Merge branch 'main' into release-aisvcs-move-rai-docs

Author: v-alje

articles/ai-foundry/what-is-azure-ai-foundry.md

@@ -6,7 +6,7 @@ author: sdgilley
 ms.author: sgilley
 manager: scottpolly
 ms.reviewer: sgilley
-ms.date: 05/12/2025
+ms.date: 06/12/2025
 ms.service: azure-ai-foundry
 ms.topic: overview
 ms.custom:
@@ -58,14 +58,16 @@ This table summarizes features available in the two project types:
 | Capability | [!INCLUDE [fdp](includes/fdp-project-name.md)] | [!INCLUDE[hub](includes/hub-project-name.md)] |
 | --- | --- | --- |
 | Agents | ✅ (GA) | ✅ (Preview only) |
-| Azure AI Foundry Models including Azure OpenAI models | ✅ (Native support) | Available via connections |
 | AI Foundry API to work with agents and across models| ✅ (Native support) | Available via connections |
-| Project files (directly upload files and start experimenting) | ✅ | |
-| Project-level isolation of files and outputs | ✅ | ✅|
+| Models sold directly by Azure - Azure OpenAI, DeepSeek, xAI, etc. | ✅ | Available via connections |
+| Partner & Community Models sold through Marketplace - Stability, Bria, Cohere, etc. | ✅ | Available via connections |
+| Open source models e.g. HuggingFace  |  | ✅ |
 | Evaluations | ✅ | ✅ |
 | Playground | ✅ | ✅ |
 | Prompt flow |  | ✅ |
-| Managed compute  |  | ✅ |
+| Content understanding  |  | ✅ |
+| Project files (directly upload files and start experimenting) | ✅ | |
+| Project-level isolation of files and outputs | ✅ | ✅|
 | Required Azure dependencies | - | Azure Storage account, Azure Key Vault |
 
 ### <a name="how-do-i-know"></a> How do I know which type of project I have?

articles/ai-services/containers/includes/cognitive-services-container-security.md

@@ -36,6 +36,10 @@ The host should allowlist **port 443** and the following domains:
 * `*.cognitive.microsoft.com`
 * `*.cognitiveservices.azure.com`
 
+If you are using the Azure AI Translator on-premise, you need to additionally allow the following URLs to download files
+
+* `translatoronprem.blob.core.windows.net`
+
 #### Disable deep packet inspection
 
 [Deep packet inspection (DPI)](https://en.wikipedia.org/wiki/Deep_packet_inspection) is a type of data processing that inspects in detail the data sent over a computer network, and usually takes action by blocking, rerouting, or logging it accordingly.

articles/ai-services/includes/quickstarts/management-azportal.md

@@ -24,7 +24,7 @@ keywords:
 
 ## Create a new Azure AI Foundry resource
 
-AI Foundry portal provides a way to create a new Azure resource with basic,  defaulted, settings. If your organization requires customized Azure configurations like alternative names, security controls or cost tags, you may have to use Azure portal or [template options](../../../ai-foundry/how-to/create-resource-template.md) to comply with your organization's Azure Policy compliance.
+[Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs) provides a way to create a new Azure resource with basic,  defaulted, settings. If your organization requires customized Azure configurations like alternative names, security controls or cost tags, you may need to instead use [Azure portal](https://portal.azure.com) or [template options](../../../ai-foundry/how-to/create-resource-template.md) to comply with your organization's Azure Policy compliance.
 
 The Azure AI Foundry multi-service resource is listed under **AI Foundry** > **AI Foundry** in the portal. The API kind is **AIServices**. Look for the logo as shown here:
 

articles/ai-services/openai/how-to/network-security-perimeter.md

@@ -269,6 +269,8 @@ items:
         href: encrypt-data-at-rest.md
       - name: Managed identity
         href: ./how-to/managed-identity.md
+      - name: Network security perimeter (preview)
+        href: ./how-to/network-security-perimeter.md
   - name: Service management
     items:
       - name: Resource creation & model deployment

articles/ai-services/openai/toc.yml

@@ -4,7 +4,7 @@ author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: include
-ms.date: 05/30/2025
+ms.date: 6/15/2025
 ---
 
 [!INCLUDE [Feature preview](../previews/preview-generic.md)]
@@ -22,94 +22,26 @@ Although you can provide your own data, this quickstart uses [sample JSON docume
 
 + An [Azure AI Search service](../../search-create-service-portal.md) on the Basic tier or higher with [semantic ranker enabled](../../semantic-how-to-enable-disable.md).
 
