in to Azure](#sign-in-to-azure).

@@ -0,0 +1,95 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 07/09/2025
+---
+
+In this quickstart, you use role-based access control (RBAC) and Microsoft Entra ID to establish a keyless connection to your Azure AI Search service. You then use REST in Visual Studio Code to interact with your service.
+
+Keyless connections provide enhanced security through granular permissions and identity-based authentication. We don't recommend hard-coded API keys, but if you prefer them, see [Connect to Azure AI Search using keys](../../search-security-api-keys.md).
+
+<!-- This quickstart is a prerequisite for other quickstarts that use Microsoft Entra ID with role assignments. -->
+
+## Prerequisites
+
++ An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F).
+
++ An [Azure AI Search service](../../search-create-service-portal.md) in any region or tier.
+
++ The [Azure CLI](/cli/azure/install-azure-cli) for keyless authentication with Microsoft Entra ID.
+
++ [Visual Studio Code](https://code.visualstudio.com/) with the [REST Client extension](https://marketplace.visualstudio.com/items?itemName=humao.rest-client).
+
+[!INCLUDE [Setup](./search-get-started-rbac-setup.md)]
+
+## Get token
+
+Before you connect to your Azure AI Search service, use the Azure CLI to sign in to the subscription that contains your service and generate a Microsoft Entra ID token. You use this token to authenticate requests in the next section.
+
+To get your token:
+
+1. On your local system, open a command-line tool.
+
+1. Sign in to Azure. If you have multiple subscriptions, select the one whose ID you obtained in [Get service information](#get-service-information).
+
+   ```azurecli
+   az login
+   ```
+
+1. Generate an access token.
+
+   ```azurecli
+   az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv
+   ```
+
+1. Make a note of the token output.
+
+## Connect to Azure AI Search
+
+> [!NOTE]
+> This section illustrates the basic REST pattern for keyless connections. For comprehensive guidance, see a specific quickstart or tutorial, such as [Quickstart: Run agentic retrieval in Azure AI Search](../../search-get-started-agentic-retrieval.md).
+
+You can use the REST Client extension in Visual Studio Code to send requests to your Azure AI Search service. For request authentication, include an `Authorization` header with the Microsoft Entra ID token you previously generated.
+
+To connect using REST:
+
+1. On your local system, open Visual Studio Code.
+
+1. Create a `.rest` or `.http` file.
+
+1. Paste the following placeholders and request into the file.
+
+   ```http
+   @baseUrl = PUT-YOUR-SEARCH-SERVICE-ENDPOINT-HERE
+   @token = PUT-YOUR-PERSONAL-IDENTITY-TOKEN-HERE
+
+   ### List existing indexes
+   GET {{baseUrl}}/indexes?api-version=2024-07-01  HTTP/1.1
+      Content-Type: application/json
+      Authorization: Bearer {{token}}
+   ```
+
+1. Replace `@baseUrl` with the value you obtained in [Get service information](#get-service-information).
+
+1. Replace `@token` with the value you obtained in [Get token](#get-token).
+
+1. Under `### List existing indexes`, select **Send Request**.
+
+   You should receive an `HTTP/1.1 200 OK` response, indicating a successful connection to your search service.
+
+### Troubleshoot 401 errors
+
+If you encounter a 401 error, follow these troubleshooting steps:
+
++ Revisit [Configure role-based access](#configure-role-based-access). Your search service must have **Role-based access control** or **Both** enabled. Policies at the subscription or resource group level might also override your role assignments.
+
++ Revisit [Get token](#get-token). You must sign in to the subscription that contains your search service.
+
++ Make sure your endpoint and token variables don't have surrounding quotes or extra spaces.
+
++ Make sure your token doesn't have the `@` symbol in the request header. For example, if the variable is `@token`, the reference in the request should be `{{token}}`.
+
++ If all else fails, restart your device to remove cached tokens and then repeat the steps in this quickstart, starting with [Get token](#get-token).

