@@ -640,6 +640,11 @@
             "source_path_from_root": "/articles/search/tutorial-csharp-create-mvc-app.md",
             "redirect_url": "/azure/search/tutorial-csharp-overview",
             "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/search/search-get-started-rag.md",
+            "redirect_url": "https://github.com/Azure-Samples/azure-search-classic-rag/tree/main/quickstarts",
+            "redirect_document_id": false
         }
     ]
   }
\ No newline at end of file

@@ -6,14 +6,14 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 11/19/2025
+ms.date: 12/12/2025
 zone_pivot_groups: agentic-retrieval-pivots
 ---
 
 # Create a knowledge base in Azure AI Search
 
 ::: zone pivot="csharp"
-[!INCLUDE [C#](includes/how-tos/agentic-knowledge-source-how-to-search-index-csharp.md)]
+[!INCLUDE [C#](includes/how-tos/agentic-retrieval-how-to-create-knowledge-base-csharp.md)]
 ::: zone-end
 
 ::: zone pivot="python"

@@ -139,7 +139,7 @@ To create the knowledge source for this quickstart:
 
 1. Select **Add vectorizer**.
 
-1. Select **Microsoft Foundry** for the kind, and then select your subscription, project, and embedding model deployment.
+1. Select **Azure AI Foundry** for the kind, and then select your subscription, project, and embedding model deployment.
 
 1. Select **System managed identity** for the authentication type.
 
@@ -166,7 +166,7 @@ To create the knowledge base for this quickstart:
 
 1. Under **Model deployment**, select **Add model deployment**.
 
-1. Select **Foundry** for the kind, and then select your subscription, project, and LLM deployment.
+1. Select **Azure AI Foundry** for the kind, and then select your subscription, project, and LLM deployment.
 
 1. Select **System assigned identity** for the authentication type.
 

@@ -4,7 +4,7 @@ author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: include
-ms.date: 11/14/2025
+ms.date: 12/12/2025
 ---
 
 [!INCLUDE [Feature preview](../previews/preview-generic.md)]
@@ -160,6 +160,7 @@ index_client = SearchIndexClient(endpoint = "search_url", credential = AzureKeyC
 
 aoai_params = AzureOpenAIVectorizerParameters(
     resource_url = "aoai_endpoint",
+    api_key="aoai_api_key",
     deployment_name = "aoai_gpt_deployment",
     model_name = "aoai_gpt_model",
 )
@@ -209,7 +210,7 @@ Replace "Where does the ocean look green?" with a query string that's valid for
 # Send grounding request
 from azure.core.credentials import AzureKeyCredential
 from azure.search.documents.knowledgebases import KnowledgeBaseRetrievalClient
-from azure.search.documents.knowledgebases.models import KnowledgeBaseMessage, KnowledgeBaseMessageTextContent, KnowledgeBaseRetrievalRequest, RemoteSharePointKnowledgeSourceParams
+from azure.search.documents.knowledgebases.models import KnowledgeBaseMessage, KnowledgeBaseMessageTextContent, KnowledgeBaseRetrievalRequest, SearchIndexKnowledgeSourceParams
 
 kb_client = KnowledgeBaseRetrievalClient(endpoint = "search_url", knowledge_base_name = "knowledge_base_name", credential = AzureKeyCredential("api_key"))
 