-+ An [Azure OpenAI resource](/azure/ai-services/openai/how-to/create-resource).
++ An [Azure AI Foundry project](/azure/ai-foundry/how-to/create-projects). You get an Azure AI Foundry resource (that's needed for model deployments) when you create an Azure AI Foundry project.
 
 + [Visual Studio Code](https://code.visualstudio.com/download) with a [REST client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client).
 
-## Deploy models
++ The [Azure CLI](/cli/azure/install-azure-cli) for keyless authentication with Microsoft Entra ID.
 
-To run agentic retrieval, you must deploy the following models to your Azure OpenAI resource:
-
-+ An LLM for query planning.
-
-+ An LLM for answer generation.
-
-+ (Optional) An embedding model for vector queries.
-
-Agentic retrieval [supports several models](../../search-agentic-retrieval-how-to-create.md#supported-models), but this quickstart assumes `gpt-4o-mini` for the query-planning LLM and `text-embedding-3-large` for the embedding model. To use the answer-generating LLM, see the Python version of this quickstart.
-
-To deploy the Azure OpenAI models:
-
-1. Sign in to the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs) and select your Azure OpenAI resource.
-
-1. From the left pane, select **Model catalog**.
-
-1. Select **gpt-4o-mini**, and then select **Use this model**.
-
-1. Specify a deployment name. To simplify your code, we recommend **gpt-4o-mini**.
-
-1. Accept the defaults.
-
-1. Select **Deploy**.
-
-1. Repeat the previous steps for **text-embedding-3-large**.
-
-## Configure role-based access
-
-Azure AI Search needs access to your Azure OpenAI models. For this task, you can use API keys or Microsoft Entra ID with role assignments. Keys are easier to start with, but roles are more secure. This quickstart assumes roles.
-
-To configure the recommended role-based access:
-
-1. Sign in to the [Azure portal](https://portal.azure.com/).
-
-1. [Enable role-based access](../../search-security-enable-roles.md) on your Azure AI Search service.
-
-1. [Create a system-assigned managed identity](../../search-howto-managed-identities-data-sources.md#create-a-system-managed-identity) on your Azure AI Search service.
-
-1. On your Azure AI Search service, [assign the following roles](../../search-security-rbac.md#how-to-assign-roles-in-the-azure-portal) to yourself.
-
-    + **Search Service Contributor**
-
-    + **Search Index Data Contributor**
-
-    + **Search Index Data Reader**
-
-1. On your Azure OpenAI resource, assign **Cognitive Services User** to the managed identity of your search service.
-
-## Get endpoints
-
-In your code, you specify the following endpoints to establish connections with Azure AI Search and Azure OpenAI. These steps assume that you configured role-based access in the previous section.
-
-To obtain your service endpoints:
-
-1. Sign in to the [Azure portal](https://portal.azure.com/).
-
-1. On your Azure AI Search service:
-
-    1. From the left pane, select **Overview**.
-
-    1. Copy the URL, which should be similar to `https://my-service.search.windows.net`.
-
-1. On your Azure OpenAI resource:
-
-    1. From the left pane, select **Resource Management** > **Keys and Endpoint**.
-
-    1. Copy the URL, which should be similar to `https://my-resource.openai.azure.com`.
+[!INCLUDE [Setup](./agentic-retrieval-setup.md)]
 
 ## Connect from your local system
 
 You configured role-based access to interact with Azure AI Search and Azure OpenAI. From the command line, use the Azure CLI to sign in to the same subscription and tenant for both services. For more information, see [Quickstart: Connect without keys](../../search-get-started-rbac.md).
 