@@ -0,0 +1,46 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 07/08/2025
+---
+
+## Configure role-based access
+
+In this section, you enable RBAC on your Azure AI Search service and assign the necessary roles for creating, loading, and querying search objects. For more information about these steps, see [Connect to Azure AI Search using roles](../../search-security-rbac.md).
+
+To configure access:
+
+1. Sign in to the [Azure portal](https://portal.azure.com) and select your search service.
+
+1. From the left pane, select **Settings > Keys**.
+
+1. Select **Role-based access control** or **Both** if you need time to transition clients to RBAC.
+
+   :::image type="content" source="../../media/search-get-started-rbac/access-control-options.png" lightbox="../../media/search-get-started-rbac/access-control-options.png" alt-text="Screenshot of the access control options in the Azure portal.":::
+
+1. From the left pane, select **Access control (IAM)**.
+
+1. Select **Add** > **Add role assignment**.
+
+   :::image type="content" source="../../media/search-get-started-rbac/add-role-assignment.png" lightbox="../../media/search-get-started-rbac/add-role-assignment.png" alt-text="Screenshot of the dropdown menu for adding a role assignment in the Azure portal.":::
+
+1. Assign the **Search Service Contributor** role to your user account or managed identity.
+
+1. Repeat the role assignment for **Search Index Data Contributor**.
+
+## Get service information
+
+In this section, you retrieve the subscription ID and endpoint of your Azure AI Search service. If you only have one subscription, skip the subscription ID and only retrieve the endpoint. You use these values in the remaining sections of this quickstart.
+
+To get your service information:
+
+1. Sign in to the [Azure portal](https://portal.azure.com) and select your search service.
+
+1. From the left pane, select **Overview**.
+
+1. Make a note of the subscription ID and endpoint.
+
+   :::image type="content" source="../../media/search-get-started-rbac/subscription-and-endpoint.png" lightbox="../../media/search-get-started-rbac/subscription-and-endpoint.png" alt-text="Screenshot of the subscription ID and endpoint in the Azure portal.":::

@@ -388,7 +388,7 @@ Key takeaways about the [Create Index](/rest/api/searchservice/indexes/create) R
 
 - Vector fields must be one of the [EDM data types used for vectors](/rest/api/searchservice/supported-data-types#edm-data-types-for-vector-fields), such as `type: Collection(Edm.Single)`. Vector fields also have `dimensions` and `vectorSearchProfile` properties.
 
-- The `vectorSearch` section is an array of approximate nearest neighbor algorithm configurations and profiles. Supported algorithms include hierarchical navigable small world and exhaustive k-nearest neighbor. For more information, see [Relevance scoring in vector search](../../vector-search-ranking.md).
+- The `vectorSearch` section is an array of Approximate Nearest Neighbor (ANN) algorithm configurations and profiles. Supported algorithms include Hierarchical Navigable Small World and exhaustive K-Nearest Neighbor. For more information, see [Relevance scoring in vector search](../../vector-search-ranking.md).
 
 - The (optional) `semantic` configuration enables reranking of search results. You can rerank results in queries of type `semantic` for string fields that are specified in the configuration. To learn more, see [Semantic ranking overview](../../semantic-search-overview.md).
 
@@ -669,7 +669,7 @@ The vector query string is semantically similar to the search string, but it inc
 
 ### Single vector search
 
-1. Formulate the request. The query is a 1536 float representation of *quintessential lodging near running trails, eateries, retail*. The query is searching `DescriptionVector` and returning k-5 results. It's using the "exhaustive" override parameter to perform a full scan of the index instead of approximate nearest neighbor. An exhaustive search is useful for small indexes.
+1. Formulate the request. The query is a 1536 float representation of *quintessential lodging near running trails, eateries, retail*. The query is searching `DescriptionVector` and returning k-5 results. It's using the "exhaustive" override parameter to perform a full scan of the index instead of ANN. An exhaustive search is useful for small indexes.
 
     ```http
     ### Run a single vector query

@@ -1,6 +1,6 @@
 ---
 manager: nitinme
-author: haileytapia
+author: heidisteen
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: include

@@ -4,6 +4,7 @@ description: Use keyless connections with an Azure Identity library for Microsof
 ms.topic: how-to
 ms.date: 06/17/2025
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 author: HeidiSteen
 ms.author: heidist
 ms.custom: devx-track-dotnet, devx-track-extended-java, devx-track-js, devx-track-python, Keyless-dotnet, Keyless-java, Keyless-js, Keyless-python, build-2024-intelligent-apps

@@ -3,6 +3,7 @@ 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.update-cycle: 90-days
 ms.topic: conceptual
 ms.date: 05/29/2025
 author: gmndrg

@@ -7,6 +7,7 @@ ms.author: heidist
 manager: nitinme
 ms.date: 04/15/2025
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.topic: conceptual
 ms.custom:
   - ignite-2023

@@ -6,6 +6,7 @@ manager: nitinme
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.custom:
   - devx-track-dotnet
   - ignite-2023

@@ -6,6 +6,7 @@ manager: nitinme
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.custom:
   - devx-track-dotnet
   - devx-track-extended-java

@@ -6,6 +6,7 @@ manager: nitinme
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.custom:
   - devx-track-dotnet
   - devx-track-js