@@ -7,7 +7,7 @@ ms.topic: include
 ms.date: 11/14/2025
 ---
 
-Use [Knowledge Sources - Status (REST API)](/rest/api/searchservice/knowledge-sources/status?view=rest-searchservice-2025-11-01-preview&preserve-view=true) to monitor ingestion progress and health, including indexer status for knowledge sources that generate an indexer pipeline and populate a search index.
+Use [Knowledge Sources - Status (REST API)](/rest/api/searchservice/knowledge-sources/get-status) to monitor ingestion progress and health, including indexer status for knowledge sources that generate an indexer pipeline and populate a search index.
 
 ```http
 ### Check knowledge source ingestion status

@@ -1,362 +0,0 @@
----
-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/pricing/purchase-options/azure-account?cid=msft_learn).
-
-- 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 Microsoft 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 you can create by running the [**Import data (new)**](../../search-import-data-portal.md) wizard and following the instructions to create the index.
-
-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.

@@ -1,387 +0,0 @@
----
-manager: nitinme
-author: KarlErickson
-ms.author: karler
-ms.service: azure-ai-search
-ms.topic: include
-ms.date: 08/08/2025
-ai-usage: ai-assisted
----
-## Prerequisites
-
-- An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn).
-
-- 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 Microsoft 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).
-- [Java 21 (LTS)](/java/openjdk/install).
-- [Maven](https://maven.apache.org/download.cgi).
-
-## 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 receives 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 receives 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**, 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, 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**.
-
-1. In the Azure portal, [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices).
-
-1. Follow the instructions in [this quickstart](../../search-import-data-portal.md) to create the index.
-
-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 find placeholders for constructs that you can add 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`.
-
-   The 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 following 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/`.
-
-## Set up environment variables for local development
-
-Define environment variables by using the following commands. Replace the placeholders with your actual values.
-
-```bash
-export AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
-export AZURE_SEARCH_INDEX_NAME=hotels-sample-index
-export AZURE_OPENAI_ENDPOINT=<YOUR AZURE OPENAI ENDPOINT>
-export AZURE_DEPLOYMENT_MODEL=<YOUR DEPLOYMENT NAME>
-```
-
-## Sign in to Azure
-
-You're using Microsoft Entra ID and role assignments for the connection. Make sure you're signed 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're now signed in to Azure from your local device.
-
-## Set up the Java project
-
-Set up a Maven project with the required dependencies.
-
-1. Create a new directory and navigate to it:
-
-   ```bash
-   mkdir rag-quickstart && cd rag-quickstart
-   ```
-
-1. Create a Maven project structure:
-
-   ```bash
-   mkdir -p src/main/java/com/example/rag
-   ```
-
-1. Create a `pom.xml` file with the following content:
-
-   :::code language="xml" source="~/azure-search-java-samples/rag-quickstart/pom.xml" :::
-
-## Set up query and chat thread
-
-Create a query script that uses the Azure AI Search index and the chat model to generate responses based on grounding data. The following steps guide you through setting up the query script.
-
-1. Create a `Query.java` file in the `src/main/java/com/example/rag` directory with the following code:
-
-   :::code language="java" source="~/azure-search-java-samples/rag-quickstart/src/main/java/com/example/rag/Query.java" :::
-
-    The preceding code does the following steps:
-    - Loads environment variables by using `System.getenv`.
-    - Configures Azure AI Search and Azure OpenAI clients by using role-based authentication.
-    - Queries Azure AI Search for hotels with complimentary breakfast by using semantic search.
-    - Sends the search results to Azure OpenAI as context for a chat completion.
-    - Prints the model's response.
-
-1. Run the following command in a terminal to execute the query script:
-
-    ```bash
-    mvn compile exec:java -Dexec.mainClass="com.example.rag.Query"
-    ```
-
-1. View the output, which consists of recommendations for several hotels. Here's an example of what the output might look like:
-
-    ```
-    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
-    ```
-
-## Troubleshooting
-
-If you get a **Forbidden** error message, check your Azure AI Search configuration to make sure you enable role-based access.
-
-If you get an **Authorization failed** error message, wait a few minutes and try again. It can take several minutes for role assignments to become operational.
-
-If you get a **Resource not found** error message, check the resource URIs and make sure the API version on the chat model is valid.
-
-Otherwise, 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.
-
-You might also try the query without semantic ranking by removing the semantic search options in the query parameters step. Semantic ranking can noticeably improve the relevance of query results and the ability of the LLM to return useful information. Experimentation can help you decide whether it makes a difference for your content.
-
-## 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, change your prompt to include formatting instructions: 
-
-```text
-Can you recommend a few hotels that offer complimentary breakfast? 
-Tell me their description, address, tags, and the rate for one room that sleeps 4 people.
-```
-
-1. Create a new file `QueryComplex.java` in the `src/main/java/com/example/rag` directory.
-1. Copy the following code to the file:
-
-   :::code language="java" source="~/azure-search-java-samples/rag-quickstart/src/main/java/com/example/rag/QueryComplex.java" :::
-
-1. Run the following command in a terminal to execute the query script:
-
-    ```bash
-    mvn compile exec:java -Dexec.mainClass="com.example.rag.QueryComplex"
-    ```
-
-1. View the output from Azure OpenAI, and it adds content from complex types.
-
-    ```
-    Here are a few hotels that offer complimentary breakfast and have rooms that sleep 4 people:
-
-    1. **Head Wind Resort**
-       - **Description:** The best of old town hospitality combined with views of the river and 
-       cool breezes off the prairie. Enjoy a complimentary continental breakfast in the lobby, 
-       and free Wi-Fi throughout the hotel.
-       - **Address:** 7633 E 63rd Pl, Tulsa, OK 74133, USA
-       - **Tags:** Coffee in lobby, free Wi-Fi, view
-       - **Room for 4:** Suite, 2 Queen Beds (Amenities) - $254.99
-
-    2. **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. Offers continental breakfast.
-       - **Address:** 2211 Elliott Ave, Seattle, WA 98121, USA
-       - **Tags:** View, pool, restaurant, bar, continental breakfast
-       - **Room for 4:** Suite, 2 Queen Beds (Amenities) - $254.99
-
-    3. **Swan Bird Lake Inn**
-       - **Description:** Continental-style breakfast featuring a variety of food and drinks. 
-       Locally made caramel cinnamon rolls are a favorite.
-       - **Address:** 1 Memorial Dr, Cambridge, MA 02142, USA
-       - **Tags:** Continental breakfast, free Wi-Fi, 24-hour front desk service
-       - **Room for 4:** Budget Room, 2 Queen Beds (City View) - $85.99
-
-    4. **Gastronomic Landscape Hotel**
-       - **Description:** Known for its culinary excellence under the management of William Dough, 
-       offers continental breakfast.
-       - **Address:** 3393 Peachtree Rd, Atlanta, GA 30326, USA
-       - **Tags:** Restaurant, bar, continental breakfast
-       - **Room for 4:** Budget Room, 2 Queen Beds (Amenities) - $66.99
-    ...
-       - **Tags:** Pool, continental breakfast, free parking
-       - **Room for 4:** Budget Room, 2 Queen Beds (Amenities) - $60.99
-
-    Enjoy your stay! Let me know if you need any more information.
-    ```
-
-## Troubleshooting errors
-
-To debug Azure SDK errors, enable logging by adding the following dependency to your `pom.xml`:
-
-```xml
-<dependency>
-    <groupId>org.slf4j</groupId>
-    <artifactId>slf4j-simple</artifactId>
-    <version>2.0.7</version>
-</dependency>
-```
-
-Then set the following system properties when running your application:
-
-```bash
-mvn compile exec:java -Dexec.mainClass="com.example.rag.Query" \
-    -Dorg.slf4j.simpleLogger.defaultLogLevel=debug
-```
-
-This configuration enables detailed logging for the Azure SDK, which can help you identify [issues with authentication](https://github.com/Azure/azure-sdk-for-java/wiki/Azure-Identity-Troubleshooting), network connectivity, or other problems.
-
-Rerun the query script. You should now see informational statements from the SDKs in the output that provide more detail about any issues.
-
-If you see output messages related to `ManagedIdentityCredential` and token acquisition failures, it might be that you have multiple tenants, and your Azure sign-in uses 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`.