 To connect from your local system:
 
-1. Run the following commands in sequence.
+1. Open a new terminal in Visual Studio Code and change to the directory where you want to save your files.
 
-    ```azurecli
-    az account show
+1. Run the following command and sign in with your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service and Azure AI Foundry project.
 
-    az account set --subscription <PUT YOUR SUBSCRIPTION ID HERE>
-    
-    az login --tenant <PUT YOUR TENANT ID HERE>
+    ```azurecli
+    az login
     ```
 
 1. To obtain your Microsoft Entra token, run the following command. You specify this value in the next section.
@@ -124,26 +56,27 @@ Before you send any requests, define credentials, endpoints, and deployment deta
 
 To load the connections:
 
-1. In Visual Studio Code, paste the following placeholders into a `.rest` or `.http` file.
+1. In Visual Studio Code create a `.rest` or `.http` file. For example, you can name the file `agentic-retrieval.rest`.
+1. Paste these placeholders into the new file:
 
     ```HTTP
     @baseUrl = PUT-YOUR-SEARCH-SERVICE-URL-HERE
     @token = PUT-YOUR-MICROSOFT-ENTRA-TOKEN-HERE
-    @aoaiBaseUrl = PUT-YOUR-AOAI-URL-HERE
-    @aoaiGptModel = gpt-4o-mini
-    @aoaiGptDeployment = gpt-4o-mini
+    @aoaiBaseUrl = PUT-YOUR-AI-FOUNDRY-URL-HERE
+    @aoaiGptModel = gpt-4.1-mini
+    @aoaiGptDeployment = gpt-4.1-mini
     @aoaiEmbeddingModel = text-embedding-3-large
     @aoaiEmbeddingDeployment = text-embedding-3-large
     @index-name = earth_at_night
     @agent-name = earth-search-agent
     @api-version = 2025-05-01-Preview
     ```
 
-1. Replace `@baseUrl` and `@aoaiBaseUrl` with the values you obtained in [Get endpoints](#get-endpoints).
+1. Set `@baseUrl` to your Azure AI Search endpoint, which looks like `https://<your-search-service-name>.search.windows.net.` Set `@aoaiBaseUrl` to your Azure AI Foundry endpoint, which looks like `https://<your-foundry-resource-name>.openai.azure.com.` You obtained both values in the [Get endpoints](#get-endpoints) section. 
 
 1. Replace `@token` with the Microsoft Entra token you obtained in [Connect from your local system](#connect-from-your-local-system).
 
-1. To verify the variables, send the following request.
+1. In the same file, enter and send the following HTTP request to verify that you can connect to Azure AI Search. The request lists existing indexes in your search service.
 
     ```HTTP
     ### List existing indexes by name
@@ -280,7 +213,7 @@ POST {{baseUrl}}/indexes/{{index-name}}/docs/index?api-version={{api-version}}
 
 ## Create a knowledge agent
 
-To connect Azure AI Search to your `gpt-4o-mini` deployment and target the `earth_at_night` index at query time, you need a knowledge agent. Use [Create Knowledge Agents](/rest/api/searchservice/knowledge-agents/create?view=rest-searchservice-2025-05-01-preview&preserve-view=true) to define an agent named `earth-search-agent`, which you specified using the `@agent-name` variable in a previous section.
+To connect Azure AI Search to your `gpt-4.1-mini` deployment and target the `earth_at_night` index at query time, you need a knowledge agent. Use [Create Knowledge Agents](/rest/api/searchservice/knowledge-agents/create?view=rest-searchservice-2025-05-01-preview&preserve-view=true) to define an agent named `earth-search-agent`, which you specified using the `@agent-name` variable in a previous section.
 
 To ensure relevant and semantically meaningful responses, `defaultRerankerThreshold` is set to exclude responses with a reranker score of `2.5` or lower.
 
@@ -346,7 +279,7 @@ The output should be similar to the following JSON, where:
 
 + `response` provides a text string of the most relevant documents (or chunks) in the search index based on the user query. You can pass this string to an LLM for use as grounding data in answer generation.
 