@@ -6,6 +6,7 @@ manager: nitinme
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.custom:
   - devx-track-dotnet
   - devx-track-python

@@ -6,6 +6,7 @@ manager: nitinme
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.custom:
   - ignite-2023
 ms.topic: concept-article

@@ -7,6 +7,7 @@ ms.author: heidist
 manager: nitinme
 ms.date: 06/08/2025
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.topic: concept-article
 ms.custom:
   - references_regions

@@ -7,6 +7,7 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.topic: how-to
 ms.date: 05/30/2025
 ---

@@ -7,6 +7,7 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.topic: how-to
 ms.date: 05/05/2025
 ---

@@ -7,6 +7,7 @@ ms.author: heidist
 manager: nitinme
 ms.date: 06/08/2025
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.topic: how-to
 ms.custom:
   - build-2025

@@ -7,6 +7,7 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.topic: how-to
 ms.date: 06/08/2025
 ---

@@ -0,0 +1,162 @@
+---  
+title: Use a Blob indexer to ingest RBAC scopes metadata
+titleSuffix: Azure AI Search  
+description: Learn how to configure Azure AI Search indexers for ingesting Azure Role-Based Access (RBAC) metadata on Azure Blobs.
+ms.service: azure-ai-search  
+ms.topic: how-to
+ms.date: 07/07/2025  
+author: vaishalishah
+ms.author: vaishalishah
+---  
+
+# Use a Blob indexer to ingest RBAC scopes metadata
+
+[!INCLUDE [Feature preview](./includes/previews/preview-generic.md)]
+
+Starting in 2025-05-01-preview, you can now include RBAC scope alongside document ingestion in Azure AI Search and use those permissions to control access to search results.
+
+You can use the push APIs to upload and index content and permission metadata manually see [Indexing Permissions using the push REST API](search-index-access-control-lists-and-rbac-push-api.md), or you can use an indexer to automate data ingestion. This article focuses on the indexer approach.
+
+The indexer approach is built on this foundation:
+
++ [Role-based access control (Azure RBAC)](/azure/storage/blobs/data-lake-storage-access-control-model#role-based-access-control-azure-rbac). There's no support for Attribute-based access control (Azure ABAC).
+
++ [An Azure AI Search indexer for Blob](search-howto-indexing-azure-blob-storage.md) that retrieves and ingests data and metadata, including permission filters. To get permission filter support, you must use the 2025-05-01-preview REST API or a prerelease package of an Azure SDK that supports the feature.
+
++ [An index in Azure AI Search](search-how-to-create-search-index.md) containing the ingested documents and corresponding permissions. Permission metadata is stored as fields in the index. To set up queries that respect the permission filters, you must use the 2025-05-01-preview REST API or a prerelease package of an Azure SDK that supports the feature.
+
+## Prerequisites
+
++ [Microsoft Entra ID authentication and authorization](/entra/identity/authentication/overview-authentication). Services and apps must be in the same tenant. Role assignments are used for each authenticated connection.
+
++ Azure AI Search, any region, but you must have a billable tier (basic and higher) see [Service limits](search-limits-quotas-capacity.md) for managed identity support. The search service must be [configured for role-based access](search-security-enable-roles.md) and it must [have a managed identity (either system or user)](search-howto-managed-identities-data-sources.md).
+
+## Limitations
+
++ The following indexer features don't support permission preservation capabilities but are otherwise operational for Azure Blob content-only indexing:
+  + One-to-many [parsing modes](/rest/api/searchservice/indexers/create?view=rest-searchservice-2025-05-01-preview&preserve-view=true#blobindexerparsingmode), such as: `delimitedText`, `jsonArray`, `jsonLines`, and `markdown` with sub-mode `oneToMany`
+
+
+### Authorization
+
+For indexer execution, your search service identity must have **Storage Blob Data Reader** permission see [Connect to Azure Storage using a managed identity](search-howto-managed-identities-storage.md).
+
+## Configure Azure AI Search for indexing permission filters
+
+Recall that the search service must have:
+
++ [Role-based access enabled](search-security-enable-roles.md)
++ [Managed identity configured](search-howto-managed-identities-data-sources.md)