-
-After you get your tenant ID, run `az login --tenant <YOUR-TENANT-ID>` at a command prompt, then rerun the script.
-
-## Clean up
-
-When you're working in your own subscription, it's a good idea at the end of a project to check if 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.

@@ -1,389 +0,0 @@
----
-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/pricing/purchase-options/azure-account?cid=msft_learn).
-
-- 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 Microsoft 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).
-- [Node.JS with LTS](https://nodejs.org/en/download/).
-
-
-## 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**.
-
-1. In the Azure portal, [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices).
-
-1. Follow the instructions in [this quickstart](../../search-import-data-portal.md) to create the index.
-
-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/`.
-
-## Set up environment variables for local development
-
-1. Create a `.env` file.
-1. Add the following environment variables to the `.env` file, replacing the values with your own service endpoints and keys.
-
-   ```plaintext
-   AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
-   AZURE_SEARCH_INDEX_NAME=hotels-sample-index
-
-   AZURE_OPENAI_ENDPOINT=<YOUR AZURE OPENAI ENDPOINT>
-   AZURE_OPENAI_VERSION=<YOUR AZURE OPENAI API VERSION>
-   AZURE_DEPLOYMENT_MODEL=<YOUR DEPLOYMENT NAME>
-   ```
-
-## Set up the Node.JS project
-
-Set up project with Visual Studio Code and TypeScript.
-
-1. Start Visual Studio Code in a new directory.
-
-   ```bash
-   mkdir rag-quickstart && cd rag-quickstart
-   code .
-   ```
-1. Create a new package for ESM modules in your project directory.
-
-   ```bash
-   npm init -y
-   npm pkg set type=module
-   ```
-
-   This creates a `package.json` file with default values.
-
-1. Install the following npm packages.
-
-   ```bash
-   npm install @azure/identity @azure/search-documents openai dotenv 
-   ```
-
-1. Create a `src` directory in your project directory.
-
-   ```bash
-   mkdir src
-   ```
-
-## 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 query and chat thread
-
-Create a query script that uses the Azure AI Search index and the chat model to generate responses based on grounding data. The following steps guide you through setting up the query script.
-
-1. Create a `query.js` file in the `src` directory with the following code.
-    
-    :::code language="javascript" source="~/azure-search-javascript-samples/quickstart-rag-js/src/query.js" :::
-
-    The preceding code does the following:
-    - Imports the necessary libraries for Azure AI Search and Azure OpenAI.
-    - Uses environment variables to configure the Azure AI Search and Azure OpenAI clients.
-    - Defines a function to get the clients for Azure AI Search and Azure OpenAI, using environment variables for configuration.
-    - Defines a function to query Azure AI Search for sources based on the user query.
-    - Defines a function to query Azure OpenAI for a response based on the user query and the sources retrieved from Azure AI Search.
-    - The `main` function orchestrates the flow by calling the search and OpenAI functions, and then prints the response.    
-     
-1. Run the following command in a terminal to execute the query script:
-
-    ```bash
-    node -r dotenv/config query.js
-    ```
-
-    The `.env` is passed into the runtime using the `-r dotenv/config`. 
-
-1. View the output, which consists of recommendations for several hotels. Here's an example of what the output might look like:
-    
-    ```
-    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
-    ```
-
-## Troubleshooting
-
-If you get a **Forbidden** error message, check Azure AI Search configuration to make sure role-based access is enabled.
-
-If you get an **Authorization failed** error message, wait a few minutes and try again. It can take several minutes for role assignments to become operational.
-
-If you get a **Resource not found** error message, check the resource URIs and make sure the API version on the chat model is valid.
-
-Otherwise, 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.
-
-You might also try the query without semantic ranking by setting `use_semantic_reranker=False` in the query parameters step. Semantic ranking can noticably improve the relevance of query results and the ability of the LLM to return useful information. Experimentation can help you decide whether it makes a difference for your content.
-
-## 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, change your prompt to include formatting instructions: 
-
-```text
-Can you recommend a few hotels that offer complimentary breakfast? 
-Tell me their description, address, tags, and the rate for one room that sleeps 4 people.
-```
-
-1. Create a new file `queryComplex.js`. 
-1. Copy the following code to the file:
-
-    :::code language="javascript" source="~/azure-search-javascript-samples/quickstart-rag-js/src/queryComplex.js" :::
-
-
-1. Run the following command in a terminal to execute the query script:
-
-    ```bash
-    node -r dotenv/config queryComplex.js
-    ```
-
-    The `.env` is passed into the runtime using the `-r dotenv/config`. 
-
-1. View the output from Azure OpenAI, and it adds content from complex types.
-    
-    ```
-    Here are a few hotels that offer complimentary breakfast and have rooms that sleep 4 people:
-    
-    1. **Head Wind Resort**
-       - **Description:** The best of old town hospitality combined with views of the river and 
-       cool breezes off the prairie. Enjoy a complimentary continental breakfast in the lobby, 
-       and free Wi-Fi throughout the hotel.
-       - **Address:** 7633 E 63rd Pl, Tulsa, OK 74133, USA
-       - **Tags:** Coffee in lobby, free Wi-Fi, view
-       - **Room for 4:** Suite, 2 Queen Beds (Amenities) - $254.99
-    
-    2. **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. Offers continental breakfast.
-       - **Address:** 2211 Elliott Ave, Seattle, WA 98121, USA
-       - **Tags:** View, pool, restaurant, bar, continental breakfast
-       - **Room for 4:** Suite, 2 Queen Beds (Amenities) - $254.99
-    
-    3. **Swan Bird Lake Inn**
-       - **Description:** Continental-style breakfast featuring a variety of food and drinks. 
-       Locally made caramel cinnamon rolls are a favorite.
-       - **Address:** 1 Memorial Dr, Cambridge, MA 02142, USA
-       - **Tags:** Continental breakfast, free Wi-Fi, 24-hour front desk service
-       - **Room for 4:** Budget Room, 2 Queen Beds (City View) - $85.99
-    
-    4. **Gastronomic Landscape Hotel**
-       - **Description:** Known for its culinary excellence under the management of William Dough, 
-       offers continental breakfast.
-       - **Address:** 3393 Peachtree Rd, Atlanta, GA 30326, USA
-       - **Tags:** Restaurant, bar, continental breakfast
-       - **Room for 4:** Budget Room, 2 Queen Beds (Amenities) - $66.99
-    ...
-       - **Tags:** Pool, continental breakfast, free parking
-       - **Room for 4:** Budget Room, 2 Queen Beds (Amenities) - $60.99
-    
-    Enjoy your stay! Let me know if you need any more information.
-    ```
-
-## Troubleshooting errors
-
-To debug Azure SDK errors, set the environment variable `AZURE_LOG_LEVEL` to one of the following: `verbose`, `info`, `warning`, `error`. This will enable detailed logging for the Azure SDK, which can help identify [issues with authentication](https://github.com/Azure/azure-sdk-for-js/blob/main/sdk/identity/identity/TROUBLESHOOTING.md#enable-and-configure-logging), network connectivity, or other problems.
-
-Rerun the query script. You should now get informational statements from the SDKs in the output that provide more detail about any issues.
-
-If you see output messages 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.
-
-## 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.

@@ -1,448 +0,0 @@
----
-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/pricing/purchase-options/azure-account?cid=msft_learn).
-
-- 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 Microsoft 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) 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).
-
-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 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.
-
-## Update the hotels-sample-index
-
-A search index provides grounding data for the chat model. We recommend the **hotels-sample-index**.
-
-1. In the Azure portal, [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices).
-
-1. Follow the instructions in [this quickstart](../../search-import-data-portal.md) to create the index.
-
-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/`.
-
-## Create a virtual environment
-
-In this step, switch back to your local system and Visual Studio Code. We recommend that you create a virtual environment so that you can install the dependencies in isolation.
-
-1. In Visual Studio Code, open the folder containing Quickstart-RAG.ipynb.
-
-1. Press Ctrl-shift-P to open the command palette, search for "Python: Create Environment", and then select `Venv` to create a virtual environment in the current workspace.
-