-+ `activity` tracks the steps that were taken during the retrieval process, including the subqueries generated by your `gpt-4o-mini` deployment and the tokens used for query planning and execution.
++ `activity` tracks the steps that were taken during the retrieval process, including the subqueries generated by your `gpt-4.1-mini` deployment and the tokens used for query planning and execution.
 
 + `references` lists the documents that contributed to the response, each one identified by their `docKey`.
 

articles/search/includes/quickstarts/agentic-retrieval-python.md

@@ -0,0 +1,82 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 6/15/2025
+---
+
+## Configure role-based access
+
+You can use search service API keys or Microsoft Entra ID with role assignments. Keys are easier to start with, but roles are more secure. This quickstart assumes roles.
+
+To configure the recommended role-based access:
+
+1. Sign in to the [Azure portal](https://portal.azure.com/).
+
+1. [Enable role-based access](../../search-security-enable-roles.md) on your Azure AI Search service.
+
+1. On your Azure AI Search service, [assign the following roles](../../search-security-rbac.md#how-to-assign-roles-in-the-azure-portal) to yourself.
+
+    + **Search Service Contributor**
+
+    + **Search Index Data Contributor**
+
+    + **Search Index Data Reader**
+
+For agentic retrieval, Azure AI Search also needs access to your Azure OpenAI Foundry resource. 
+
+1. [Create a system-assigned managed identity](../../search-howto-managed-identities-data-sources.md#create-a-system-managed-identity) on your Azure AI Search service. Here's how to do it using the Azure CLI:
+
+   ```azurecli
+   az search service update --name YOUR-SEARCH-SERVICE-NAME --resource-group YOUR-RESOURCE-GROUP-NAME --identity-type SystemAssigned
+   ```
+   
+    If you already have a managed identity, you can skip this step.
+
+1. On your Azure AI Foundry resource, assign **Cognitive Services User** to the managed identity that you created for your search service. 
+
+## Deploy models
+
+To use agentic retrieval, you must deploy [one of the supported Azure OpenAI models](../../search-agentic-retrieval-how-to-create.md#supported-models) to your Azure AI Foundry resource:
+
++ A chat model for query planning and answer generation. We use `gpt-4.1-mini` in this quickstart. Optionally, you can use a different model for query planning and another for answer generation, but this quickstart uses the same model for simplicity.
+
++ An embedding model for vector queries. We use `text-embedding-3-large` in this quickstart, but you can use any embedding model that supports the `text-embedding` task.
+
+To deploy the Azure OpenAI models:
+
+1. Sign in to the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs) and select your Azure AI Foundry resource.
+
+1. From the left pane, select **Model catalog**.
+
+1. Select **gpt-4.1-mini**, and then select **Use this model**.
+
+1. Specify a deployment name. To simplify your code, we recommend **gpt-4.1-mini**.
+
+1. Leave the default settings.
+
+1. Select **Deploy**.
+
+1. Repeat the previous steps, but this time deploy the **text-embedding-3-large** embedding model.
+
+## Get endpoints
+
+In your code, you specify the following endpoints to establish connections with you Azure AI Search service and Azure AI Foundry resource. These steps assume that you [configured role-based access as described previously](#configure-role-based-access). 
+
+To obtain your service endpoints:
+
+1. Sign in to the [Azure portal](https://portal.azure.com/).
+
+1. On your Azure AI Search service:
+
+    1. From the left pane, select **Overview**.
+
+    1. Copy the URL, which should be similar to `https://my-service.search.windows.net`. 
+
+1. On your Azure AI Foundry resource:
+
+    1. From the left pane, select **Resource Management** > **Keys and Endpoint**. 
+
+    1. Select the **OpenAI** tab and copy the URL that looks similar to `https://my-resource.openai.azure.com`.

articles/search/includes/quickstarts/agentic-retrieval-rest.md

@@ -6,7 +6,7 @@ author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: quickstart
-ms.date: 05/29/2025
+ms.date: 6/15/2025
 zone_pivot_groups: search-get-started-agentic-retrieval
 ---