+
+### Authorization
+
+For indexer execution, the client issuing the API call must have **Search Service Contributor** permission to create objects, **Search Index Data Contributor** permission to perform data import, and **Search Index Data Reader** to query an index see [Connect to Azure AI Search using roles](search-security-rbac.md).
+
+## Indexing permission metadata
+
+In Azure AI Search, configure an indexer, data source, and index to pull permission metadata from blobs.
+
+### Configure the data source
+
++ Data Source type must be `azureblob`.
+
++ Data source must have `indexerPermissionOptions` with `rbacScope`.
+
+  + For `rbacScope`, configure the [connection string](search-howto-index-azure-data-lake-storage.md#supported-credentials-and-connection-strings) with managed identity format.
+  
+  + For connection strings using a [user-assigned managed identity](search-howto-managed-identities-storage.md#user-assigned-managed-identity), you must also specify the `identity` property.
+
+<!-- Question/Comment: check this example -->
+JSON example with system managed identity:
+
+```json
+{
+    "name" : "my-blob-datasource",
+    "type": "azureblob",
+    "indexerPermissionOptions": ["rbacScope"],
+    "credentials": {
+    "connectionString": "ResourceId=/subscriptions/<your subscription ID>/resourceGroups/<your resource group name>/providers/Microsoft.Storage/storageAccounts/<your storage account name>/;"
+    },
+    "container": {
+        "name": "<your container name>",
+        "query": "<optional-query>"
+    }
+}
+```
+
+JSON schema example with a user-managed identity in the connection string:
+
+```json
+{
+    "name" : "my-blob-datasource",
+    "type": "azureblob",
+    "indexerPermissionOptions": ["rbacScope"],
+    "credentials": {
+    "connectionString": "ResourceId=/subscriptions/<your subscription ID>/resourceGroups/<your resource group name>/providers/Microsoft.Storage/storageAccounts/<your storage account name>/;"
+    },
+    "container": {
+        "name": "<your container name>",
+        "query": "<optional-query>"
+    },
+    "identity": {
+        "@odata.type": "#Microsoft.Azure.Search.DataUserAssignedIdentity",
+        "userAssignedIdentity": "/subscriptions/{subscription-ID}/resourceGroups/{resource-group-name}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{user-assigned-managed-identity-name}"
+    }
+}
+```
+
+### Create permission fields in the index
+
+In Azure AI Search, make sure your index contains field definitions for the permission metadata. Permission metadata can be indexed when `indexerPermissionOptions` is specified in the data source definition.
+
+Recommended schema attributes RBAC Scope:
+
++ RBAC scope field with `rbacScope` permissionFilter value.
++ Property `permissionFilterOption` to enable filtering at querying time.
++ Use string fields for permission metadata
++ Set `filterable` to true on all fields.
+
+Notice that `retrievable` is false. You can set it true during development to verify permissions are present, but remember to set to back to false before deploying to a production environment.
+
+JSON schema example:
+
+```json
+{
+  ...
+  "fields": [
+    ...
+    { 
+        "name": "RbacScope", 
+        "type": "Edm.String", 
+        "permissionFilter": "rbacScope", 
+        "filterable": true, 
+        "retrievable": false 
+    }
+  ],
+  "permissionFilterOption": "enabled"
+}
+```
+
+### Configure the indexer
+
+Field mappings within an indexer set the data path to fields in an index. Target and destination fields that vary by name or data type require an explicit field mapping. The following metadata fields in Azure Blob might need field mappings if you vary the field name:
+
++ **metadata_rbac_scope** (`Edm.String`) - the container RBAC scope.
+
+Specify `fieldMappings` in the indexer to route the permission metadata to target fields during indexing.
+
+JSON schema example:
+
+```json
+{
+  ...
+  "fieldMappings": [
+    { "sourceFieldName": "metadata_rbac_scope", "targetFieldName": "RbacScope" }
+  ]
+}
+```
+
+## Deletion tracking 
+
+To effectively manage blob deletion, ensure that you have enabled [deletion tracking](search-howto-index-changed-deleted-blobs.md) before your indexer runs for the first time. This feature allows the system to detect deleted blobs from your source and have them deleted from the index.  
+