-1. Select Quickstart-RAG\requirements.txt for the dependencies.
-
-It takes several minutes to create the environment. When the environment is ready, continue to the next step.
-
-## 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 query and chat thread
-
-This section uses Visual Studio Code and Python to call the chat completion APIs on Azure OpenAI.
-
-1. Start Visual Studio Code and [open the .ipynb file](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-RAG) or create a new Python file.
-
-1. Install the following Python packages.
-
-   ```python
-   ! pip install azure-search-documents==11.6.0b5 --quiet
-   ! pip install azure-identity==1.16.1 --quiet
-   ! pip install openai --quiet
-   ! pip install aiohttp --quiet
-   ! pip install ipykernel --quiet
-   ```
-
-1. Set the following variables, substituting placeholders with the endpoints you collected in the previous step. 
-
-   ```python
-    AZURE_SEARCH_SERVICE: str = "PUT YOUR SEARCH SERVICE ENDPOINT HERE"
-    AZURE_OPENAI_ACCOUNT: str = "PUT YOUR AZURE OPENAI ENDPOINT HERE"
-    AZURE_DEPLOYMENT_MODEL: str = "gpt-4o"
-   ```
-
-1. Set up clients, the prompt, query, and response.
-
-   For the Azure Government cloud, modify the API endpoint on the token provider to `"https://cognitiveservices.azure.us/.default"`.
-
-   ```python
-   # Set up the query for generating responses
-    from azure.identity import DefaultAzureCredential
-    from azure.identity import get_bearer_token_provider
-    from azure.search.documents import SearchClient
-    from openai import AzureOpenAI
-    
-    credential = DefaultAzureCredential()
-    token_provider = get_bearer_token_provider(credential, "https://cognitiveservices.azure.com/.default")
-    openai_client = AzureOpenAI(
-        api_version="2024-06-01",
-        azure_endpoint=AZURE_OPENAI_ACCOUNT,
-        azure_ad_token_provider=token_provider
-    )
-    
-    search_client = SearchClient(
-        endpoint=AZURE_SEARCH_SERVICE,
-        index_name="hotels-sample-index",
-        credential=credential
-    )
-    
-    # This prompt provides instructions to the model
-    GROUNDED_PROMPT="""
-    You are a friendly assistant that recommends hotels based on activities and amenities.
-    Answer the query using only the sources provided below in a friendly and concise bulleted manner.
-    Answer ONLY with the facts listed in the list of sources below.
-    If there isn't enough information below, say you don't know.
-    Do not generate answers that don't use the sources below.
-    Query: {query}
-    Sources:\n{sources}
-    """
-    
-    # Query is the question being asked. It's sent to the search engine and the chat model
-    query="Can you recommend a few hotels with complimentary breakfast?"
-    
-    # Search results are created by the search client
-    # Search results are composed of the top 5 results and the fields selected from the search index
-    # Search results include the top 5 matches to your query
-    search_results = search_client.search(
-        search_text=query,
-        top=5,
-        select="Description,HotelName,Tags"
-    )
-    sources_formatted = "\n".join([f'{document["HotelName"]}:{document["Description"]}:{document["Tags"]}' for document in search_results])
-    
-    # Send the search results and the query to the LLM to generate a response based on the prompt.
-    response = openai_client.chat.completions.create(
-        messages=[
-            {
-                "role": "user",
-                "content": GROUNDED_PROMPT.format(query=query, sources=sources_formatted)
-            }
-        ],
-        model=AZURE_DEPLOYMENT_MODEL
-    )
-    
-    # Here is the response from the chat model.
-    print(response.choices[0].message.content)
-    ```
-
-    Output is from Azure OpenAI, and it consists of recommendations for several hotels. Here's an example of what the output might look like:
-
-    ```
-   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
-    ```
-
-    If you get a **Forbidden** error message, check Azure AI Search configuration to make sure role-based access is enabled.
-
-    If you get an **Authorization failed** error message, wait a few minutes and try again. It can take several minutes for role assignments to become operational.
-
-    If you get a **Resource not found** error message, check the resource URIs and make sure the API version on the chat model is valid.
-
-    Otherwise, 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.
-
-    You might also try the query without semantic ranking by setting `use_semantic_reranker=False` in the query parameters step. Semantic ranking can noticably improve the relevance of query results and the ability of the LLM to return useful information. Experimentation can help you decide whether it makes a difference for your content.
-
-## 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. The following example adds complex types to the request. The formatting instructions include a JSON specification.
-
-```python
-import json
-
-# Query is the question being asked. It's sent to the search engine and the LLM.
-query="Can you recommend a few hotels that offer complimentary breakfast? 
-Tell me their description, address, tags, and the rate for one room that sleeps 4 people."
-
-# Set up the search results and the chat thread.
-# Retrieve the selected fields from the search index related to the question.
-selected_fields = ["HotelName","Description","Address","Rooms","Tags"]
-search_results = search_client.search(
-    search_text=query,
-    top=5,
-    select=selected_fields,
-    query_type="semantic"
-)
-sources_filtered = [{field: result[field] for field in selected_fields} for result in search_results]
-sources_formatted = "\n".join([json.dumps(source) for source in sources_filtered])
-
-response = openai_client.chat.completions.create(
-    messages=[
-        {
-            "role": "user",
-            "content": GROUNDED_PROMPT.format(query=query, sources=sources_formatted)
-        }
-    ],
-    model=AZURE_DEPLOYMENT_MODEL
-)
-
-print(response.choices[0].message.content)
-```
-
-Output is from Azure OpenAI, and it adds content from complex types.
-
-```
-Here are a few hotels that offer complimentary breakfast and have rooms that sleep 4 people:
-
-1. **Head Wind Resort**
-   - **Description:** The best of old town hospitality combined with views of the river and 
-   cool breezes off the prairie. Enjoy a complimentary continental breakfast in the lobby, 
-   and free Wi-Fi throughout the hotel.
-   - **Address:** 7633 E 63rd Pl, Tulsa, OK 74133, USA
-   - **Tags:** Coffee in lobby, free Wi-Fi, view
-   - **Room for 4:** Suite, 2 Queen Beds (Amenities) - $254.99
-
-2. **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. Offers continental breakfast.
-   - **Address:** 2211 Elliott Ave, Seattle, WA 98121, USA
-   - **Tags:** View, pool, restaurant, bar, continental breakfast
-   - **Room for 4:** Suite, 2 Queen Beds (Amenities) - $254.99
-
-3. **Swan Bird Lake Inn**
-   - **Description:** Continental-style breakfast featuring a variety of food and drinks. 
-   Locally made caramel cinnamon rolls are a favorite.
-   - **Address:** 1 Memorial Dr, Cambridge, MA 02142, USA
-   - **Tags:** Continental breakfast, free Wi-Fi, 24-hour front desk service
-   - **Room for 4:** Budget Room, 2 Queen Beds (City View) - $85.99
-
-4. **Gastronomic Landscape Hotel**
-   - **Description:** Known for its culinary excellence under the management of William Dough, 
-   offers continental breakfast.
-   - **Address:** 3393 Peachtree Rd, Atlanta, GA 30326, USA
-   - **Tags:** Restaurant, bar, continental breakfast
-   - **Room for 4:** Budget Room, 2 Queen Beds (Amenities) - $66.99
-...
-   - **Tags:** Pool, continental breakfast, free parking
-   - **Room for 4:** Budget Room, 2 Queen Beds (Amenities) - $60.99
-
-Enjoy your stay! Let me know if you need any more information.
-```
-
-## Troubleshooting errors
-
-To debug authentication errors, insert the following code before the step that calls the search engine and the LLM.
-
-```python
-import sys
-import logging # Set the logging level for all azure-storage-* libraries
-logger = logging.getLogger('azure.identity') 
-logger.setLevel(logging.DEBUG)
-
-handler = logging.StreamHandler(stream=sys.stdout)
-formatter = logging.Formatter('[%(levelname)s %(name)s] %(message)s')
-handler.setFormatter(formatter)
-logger.addHandler(handler)
-```
-
-Rerun the query script. You should now get INFO and DEBUG statements in the output that provide more detail about the issue.
-
-If you see output messages 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.
-
-## 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.
-

@@ -1,266 +0,0 @@
----
-manager: nitinme
-author: heidisteen
-ms.author: haileytapia
-ms.service: azure-ai-search
-ms.topic: include
-ms.date: 08/27/2025
----
-
-## Prerequisites
-
-- An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn).
-
-- 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 Microsoft 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/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=2025-11-01-preview&$select=name  HTTP/1.1
-    Authorization: Bearer {{personalAccessToken}}
-   ```
-
-1. Select **Send 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.

@@ -1,441 +0,0 @@
----
-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/pricing/purchase-options/azure-account?cid=msft_learn).
-
-- 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 Microsoft 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).
-- [Node.JS with LTS](https://nodejs.org/en/download/).
-- [TypeScript](https://www.typescriptlang.org/download). You can globally install TypeScript using npm:
-
-   ```bash
-   npm install -g typescript
-   ```
-
-
-## 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**.
-
-1. In the Azure portal, [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices).
-
-1. Follow the instructions in [this quickstart](../../search-import-data-portal.md) to create the index.
-
-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/`.
-
-## Set up environment variables for local development
-
-1. Create a `.env` file.
-1. Add the following environment variables to the `.env` file, replacing the values with your own service endpoints and keys.
-
-   ```plaintext
-   AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
-   AZURE_SEARCH_INDEX_NAME=hotels-sample-index
-
-   AZURE_OPENAI_ENDPOINT=<YOUR AZURE OPENAI ENDPOINT>
-   AZURE_OPENAI_VERSION=<YOUR AZURE OPENAI API VERSION>
-   AZURE_DEPLOYMENT_MODEL=<YOUR DEPLOYMENT NAME>
-   ```
-
-## Set up the Node.JS project
-
-Set up project with Visual Studio Code and TypeScript.
-
-1. Start Visual Studio Code in a new directory.
-
-   ```bash
-   mkdir rag-quickstart && cd rag-quickstart
-   code .
-   ```
-1. Create a new package for ESM modules in your project directory.
-
-   ```bash
-   npm init -y
-   npm pkg set type=module
-   ```
-
-   This creates a `package.json` file with default values.
-
-1. Install the following npm packages.
-
-   ```bash
-   npm install @azure/identity @azure/search-documents openai dotenv @types/node
-   ``` 
-
-1. Create a `src` directory in your project directory.
-
-   ```bash
-   mkdir src
-   ```
-
-1. Create a `tsconfig.json` file in the project directory for ESM with the following content.
-
-    ```json
-    {
-      "compilerOptions": {
-        "target": "esnext",
-        "module": "NodeNext",
-        "moduleResolution": "nodenext",
-        "rootDir": "./src",
-        "outDir": "./dist/",
-        "esModuleInterop": true,
-        "forceConsistentCasingInFileNames": true,
-        "strict": true,
-        "skipLibCheck": true,
-        "declaration": true,
-        "sourceMap": true,
-        "resolveJsonModule": true,
-        "moduleDetection": "force", // Add this for ESM
-        "allowSyntheticDefaultImports": true // Helpful for ESM interop
-      },
-      "include": [
-        "src/**/*.ts"
-      ]
-    }
-   ```
-
-## 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 query and chat thread
-
-Create a query script that uses the Azure AI Search index and the chat model to generate responses based on grounding data. The following steps guide you through setting up the query script.
-
-
-1. Create a `query.ts` file in the `src` directory with the following code.
-
-    :::code language="typescript" source="~/azure-search-javascript-samples/quickstart-rag-ts/src/query.ts" :::
-
-
-
-    The preceding code does the following:
-    - Imports the necessary libraries for Azure AI Search and Azure OpenAI.
-    - Uses environment variables to configure the Azure AI Search and Azure OpenAI clients.
-    - Defines a function to get the clients for Azure AI Search and Azure OpenAI, using environment variables for configuration.
-    - Defines a function to query Azure AI Search for sources based on the user query.
-    - Defines a function to query Azure OpenAI for a response based on the user query and the sources retrieved from Azure AI Search.
-    - The `main` function orchestrates the flow by calling the search and OpenAI functions, and then prints the response.    
-    
-1. Build the TypeScript code to JavaScript.
-
-   ```bash
-   tsc
-   ```
-
-   This command compiles the TypeScript code in the `src` directory and outputs the JavaScript files in the `dist` directory.
-
-1. Run the following command in a terminal to execute the query script:
-
-    ```bash
-    node -r dotenv/config dist/query.js
-    ```
-
-    The `.env` is passed into the runtime using the `-r dotenv/config`. 
-
-1. View the output consists of recommendations for several hotels. Here's an example of what the output might look like:
-
-    ```
-    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
-    ```
-
-## Troubleshooting
-
-If you get a **Forbidden** error message, check Azure AI Search configuration to make sure role-based access is enabled.
-
-If you get an **Authorization failed** error message, wait a few minutes and try again. It can take several minutes for role assignments to become operational.
-
-If you get a **Resource not found** error message, check the resource URIs and make sure the API version on the chat model is valid.
-
-Otherwise, 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.
-
-You might also try the query without semantic ranking by setting `use_semantic_reranker=False` in the query parameters step. Semantic ranking can noticably improve the relevance of query results and the ability of the LLM to return useful information. Experimentation can help you decide whether it makes a difference for your content.
-
-## 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, change your prompt to include formatting instructions: 
-
-```text
-Can you recommend a few hotels that offer complimentary breakfast? 
-Tell me their description, address, tags, and the rate for one room that sleeps 4 people.
-```
-
-1. Create a new file `queryComplex.ts` in the `src` directory.
-1. Copy the following code to the file:
-
-    :::code language="typescript" source="~/azure-search-javascript-samples/quickstart-rag-ts/src/queryComplex.ts" :::
-
-1. Build the TypeScript code to JavaScript.
-
-   ```bash
-   tsc
-   ```
-
-   This command compiles the TypeScript code in the `src` directory and outputs the JavaScript files in the `dist` directory.
-
-1. Run the following command in a terminal to execute the query script:
-
-    ```bash
-    node -r dotenv/config dist/queryComplex.js
-    ```
-
-    The `.env` is passed into the runtime using the `-r dotenv/config`. 
-
-
-1. View the output from Azure OpenAI, and it adds content from complex types.
-
-```
-Here are a few hotels that offer complimentary breakfast and have rooms that sleep 4 people:
-
-1. **Head Wind Resort**
-   - **Description:** The best of old town hospitality combined with views of the river and 
-   cool breezes off the prairie. Enjoy a complimentary continental breakfast in the lobby, 
-   and free Wi-Fi throughout the hotel.
-   - **Address:** 7633 E 63rd Pl, Tulsa, OK 74133, USA
-   - **Tags:** Coffee in lobby, free Wi-Fi, view
-   - **Room for 4:** Suite, 2 Queen Beds (Amenities) - $254.99
-
-2. **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. Offers continental breakfast.
-   - **Address:** 2211 Elliott Ave, Seattle, WA 98121, USA
-   - **Tags:** View, pool, restaurant, bar, continental breakfast
-   - **Room for 4:** Suite, 2 Queen Beds (Amenities) - $254.99
-
-3. **Swan Bird Lake Inn**
-   - **Description:** Continental-style breakfast featuring a variety of food and drinks. 
-   Locally made caramel cinnamon rolls are a favorite.
-   - **Address:** 1 Memorial Dr, Cambridge, MA 02142, USA
-   - **Tags:** Continental breakfast, free Wi-Fi, 24-hour front desk service
-   - **Room for 4:** Budget Room, 2 Queen Beds (City View) - $85.99
-
-4. **Gastronomic Landscape Hotel**
-   - **Description:** Known for its culinary excellence under the management of William Dough, 
-   offers continental breakfast.
-   - **Address:** 3393 Peachtree Rd, Atlanta, GA 30326, USA
-   - **Tags:** Restaurant, bar, continental breakfast
-   - **Room for 4:** Budget Room, 2 Queen Beds (Amenities) - $66.99
-...
-   - **Tags:** Pool, continental breakfast, free parking
-   - **Room for 4:** Budget Room, 2 Queen Beds (Amenities) - $60.99
-
-Enjoy your stay! Let me know if you need any more information.
-```
-
-## Troubleshooting errors
-
-To debug Azure SDK errors, set the environment variable `AZURE_LOG_LEVEL` to one of the following: `verbose`, `info`, `warning`, `error`. This will enable detailed logging for the Azure SDK, which can help identify [issues with authentication](https://github.com/Azure/azure-sdk-for-js/blob/main/sdk/identity/identity/TROUBLESHOOTING.md#enable-and-configure-logging), network connectivity, or other problems.
-
-Rerun the query script. You should now get informational statements from the SDKs in the output that provide more detail about any issues.
-
-If you see output messages 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.
-
-## 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.