@@ -7,6 +7,7 @@ manager: nitinme
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.custom:
   - references_regions
   - build-2024

@@ -6,6 +6,7 @@ author: HeidiSteen
 ms.author: heidist
 
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.custom:
   - ignite-2023
 ms.topic: conceptual

@@ -6,6 +6,7 @@ author: gmndrg
 ms.author: gimondra
 ms.date: 07/03/2025
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.topic: conceptual
 ms.custom:
   - build-2025

@@ -8,6 +8,7 @@ metadata:
   author: haileytap
   ms.author: haileytapia
   ms.service: azure-ai-search
+  ms.update-cycle: 90-days
   ms.topic: faq
   ms.date: 03/21/2025
 title: Azure AI Search Frequently Asked Questions

@@ -7,6 +7,7 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.custom:
   - ignite-2024
 ms.topic: conceptual
@@ -36,7 +37,7 @@ The following table summarizes features by category. There's feature parity in a
 |-------------------|----------|
 | Vector indexing | Within a search index, add [vector fields](vector-search-how-to-create-index.md) to support  [**vector search**](vector-search-overview.md) scenarios. Vector fields can coexist with nonvector fields in the same search document. |
 | Vector queries | [Formulate single and multiple vector queries](vector-search-how-to-query.md). |
-| Vector search algorithms | Use [Hierarchical Navigable Small World (HNSW)](vector-search-ranking.md#when-to-use-hnsw) or [exhaustive K-Nearest Neighbors (KNN)](vector-search-ranking.md#when-to-use-exhaustive-knn) to find similar vectors in a search index. |
+| Vector search algorithms | Use [Hierarchical Navigable Small World (HNSW)](vector-search-ranking.md#about-hnsw) or [exhaustive K-Nearest Neighbors (KNN)](vector-search-ranking.md#about-exhaustive-knn) to find similar vectors in a search index. |
 | Vector filters | [Apply filters before or after query execution](vector-search-filters.md) for greater precision during information retrieval. |
 | Hybrid information retrieval | Search for concepts and keywords in a single [hybrid query request](hybrid-search-how-to-query.md). </p>[**Hybrid search**](hybrid-search-overview.md) consolidates vector and text search, with optional semantic ranking and relevance tuning for best results.|
 | Integrated data chunking and vectorization | Native data chunking through [Text Split skill](cognitive-search-skill-textsplit.md). Native vectorization through [vectorizers](vector-search-how-to-configure-vectorizer.md) and embedding skills such as [AzureOpenAIEmbeddingModel](cognitive-search-skill-azure-openai-embedding.md), [Azure AI Vision multimodal](cognitive-search-skill-vision-vectorize.md), and the [AML skill](cognitive-search-aml-skill.md) that you can use to connect to endpoints in the Azure AI Foundry model catalog. </p>[**Integrated vectorization**](vector-search-integrated-vectorization.md) provides an end-to-end indexing pipeline from source files to queries.|

@@ -5,6 +5,7 @@ description: Learn how to create a knowledge agent that processes multi-turn con
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.topic: quickstart
 ms.date: 6/24/2025
 zone_pivot_groups: search-get-started-agentic-retrieval

@@ -5,6 +5,7 @@ description: Learn how to index and search for multimodal content in the Azure p
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.topic: quickstart
 ms.date: 06/11/2025
 ms.custom:

@@ -5,6 +5,7 @@ description: Learn how to use a wizard to automate data chunking and vectorizati
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.custom:
   - build-2024
   - ignite-2024

@@ -6,6 +6,7 @@ manager: nitinme
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.topic: quickstart
 ms.date: 03/04/2025
 ms.custom:

@@ -5,33 +5,45 @@ description: Learn how to use grounding data from Azure AI Search with a chat mo
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.custom:
   - ignite-2024
 ms.topic: quickstart
-ms.date: 03/04/2025
+ms.date: 07/09/2025
 zone_pivot_groups: programming-languages-ai-search-rag-qs
 ---
 
 # Quickstart: Generative search (RAG) using grounding data from Azure AI Search
 
 In this quickstart, you send queries to a chat completion model for a conversational search experience over your indexed content on Azure AI Search. After setting up Azure OpenAI and Azure AI Search resources in the Azure portal, you run code to call the APIs.
 
+::: zone pivot="csharp"
+
+[!INCLUDE [.NET quickstart](includes/quickstarts/search-get-started-rag-dotnet.md)]
+
+::: zone-end
+
+::: zone pivot="javascript"
+
+[!INCLUDE [JavaScript quickstart](includes/quickstarts/search-get-started-rag-javascript.md)]
+
+::: zone-end
 
 ::: zone pivot="python"
 
 [!INCLUDE [Python quickstart](includes/quickstarts/search-get-started-rag-python.md)]
 
 ::: zone-end
 
-::: zone pivot="typescript"
+::: zone pivot="rest"
 
-[!INCLUDE [TypeScript quickstart](includes/quickstarts/search-get-started-rag-typescript.md)]
+[!INCLUDE [Rest quickstart](includes/quickstarts/search-get-started-rag-rest.md)]
 
 ::: zone-end
 
-::: zone pivot="javascript"
+::: zone pivot="typescript"
 
-[!INCLUDE [JavaScript quickstart](includes/quickstarts/search-get-started-rag-javascript.md)]
+[!INCLUDE [TypeScript quickstart](includes/quickstarts/search-get-started-rag-typescript.md)]
 