@@ -297,7 +297,7 @@ In the following table, the token filters that are implemented using Apache Luce
 |[shingle](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/shingle/ShingleFilter.html)|ShingleTokenFilter|Creates combinations of tokens as a single token.<br><br> **Options**<br><br> maxShingleSize (type: int) - Defaults to 2.<br><br> minShingleSize (type: int) - Defaults to 2.<br><br> outputUnigrams (type: bool) - if true, the output stream contains the input tokens (unigrams) as well as shingles. The default is true.<br><br> outputUnigramsIfNoShingles (type: bool) - If true, override the behavior of outputUnigrams==false for those times when no shingles are available. The default is false.<br><br> tokenSeparator (type: string) - The string to use when joining adjacent tokens to form a shingle. The default is a single empty space ` `. <br><br> filterToken (type: string) - The string to insert for each position for which there's no token. The default is `_`.|  
 |[snowball](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/snowball/SnowballFilter.html)|SnowballTokenFilter|Snowball Token Filter.<br><br> **Options**<br><br> language (type: string) - Allowed values include: `armenian`, `basque`, `catalan`, `danish`, `dutch`, `english`, `finnish`, `french`, `german`, `german2`, `hungarian`, `italian`, `kp`, `lovins`, `norwegian`, `porter`, `portuguese`, `romanian`, `russian`, `spanish`, `swedish`, `turkish`|  
 |[sorani_normalization](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/ckb/SoraniNormalizationFilter.html)|SoraniNormalizationTokenFilter|Normalizes the Unicode representation of `Sorani` text.<br><br> **Options**<br><br> None.|  
-|stemmer|StemmerTokenFilter|Language-specific stemming filter.<br><br> **Options**<br><br> language (type: string) - Allowed values include: <br> -   [`arabic`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/ar/ArabicStemmer.html)<br>-   [`armenian`](https://snowballstem.org/algorithms/armenian/stemmer.html)<br>-   [`basque`](https://snowballstem.org/algorithms/basque/stemmer.html)<br>-   [`brazilian`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/br/BrazilianStemmer.html)<br>-   `bulgarian`<br>-   [`catalan`](https://snowballstem.org/algorithms/catalan/stemmer.html)<br>-   [`czech`](https://portal.acm.org/citation.cfm?id=1598600)<br>-   [`danish`](https://snowballstem.org/algorithms/danish/stemmer.html)<br>-   [`dutch`](https://snowballstem.org/algorithms/dutch/stemmer.html)<br>-   [`dutchKp`](https://snowballstem.org/algorithms/kraaij_pohlmann/stemmer.html)<br>-   [`english`](https://snowballstem.org/algorithms/porter/stemmer.html)<br>-   [`lightEnglish`](https://ciir.cs.umass.edu/pubfiles/ir-35.pdf)<br>-   [`minimalEnglish`](https://www.researchgate.net/publication/220433848_How_effective_is_suffixing)<br>-   [`possessiveEnglish`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/en/EnglishPossessiveFilter.html)<br>-   [`porter2`](https://snowballstem.org/algorithms/english/stemmer.html)<br>-   [`lovins`](https://snowballstem.org/algorithms/lovins/stemmer.html)<br>-   [`finnish`](https://snowballstem.org/algorithms/finnish/stemmer.html)<br>-   `lightFinnish`<br>-   [`french`](https://snowballstem.org/algorithms/french/stemmer.html)<br>-   [`lightFrench`](https://dl.acm.org/citation.cfm?id=1141523)<br>-   [`minimalFrench`](https://dl.acm.org/citation.cfm?id=318984)<br>-   `galician`<br>-   `minimalGalician`<br>-   [`german`](https://snowballstem.org/algorithms/german/stemmer.html)<br>-   [`german2`](https://snowballstem.org/algorithms/german2/stemmer.html)<br>-   [`lightGerman`](https://dl.acm.org/citation.cfm?id=1141523)<br>-   `minimalGerman`<br>-   [`greek`](https://sais.se/mthprize/2007/ntais2007.pdf)<br>-   `hindi`<br>-   [`hungarian`](https://snowballstem.org/algorithms/hungarian/stemmer.html)<br>-   [`lightHungarian`](https://dl.acm.org/citation.cfm?id=1141523&dl=ACM&coll=DL&CFID=179095584&CFTOKEN=80067181)<br>-   [`indonesian`](https://eprints.illc.uva.nl/741/2/MoL-2003-03.text.pdf)<br>-   [`irish`](https://snowballstem.org/algorithms/irish/stemmer.html)<br>-   [`italian`](https://snowballstem.org/algorithms/italian/stemmer.html)<br>-   [`lightItalian`](https://www.ercim.eu/publication/ws-proceedings/CLEF2/savoy.pdf)<br>-   [`sorani`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/ckb/SoraniStemmer.html)<br>-   [`latvian`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/lv/LatvianStemmer.html)<br>-   [`norwegian`](https://snowballstem.org/algorithms/norwegian/stemmer.html)<br>-   [`lightNorwegian`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/no/NorwegianLightStemmer.html)<br>-   [`minimalNorwegian`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/no/NorwegianMinimalStemmer.html)<br>-   [`lightNynorsk`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/no/NorwegianLightStemmer.html)<br>-   [`minimalNynorsk`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/no/NorwegianMinimalStemmer.html)<br>-   [`portuguese`](https://snowballstem.org/algorithms/portuguese/stemmer.html)<br>-   [`lightPortuguese`](https://dl.acm.org/citation.cfm?id=1141523&dl=ACM&coll=DL&CFID=179095584&CFTOKEN=80067181)<br>-   [`minimalPortuguese`](https://web.archive.org/web/20230425141918/https://www.inf.ufrgs.br/~buriol/papers/Orengo_CLEF07.pdf)<br>-   [`portugueseRslp`](https://web.archive.org/web/20230422082818/https://www.inf.ufrgs.br/~viviane/rslp/index.htm)<br>-   [`romanian`](https://snowballstem.org/otherapps/romanian/)<br>-   [`russian`](https://snowballstem.org/algorithms/russian/stemmer.html)<br>-   [`lightRussian`](https://doc.rero.ch/lm.php?url=1000%2C43%2C4%2C20091209094227-CA%2FDolamic_Ljiljana_-_Indexing_and_Searching_Strategies_for_the_Russian_20091209.pdf)<br>-   [`spanish`](https://snowballstem.org/algorithms/spanish/stemmer.html)<br>-   [`lightSpanish`](https://www.ercim.eu/publication/ws-proceedings/CLEF2/savoy.pdf)<br>-   [`swedish`](https://snowballstem.org/algorithms/swedish/stemmer.html)<br>-   `lightSwedish`<br>-   [`turkish`](https://snowballstem.org/algorithms/turkish/stemmer.html)|  
+|stemmer|StemmerTokenFilter|Language-specific stemming filter.<br><br> **Options**<br><br> language (type: string) - Allowed values include: <br> -   [`arabic`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/ar/ArabicStemmer.html)<br>-   [`armenian`](https://snowballstem.org/algorithms/armenian/stemmer.html)<br>-   [`basque`](https://snowballstem.org/algorithms/basque/stemmer.html)<br>-   [`brazilian`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/br/BrazilianStemmer.html)<br>-   `bulgarian`<br>-   [`catalan`](https://snowballstem.org/algorithms/catalan/stemmer.html)<br>-   [`czech`](https://portal.acm.org/citation.cfm?id=1598600)<br>-   [`danish`](https://snowballstem.org/algorithms/danish/stemmer.html)<br>-   [`dutch`](https://snowballstem.org/algorithms/dutch/stemmer.html)<br>-   [`dutchKp`](https://snowballstem.org/algorithms/kraaij_pohlmann/stemmer.html)<br>-   [`english`](https://snowballstem.org/algorithms/porter/stemmer.html)<br>-   [`lightEnglish`](https://ciir.cs.umass.edu/pubfiles/ir-35.pdf)<br>-   [`minimalEnglish`](https://www.researchgate.net/publication/220433848_How_effective_is_suffixing)<br>-   [`possessiveEnglish`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/en/EnglishPossessiveFilter.html)<br>-   [`porter2`](https://snowballstem.org/algorithms/english/stemmer.html)<br>-   [`lovins`](https://snowballstem.org/algorithms/lovins/stemmer.html)<br>-   [`finnish`](https://snowballstem.org/algorithms/finnish/stemmer.html)<br>-   `lightFinnish`<br>-   [`french`](https://snowballstem.org/algorithms/french/stemmer.html)<br>-   [`lightFrench`](https://dl.acm.org/citation.cfm?id=1141523)<br>-   [`minimalFrench`](https://dl.acm.org/citation.cfm?id=318984)<br>-   `galician`<br>-   `minimalGalician`<br>-   [`german`](https://snowballstem.org/algorithms/german/stemmer.html)<br>-   [`german2`](https://snowballstem.org/algorithms/german2/stemmer.html)<br>-   [`lightGerman`](https://dl.acm.org/citation.cfm?id=1141523)<br>-   `minimalGerman`<br>-   [`greek`](https://snowballstem.org/algorithms/greek/stemmer.html)<br>-   `hindi`<br>-   [`hungarian`](https://snowballstem.org/algorithms/hungarian/stemmer.html)<br>-   [`lightHungarian`](https://dl.acm.org/citation.cfm?id=1141523&dl=ACM&coll=DL&CFID=179095584&CFTOKEN=80067181)<br>-   [`indonesian`](https://eprints.illc.uva.nl/741/2/MoL-2003-03.text.pdf)<br>-   [`irish`](https://snowballstem.org/algorithms/irish/stemmer.html)<br>-   [`italian`](https://snowballstem.org/algorithms/italian/stemmer.html)<br>-   [`lightItalian`](https://www.ercim.eu/publication/ws-proceedings/CLEF2/savoy.pdf)<br>-   [`sorani`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/ckb/SoraniStemmer.html)<br>-   [`latvian`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/lv/LatvianStemmer.html)<br>-   [`norwegian`](https://snowballstem.org/algorithms/norwegian/stemmer.html)<br>-   [`lightNorwegian`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/no/NorwegianLightStemmer.html)<br>-   [`minimalNorwegian`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/no/NorwegianMinimalStemmer.html)<br>-   [`lightNynorsk`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/no/NorwegianLightStemmer.html)<br>-   [`minimalNynorsk`](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/no/NorwegianMinimalStemmer.html)<br>-   [`portuguese`](https://snowballstem.org/algorithms/portuguese/stemmer.html)<br>-   [`lightPortuguese`](https://dl.acm.org/citation.cfm?id=1141523&dl=ACM&coll=DL&CFID=179095584&CFTOKEN=80067181)<br>-   [`minimalPortuguese`](https://web.archive.org/web/20230425141918/https://www.inf.ufrgs.br/~buriol/papers/Orengo_CLEF07.pdf)<br>-   [`portugueseRslp`](https://web.archive.org/web/20230422082818/https://www.inf.ufrgs.br/~viviane/rslp/index.htm)<br>-   [`romanian`](https://snowballstem.org/otherapps/romanian/)<br>-   [`russian`](https://snowballstem.org/algorithms/russian/stemmer.html)<br>-   [`lightRussian`](https://doc.rero.ch/lm.php?url=1000%2C43%2C4%2C20091209094227-CA%2FDolamic_Ljiljana_-_Indexing_and_Searching_Strategies_for_the_Russian_20091209.pdf)<br>-   [`spanish`](https://snowballstem.org/algorithms/spanish/stemmer.html)<br>-   [`lightSpanish`](https://www.ercim.eu/publication/ws-proceedings/CLEF2/savoy.pdf)<br>-   [`swedish`](https://snowballstem.org/algorithms/swedish/stemmer.html)<br>-   `lightSwedish`<br>-   [`turkish`](https://snowballstem.org/algorithms/turkish/stemmer.html)|  
 |[stemmer_override](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/miscellaneous/StemmerOverrideFilter.html)|StemmerOverrideTokenFilter|Any dictionary-Stemmed terms are marked as keywords, which prevents stemming down the chain. Must be placed before any stemming filters.<br><br> **Options**<br><br> rules (type: string array) - Stemming rules in the following format `word => stem` for example `ran => run`. The default is an empty list.  Required.|  
 |[stopwords](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/core/StopFilter.html)|StopwordsTokenFilter|Removes stop words from a token stream. By default, the filter uses a predefined stop word list for English.<br><br> **Options**<br><br> stopwords (type: string array) - A list of stopwords. Can't be specified if a stopwordsList is specified.<br><br> stopwordsList (type: string) - A predefined list of stopwords. Can't be specified if `stopwords` is specified. Allowed values include:`arabic`, `armenian`, `basque`, `brazilian`, `bulgarian`, `catalan`, `czech`, `danish`, `dutch`, `english`, `finnish`, `french`, `galician`, `german`, `greek`, `hindi`, `hungarian`, `indonesian`, `irish`, `italian`, `latvian`, `norwegian`, `persian`, `portuguese`, `romanian`, `russian`, `sorani`, `spanish`, `swedish`, `thai`, `turkish`, default: `english`. Can't be specified if `stopwords` is specified. <br><br> ignoreCase (type: bool) - If true, all words are lower cased first. The default is false.<br><br> removeTrailing (type: bool) - If true, ignore the last search term if it's a stop word. The default is true.
 |[synonym](https://lucene.apache.org/core/6_6_1/analyzers-common/org/apache/lucene/analysis/synonym/SynonymFilter.html)|SynonymTokenFilter|Matches single or multi word synonyms in a token stream.<br><br> **Options**<br><br> synonyms (type: string array) - Required. List of synonyms in one of the following two formats:<br><br> -incredible, unbelievable, fabulous => amazing - all terms on the left side of => symbol are replaced with all terms on its right side.<br><br> -incredible, unbelievable, fabulous, amazing - A comma-separated list of equivalent words. Set the expand option to change how this list is interpreted.<br><br> ignoreCase (type: bool) - Case-folds input for matching. The default is false.<br><br> expand (type: bool) - If true, all words in the list of synonyms (if => notation isn't used) map to one another. <br>The following list: incredible, unbelievable, fabulous, amazing is equivalent to: incredible, unbelievable, fabulous, amazing => incredible, unbelievable, fabulous, amazing<br><br>- If false, the following list: incredible, unbelievable, fabulous, amazing are equivalent to: incredible, unbelievable, fabulous, amazing => incredible.|  