 ::: zone-end
 

@@ -1,171 +1,32 @@
 ---
 title: 'Quickstart: Keyless Connection'
 titleSuffix: Azure AI Search
-description: Learn how to switch from API keys to Microsoft Entra identities and role-based access control (RBAC).
+description: Learn how to use role-based access control (RBAC) to connect to an Azure AI Search service.
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.topic: quickstart
-ms.date: 07/02/2025
+ms.date: 07/08/2025
+zone_pivot_groups: search-get-started-rbac
 ---
 
-# Quickstart: Connect without keys
+# Quickstart: Connect to a search service
 
-In this quickstart, you configure Azure AI Search to use Microsoft Entra ID authentication and role-based access control (RBAC) to connect from your local system without API keys. You then use Jupyter notebooks or a REST client to interact with your search service.
+::: zone pivot="python"
 
-If you completed other quickstarts that connect using API keys, this quickstart shows you how to switch to identity-based authentication so that you can avoid hard-coded keys in your example code.
+[!INCLUDE [Python quickstart](includes/quickstarts/search-get-started-rbac-python.md)]
 
-## Prerequisites
+::: zone-end
 
-- An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F).
+::: zone pivot="rest"
 
-- An [Azure AI Search service](search-create-service-portal.md) in any region or tier. However, to configure a managed identity for Azure AI Search, you need the Basic tier or higher.
+[!INCLUDE [REST quickstart](includes/quickstarts/search-get-started-rbac-rest.md)]
 
-- A command line tool, such as PowerShell or Bash, and the [Azure CLI](/cli/azure/install-azure-cli).
+::: zone-end
 
-## Step 1: Get your Azure subscription and tenant IDs
+## Related content
 
-You need this step if you have more than one subscription or tenant.
-
-1. Get the Azure subscription and tenant for your search service:
-
-   1. Sign into the [Azure portal](https://portal.azure.com) and navigate to your search service.
-
-   1. Notice the subscription name and ID in **Overview** > **Essentials**.
-
-   1. Now select the subscription name to show the parent management group (tenant ID) on the next page.
-
-      :::image type="content" source="media/search-get-started-rbac/select-subscription-name.png" lightbox="media/search-get-started-rbac/select-subscription-name.png" alt-text="Screenshot of the Azure portal page providing the subscription name":::
-
-1. You now know which subscription and tenant Azure AI Search is under. Switch to your local device and a command prompt, and identify the active Azure subscription and tenant on your device:
-
-   ```azurecli
-   az account show
-   ```
-
-1. If the active subscription and tenant differ from the information obtained in the previous step, change the subscription ID. Next, sign in to Azure using the tenant ID that you found in the previous step:
-
-   ```azurecli
-    az account set --subscription <your-subscription-id>
-
-    az login --tenant <your-tenant-id>
-   ```
-
-## Step 2: Configure Azure AI Search for RBAC
-
-1. Sign in to the [Azure portal](https://portal.azure.com) and navigate to your Azure AI Search service.
-
-1. Enable role-based access control (RBAC):
-
-   1. Go to **Settings** > **Keys**.
-
-   1. Choose **Role-based control** or **Both** if you need time to transition clients to role-based access control.
-
-      If you choose **Role-based control**, make sure that you assign yourself *all* roles named in the next instruction or you won't be able to complete tasks in the Azure portal or through a  local client.
-
-1. Assign roles in the Azure portal:
-
-   1. Navigate to your search service.
-
-   1. Select **Access Control (IAM)** in the left pane.
-
-   1. Select **+ Add** > **Add role assignment**.
-
-   1. Choose a role (**Search Service Contributor**, **Search Index Data Contributor**, **Search Index Data Reader**) and assign it to your Microsoft Entra user or group identity.
-
-      Repeat for each role.
-
-      You need **Search Service Contributor** plus **Search Index Data Contributor** to create, load, and query objects on Azure AI Search. For more information, see [Connect using roles](search-security-rbac.md).
-
-> [!TIP]
-> Later, if you get authentication failure errors, recheck the settings in this section. There could be policies at the subscription or resource group level that override any API settings you specify.
-
-## Step 3: Connect from your local system
-
-If you haven't yet signed in to Azure:
-
-```azurecli
-az login
-```
-
-### Using Python and Jupyter notebooks
-
-1. Install the Azure Identity and Azure Search libraries:
-
-    ```python
-    pip install azure-identity azure-search-documents
-    ```
-
-1. Authenticate and connect to Azure AI Search:
-
-    ```python
-    from azure.identity import DefaultAzureCredential
-    from azure.search.documents import SearchClient
-    
-    service_endpoint = "https://<your-search-service-name>.search.windows.net"
-    index_name = "hotels-sample-index"
-    
-    credential = DefaultAzureCredential()
-    client = SearchClient(endpoint=service_endpoint, index_name=index_name, credential=credential)
-    
-    results = client.search("beach access")
-    for result in results:
-        print(result)
-    ```
-
-### Using a REST client
-
-Several quickstarts and tutorials use a REST client, such as Visual Studio Code with the REST extension. Here's how you connect to Azure AI Search from Visual Studio Code.
-
-You should have a `.rest` or `.http` file, similar to the one described in [Quickstart: Vector search](search-get-started-vector.md).
-
-1. Generate an access token.
-
-   ```azurecli
-   az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv
-   ```
-
-1. At the top of your file, set variables used for the connection, pasting the full search service endpoint and the access token you got in the previous step. Your variables should look similar to the following example. Notice the values aren't quote-enclosed.
-
-    ```REST
-    @baseUrl = https://contoso.search.search.windows.net
-    @token = <a long GUID>
-    ```
-
-1. Specify the authorization bearer token in a REST call:
-
-   ```REST
-    POST https://{{baseUrl}}/indexes/hotels-sample-index/docs/search?api-version=2024-07-01 HTTP/1.1
-      Content-type: application/json
-      Authorization: Bearer {{token}}
-    
-        {
-             "queryType": "simple",
-             "search": "beach access",
-             "filter": "",
-             "select": "HotelName,Description,Category,Tags",
-             "count": true
-         }
-   ```
-
-### Troubleshoot 401 errors
-
-- Check the active subscription and tenant (`az account show`) and make sure it's valid for your search service.
-
-- Check the search service **Settings** > **Keys** options in the Azure portal and confirm the service is configured for **Both"** or **Role-based access control**.
-
-- For the REST client only: Check the token and endpoint specified in your file and make sure there's no surrounding quotes or extra spaces. A 401 invalid token message occurs if the token in the request header includes the `@` symbol. For example, if the variable is `@token`, the reference in the request is simply `{{token}}`.
-
-If all else fails, restart your device to remove any cached tokens, and then repeat the steps in this section, starting with `az login`.
-
-## Additional configuration
-
-Configure a managed identity for outbound connections:
-
-- [Configure a system-assigned or user-assigned managed identity](search-howto-managed-identities-data-sources.md) for your search service.
-
-- [Use role assignments](keyless-connections.md) to authorize access to other Azure resources.
-
-Network access configuration:
-
-- [Set inbound rules](service-configure-firewall.md) to accept or reject requests to Azure AI Search based on IP address.
++ [Configure a managed identity in Azure AI Search](search-howto-managed-identities-data-sources.md)
++ [Connect your app to Azure AI Search using identities](keyless-connections.md)
++ [Configure network access and firewall rules for Azure AI Search](service-configure-firewall.md)
\ No newline at end of file