@@ -1,94 +1,118 @@
 ### YamlMime:Landing
 
 title: Azure AI Search documentation # < 60 chars
-summary: Information retrieval at scale for agentic retrieval, with vector and text content in traditional or generative search scenarios.
+summary: Learn how to use Azure AI Search for information retrieval at scale, with support for text, vector, and multimodal content in traditional and generative search scenarios.
 metadata:
   title: Azure AI Search documentation
-  description: Information retrieval at scale for agentic retrieval, with vector and text content in traditional or generative search scenarios.
+  description: Learn how to use Azure AI Search for information retrieval at scale, with support for text, vector, and multimodal content in traditional and generative search scenarios.
   author: HeidiSteen
   ms.author: heidist
-  ms.date: 09/23/2025
+  ms.date: 12/12/2025
   ms.service: azure-ai-search
   ms.topic: landing-page
   ms.custom:
     - ignite-2023
     - ignite-2024
     - build-2025
-# linkListType: architecture | concept | deploy | download | get-started | how-to-guide | learn | overview | quickstart | reference | tutorial | video | whats-new
+# linkListType: architecture | concept | deploy | download | get-started | how-to-guide | tutorial | overview | quickstart | reference | sample | tutorial | video | whats-new
 
 landingContent:
 # Cards and links should be based on top customer tasks or top subjects
 # Start card title with a verb
   # Card
-  - title: About Azure AI Search
+  - title: Get started
     linkLists:
-      - linkListType: concept
+      - linkListType: overview
         links:
-          - text: What is Azure AI Search?
+          - text: What's Azure AI Search?
             url: search-what-is-azure-search.md
-          - text: AI enrichment
-            url: cognitive-search-concept-intro.md
-          - text: Semantic ranking
-            url: semantic-search-overview.md
+          - text: Features
+            url: search-features-list.md
+          - text: FAQ
+            url: search-faq-frequently-asked-questions.yml
       - linkListType: how-to-guide
         links:
           - text: Create a search service
             url: search-create-service-portal.md
           - text: Configure a search service
             url: search-manage.md
-          - text: Create a search index
-            url: search-get-started-portal.md
-          - text: Add vectors to an index
-            url: search-get-started-vector.md
-          - text: Query a vector index
-            url: vector-search-how-to-query.md
 
   # Card
-  - title: Agentic retrieval and vectors
+  - title: Agentic retrieval (RAG)
     linkLists:
       - linkListType: concept
         links:
-          - text: Retrieval Augmented Generation (RAG)
-            url: retrieval-augmented-generation-overview.md
           - text: Agentic retrieval
-            url: agentic-retrieval-overview.md        
+            url: agentic-retrieval-overview.md
+      - linkListType: quickstart
+        links:
+          - text: Use agentic retrieval
+            url: search-get-started-agentic-retrieval.md
+      - linkListType: how-to-guide
+        links:
+          - text: Create a knowledge base
+            url: agentic-retrieval-how-to-create-knowledge-base.md
+          - text: Retrieve from a knowledge base
+            url: agentic-retrieval-how-to-retrieve.md
+
+  # Card
+  - title: Classic search
+    linkLists:
+      - linkListType: concept
+        links:
+          - text: Full-text search
+            url: search-lucene-query-architecture.md
           - text: Vector search
             url: vector-search-overview.md
+          - text: Hybrid search
+            url: hybrid-search-overview.md
           - text: Multimodal search
             url: multimodal-search-overview.md
-          - text: Built-in vectorization
-            url: vector-search-integrated-vectorization.md
-          - text: Built-in compression
-            url: vector-search-how-to-quantization.md
       - linkListType: quickstart
         links:
-          - text: 'Classic RAG'
-            url: search-get-started-agentic-retrieval.md
-          - text: 'Agentic retrieval'
-            url: search-get-started-agentic-retrieval.md
-
-
+          - text: Use full-text search
+            url: search-get-started-text.md
+          - text: Use vector search
+            url: search-get-started-vector.md
+      - linkListType: how-to-guide
+        links:
+          - text: Create a search index
+            url: search-how-to-create-search-index.md
+          - text: Create a vector index
+            url: vector-search-how-to-create-index.md
+          - text: Query a vector index
+            url: vector-search-how-to-query.md
 
   # Card
-  - title: Code
+  - title: Code samples
     linkLists:
-      - linkListType: get-started
+      - linkListType: sample
         links:
-          - text: Python samples
-            url: samples-python.md
           - text: .NET samples
             url: samples-dotnet.md
+          - text: Java samples
+            url: samples-java.md
+          - text: JavaScript samples
+            url: samples-javascript.md
+          - text: Python samples
+            url: samples-python.md
+          - text: REST samples
+            url: samples-rest.md
           - text: Vector samples
             url: https://github.com/Azure/azure-search-vector-samples
+
+  # Card
+  - title: References
+    linkLists:
       - linkListType: reference
         links:
-          - text: Azure REST API Reference
-            url: /rest/api/searchservice/
           - text: Azure SDK for .NET
             url: /dotnet/api/overview/azure/search.documents-readme
-          - text: Azure SDK for Python
-            url: /python/api/overview/azure/search-documents-readme
           - text: Azure SDK for Java
             url: /java/api/overview/azure/search-documents-readme
           - text: Azure SDK for JavaScript
             url: /javascript/api/overview/azure/search-documents-readme
+          - text: Azure SDK for Python
+            url: /python/api/overview/azure/search-documents-readme
+          - text: Azure REST API
+            url: /rest/api/searchservice/
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