@@ -6,6 +6,7 @@ manager: nitinme
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.custom:
   - devx-track-dotnet
   - devx-track-python

@@ -6,6 +6,7 @@ manager: nitinme
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.custom:
   - ignite-2023
 ms.topic: quickstart

@@ -6,6 +6,7 @@ manager: nitinme
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.custom:
   - devx-track-dotnet
   - devx-track-extended-java

@@ -5,6 +5,7 @@ description: Learn how to call the Search REST and Azure SDK APIs for vector wor
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.custom:
   - ignite-2023
 ms.topic: quickstart

@@ -6,6 +6,7 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.topic: how-to
 ms.date: 05/19/2025
 ---

@@ -8,6 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.topic: how-to
 ms.date: 05/08/2025
 ---

@@ -27,6 +27,9 @@ To configure and run the indexer, you can use:
 
 This article uses the REST APIs to illustrate each step.
 
+> [!NOTE]
+> Azure AI Search can now ingest RBAC scope during indexing and transfers those permissions to indexed content in the search index. For more information about RBAC scope during indexing, see [Indexing Azure Role-Based Access Control scope using Indexers](search-blob-indexer-role-based-access.md).
+
 ## Prerequisites
 
 + [Azure Blob Storage](/azure/storage/blobs/storage-blobs-overview), Standard performance (general-purpose v2).

@@ -2,7 +2,8 @@
 title: Use ADLS Gen2 indexer to ingest permission metadata
 titleSuffix: Azure AI Search  
 description: Learn how to configure Azure AI Search indexers for ingesting Access Control Lists (ACLs) and Azure Role-Based Access (RBAC) metadata on Azure Data Lake Storage (ADLS) Gen2 blobs.
-ms.service: azure-ai-search  
+ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.topic: how-to
 ms.date: 05/08/2025  
 author: wlifuture

@@ -7,6 +7,7 @@ ms.author: haileytapia
 manager: nitinme
 ms.date: 06/12/2025
 ms.service: azure-ai-search
+ms.update-cycle: 90-days
 ms.topic: conceptual
 ms.custom:
   - references_regions

@@ -7,6 +7,7 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.custom:
   - ignite-2023
 ms.topic: conceptual

@@ -7,6 +7,7 @@ ms.author: heidist
 manager: nitinme
 ms.date: 03/31/2025
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.topic: how-to
 ms.custom:
   - subject-rbac-steps

@@ -7,6 +7,7 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.custom:
   - ignite-2023
 ms.topic: how-to

@@ -6,6 +6,7 @@ manager: nitinme
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.custom:
   - ignite-2023
 ms.topic: how-to

@@ -7,6 +7,7 @@ manager: nitinme
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.topic: conceptual
 ms.date: 06/11/2025
 ms.custom: references_regions

@@ -7,6 +7,7 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.custom:
   - ignite-2024
 ms.topic: overview

@@ -6,6 +6,7 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.custom:
   - ignite-2023
 ms.topic: how-to

@@ -7,6 +7,7 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.custom:
   - ignite-2023
 ms.topic: how-to

@@ -5,6 +5,7 @@ description: Learn how to combine scoring profiles with semantic ranking in Azur
 author: gmndrg
 ms.author: gimondra
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.topic: how-to
 ms.date: 06/10/2025
 ---

@@ -7,6 +7,7 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.custom:
   - ignite-2023
   - ignite-2024

@@ -6,6 +6,7 @@ manager: nitinme
 author: eric-urban
 ms.author: eur
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.custom:
   - ignite-2024
   - references_regions

@@ -7,6 +7,7 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.custom:
   - ignite-2023
 ms.topic: concept-article

@@ -72,6 +72,8 @@ items:
       href: search-indexer-securing-resources.md
 - name: Quickstarts
   items:
+  - name: Connect to a search service
+    href: search-get-started-rbac.md
   - name: Agentic search
     href: search-get-started-agentic-retrieval.md
   - name: Vector search
@@ -84,8 +86,6 @@ items:
     href: search-get-started-semantic.md
   - name: Chat with your data
     href: /azure/ai-services/openai/use-your-data-quickstart?context=/azure/search/context/context
-  - name: Connect without keys
-    href: search-get-started-rbac.md
   - name: Azure portal
     items:
     - name: Create a search index
@@ -529,12 +529,14 @@ items:
         href: search-document-level-access-overview.md
       - name: Use security filters
         href: search-security-trimming-for-azure-search.md
-      - name: Use ACLs
+      - name: Use ACLs or RBAC scopes
         items:
         - name: Push document-level permissions to an index
           href: search-index-access-control-lists-and-rbac-push-api.md
         - name: Pull ADLS Gen2 permissions into an index
           href: search-indexer-access-control-lists-and-role-based-access.md
+        - name: Pull Blob RBAC scopes into an index
+          href: search-blob-indexer-role-based-access.md
         - name: Query with permission filters
           href: search-query-access-control-rbac-enforcement.md
     - name: Data encryption

@@ -3,6 +3,7 @@ title: 'Tutorial: Index ADLS Gen2 permission metadata'
 titleSuffix: Azure AI Search  
 description: Learn how to index Access Control Lists (ACLs) and Azure Role-Based Access Control (RBAC) scope from ADLS Gen2 and query with permission-filtered results in Azure AI Search.
 ms.service: azure-ai-search  
+ms.update-cycle: 180-days
 ms.topic: tutorial  
 ms.date: 05/20/2025
 author: wlifuture

@@ -5,6 +5,7 @@ description: Learn how to build a custom analyzer to improve the quality of sear
 author: gmndrg
 ms.author: gimondra
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.custom:
   - ignite-2023
 ms.topic: tutorial

@@ -6,6 +6,7 @@ manager: nitinme
 author: diberry
 ms.author: diberry
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.topic: tutorial
 ms.date: 01/17/2025
 ms.custom:

@@ -7,6 +7,7 @@ manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.devlang: csharp
 ms.custom:
   - ignite-2023

@@ -6,6 +6,7 @@ manager: nitinme
 author: diberry
 ms.author: diberry
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.topic: tutorial
 ms.date: 01/17/2025
 ms.custom:

@@ -6,6 +6,7 @@ manager: nitinme
 author: diberry
 ms.author: diberry
 ms.service: azure-ai-search
+ms.update-cycle: 180-days
 ms.topic: tutorial
 ms.date: 01/17/2025
 ms.custom: