Merge pull request #301306 from MicrosoftDocs/main

6/13/2025 AM Publish

Author: Taojunshen

articles/app-service/environment/toc.yml

@@ -52,7 +52,7 @@
 - name: Resources
   items:
   - name: Azure Roadmap
-    href: https://azure.microsoft.com/roadmap/
+    href: https://azure.microsoft.com/updates/
   - name: Pricing
     href: https://azure.microsoft.com/pricing/details/app-service/
   - name: Quota Information

articles/app-service/networking-features.md

@@ -1,11 +1,11 @@
 ---
 title: Networking features
 description: Learn about the networking features in Azure App Service, and learn which features you need for security or other functionality.
-author: madsd
+author: seligj95
 ms.assetid: 5c61eed1-1ad1-4191-9f71-906d610ee5b7
 ms.topic: article
-ms.date: 01/30/2025
-ms.author: madsd
+ms.date: 06/13/2025
+ms.author: jordanselig
 ms.custom: UpdateFrequency3
 #customer intent: As a deployment engineer, I want to understand the options for deploying apps in Azure App Services to control network traffic.
 ---
@@ -15,7 +15,7 @@ You can deploy applications in Azure App Service in multiple ways. By default, a
 
 There are two main deployment types for Azure App Service:
 
-- The multitenant public service hosts App Service plans in the Free, Shared, Basic, Standard, Premium, PremiumV2, and PremiumV3 pricing SKUs.
+- The multitenant public service hosts App Service plans in the Free, Shared, Basic, Standard, Premium, PremiumV2, PremiumV3, and PremiumV4 pricing SKUs.
 - The single-tenant App Service Environment (ASE) hosts Isolated SKU App Service plans directly in your Azure virtual network.
 
 The features that you use depend on whether you're in the multitenant service or in an ASE.
@@ -71,12 +71,15 @@ Azure App Service scale units support many customers in each deployment. The Fre
 
 #### Outbound addresses
 
-The worker virtual machines are broken down in large part by the App Service plans. The Free, Shared, Basic, Standard, and Premium plans all use the same worker virtual machine type. The PremiumV2 plan uses another virtual machine type. PremiumV3 uses yet another virtual machine type.
+The worker virtual machines are broken down in large part by the App Service plans. The Free, Shared, Basic, Standard, and Premium plans all use the same worker virtual machine type. The PremiumV2 plan uses another virtual machine type. PremiumV3 uses yet another virtual machine type. And PremiumV4 uses yet another virtual machine type.
 
 When you change the virtual machine family, you get a different set of outbound addresses. If you scale from Standard to PremiumV2, your outbound addresses change. If you scale from PremiumV2 to PremiumV3, your outbound addresses change. In some older scale units, both the inbound and outbound addresses change when you scale from Standard to PremiumV2.
 
 There are many addresses that are used for outbound calls. The outbound addresses used by your app for making outbound calls are listed in the properties for your app. All the apps that run on the same worker virtual machine family in the App Service deployment share these addresses. If you want to see all the addresses that your app might use in a scale unit, there's property called `possibleOutboundAddresses` that lists them.
 
+> [!NOTE]
+> Apps on the PremiumV4 SKU don't expose their outbound IP addresses.
+
 :::image type="content" source="media/networking-features/app-properties.png" alt-text="Screenshot that shows app properties, including possible outbound addresses." lightbox="media/networking-features/app-properties.png":::
 
 App Service has many endpoints that are used to manage the service. Those addresses are published in a separate document and are also in the `AppServiceManagement` IP service tag. The `AppServiceManagement` tag is used only in App Service Environments where you need to allow such traffic. The App Service inbound addresses are tracked in the `AppService` IP service tag. There's no IP service tag that contains the outbound addresses used by App Service.

articles/app-service/toc.yml

@@ -562,7 +562,7 @@ items:
     - name: Build your skills with Microsoft Learn training
       href: /training/browse/
     - name: Azure Roadmap
-      href: https://azure.microsoft.com/roadmap/
+      href: https://azure.microsoft.com/updates/
     - name: Pricing
       href: https://azure.microsoft.com/pricing/details/app-service/
     - name: Quota Information

articles/application-gateway/ingress-controller-annotations.md

@@ -27,7 +27,7 @@ For AGIC to observe an ingress resource, the resource *must be annotated* with `
 | [appgw.ingress.kubernetes.io/health-probe-hostname](#custom-health-probe) | `string` | `127.0.0.1` ||
 | [appgw.ingress.kubernetes.io/health-probe-port](#custom-health-probe) | `int32` | `80` ||
 | [appgw.ingress.kubernetes.io/health-probe-path](#custom-health-probe) | `string` | `/` ||
-| [appgw.ingress.kubernetes.io/health-probe-status-code](#custom-health-probe) | `string` | `200-399` ||
+| [appgw.ingress.kubernetes.io/health-probe-status-codes](#custom-health-probe) | `string` | `200-399` ||
 | [appgw.ingress.kubernetes.io/health-probe-interval](#custom-health-probe) | `int32` | `30` (seconds) ||
 | [appgw.ingress.kubernetes.io/health-probe-timeout](#custom-health-probe) | `int32` | `30` (seconds) ||
 | [appgw.ingress.kubernetes.io/health-probe-unhealthy-threshold](#custom-health-probe) | `int32` | `3` ||
@@ -126,7 +126,7 @@ You can [configure Application Gateway](./application-gateway-probe-overview.md)
 - `health-probe-hostname`: This annotation allows a custom hostname on the health probe.
 - `health-probe-port`: This annotation configures a custom port for the health probe.
 - `health-probe-path`: This annotation defines a path for the health probe.
-- `health-probe-status-code`: This annotation allows the health probe to accept different HTTP status codes.
+- `health-probe-status-codes`: This annotation allows the health probe to accept different HTTP status codes.
 - `health-probe-interval`: This annotation defines the interval at which the health probe runs.
 - `health-probe-timeout`: This annotation defines how long the health probe waits for a response before failing the probe.
 - `health-probe-unhealthy-threshold`: This annotation defines how many health probes must fail for the backend to be marked as unhealthy.
@@ -137,7 +137,7 @@ You can [configure Application Gateway](./application-gateway-probe-overview.md)
 appgw.ingress.kubernetes.io/health-probe-hostname: "contoso.com"
 appgw.ingress.kubernetes.io/health-probe-port: 80
 appgw.ingress.kubernetes.io/health-probe-path: "/"
-appgw.ingress.kubernetes.io/health-probe-status-code: "100-599"
+appgw.ingress.kubernetes.io/health-probe-status-codes: "100-599"
 appgw.ingress.kubernetes.io/health-probe-interval: 30
 appgw.ingress.kubernetes.io/health-probe-timeout: 30
 appgw.ingress.kubernetes.io/health-probe-unhealthy-threshold: 2
@@ -155,7 +155,7 @@ metadata:
     appgw.ingress.kubernetes.io/health-probe-hostname: "contoso.com"
     appgw.ingress.kubernetes.io/health-probe-port: 81
     appgw.ingress.kubernetes.io/health-probe-path: "/probepath"
-    appgw.ingress.kubernetes.io/health-probe-status-code: "100-599"
+    appgw.ingress.kubernetes.io/health-probe-status-codes: "100-599"
     appgw.ingress.kubernetes.io/health-probe-interval: 31
     appgw.ingress.kubernetes.io/health-probe-timeout: 31
     appgw.ingress.kubernetes.io/health-probe-unhealthy-threshold: 2

articles/azure-app-configuration/TOC.yml

@@ -170,6 +170,12 @@
   items:
   - name: Overview
     href: concept-ai-configuration.md
+  - name: Chat completion
+    items:
+    - name: Overview
+      href: howto-chat-completion-config.md
+    - name: .NET
+      href: quickstart-chat-completion-dotnet.md
 - name: Feature management
   items:
   - name: Overview

articles/azure-app-configuration/concept-ai-configuration.md

@@ -46,6 +46,12 @@ Chat completion is an AI capability that produces human-like dialogue responses
 | Google         | Gemini 2.5 Pro        |
 | DeepSeek       | DeepSeek-R1           |
 
-
 Azure OpenAI Service supports a diverse set of models from OpenAI. For more information, see [Azure OpenAI Service models](/azure/ai-services/openai/concepts/models). To learn more about models from Anthropic, refer to the [Claude models documentation](https://docs.anthropic.com/docs/about-claude/models/overview).  
-For more details about models provided by Google, see the [Gemini models documentation](https://ai.google.dev/gemini-api/docs/models).
+For more details about models provided by Google, see the [Gemini models documentation](https://ai.google.dev/gemini-api/docs/models).
+
+## Next steps
+
+Continue to the following instructions to use AI configuration in your application:
+
+> [!div class="nextstepaction"]
+> [Chat completion configuration](./howto-chat-completion-config.md)

articles/azure-app-configuration/howto-chat-completion-config.md

@@ -0,0 +1,67 @@
+---
+title: Chat completion configuration
+titleSuffix: Azure App Configuration
+description: Learn how to create chat completion configuration in Azure App Configuration.
+ms.service: azure-app-configuration
+author: MaryanneNjeri
+ms.author: mgichohi
+ms.topic: how-to
+ms.date: 04/20/2025
+ms.collection: ce-skilling-ai-copilot
+---
+
+# Chat Completion configuration in Azure App Configuration
+
+Chat completion is an AI capability that enables models to generate conversational responses based on a series of messages. Unlike simple text completion, chat completion maintains context across multiple exchanges, simulating a natural conversation. With chat completion configuration, you can define and manage how AI models respond within your application. A typical configuration includes model selection, system and user prompts, and model-specific settings such as temperature or token limits.
+
+## Prerequisites
+- An Azure account with an active subscription. [Create one for free](https://azure.microsoft.com/free)
+- An App Configuration store. [Create a store](./quickstart-azure-app-configuration-create.md#create-an-app-configuration-store).
+
+## Create a chat completion configuration
+
+In this section, you create a chat completion configuration in Azure portal using the GPT-4o model as an example.
+
+ 1. In Azure portal, navigate to your App configuration store. From the **Operations** menu, select **Configuration explorer** > **Create**, and then select **AI configuration**.
+
+ 1. Specify the following values:
+    - **Key**: Type **ChatApp:Model**.
+    - **Label**: Leave this value blank.
+    - **Model**: Select **gpt-4o**.
+    - **Message**: Add a new message.
+        - **Role**: Select **user**
+        - **Content**: Type "What is the capital of France?"
+    
+    > [!div class="mx-imgBorder"]
+    > ![Screenshot shows the create new AI configuration form.](./media/create-ai-chat-completion-config.png)
+    
+1. Leave the rest of the values as default then select **Apply**.
+
+## Add model connection configuration
+
+You successfully added your chat completion configuration named **ChatApp:Model** in the previous section. In this section, you add the connection details for your model, including the endpoint and deployment name. If required by your authentication method, you can also specify an API key using a Key Vault reference.
+
+> [!NOTE]
+> This tutorial demonstrates how to use chat completion configuration with an Azure OpenAI model. However, the chat completion configuration demonstrated in the tutorial can be applied to any AI model you choose to work with in your application.
+>
+
+1. Follow the [Get started with Azure OpenAI Service](/azure/ai-services/openai/overview#get-started-with-azure-openai-service) to create and deploy an Azure OpenAI service resource with a **gpt-4o** model. Note down the deployment name for later use.
+
+1. In your Azure OpenAI resource, from the **Resource Management** menu, select **Keys and Endpoint** and copy the Azure OpenAI resource endpoint. It should follow the format: `https://<open-ai-resource-name>.openai.azure.com`. If using the API key for authentication, copy the API key as well.
+
+1. Navigate to your App Configuration store and add the following key-values. Leave **Label** and **Content Type** with their default values. For more information about how to add key-values to a store using the Azure portal or the CLI, go to [Create a key-value](./quickstart-azure-app-configuration-create.md#create-a-key-value).
+
+    | **Key**                                 | **Value**                                                             |
+    |-----------------------------------------|-----------------------------------------------------------------------|
+    |  _ChatApp:AzureOpenAI:Endpoint_         | _Paste the resource endpoint you copied in the previous step_         |
+    |  _ChatApp:AzureOpenAI:DeploymentName_   | _Paste the model deployment name you copied in the previous step_     |
+ 
+    If you plan to use an API key for authentication, you can optionally add it as a Key Vault reference using the following key-value. For detailed instructions, see [Add a Key Vault reference to App Configuration](./use-key-vault-references-dotnet-core.md#add-a-key-vault-reference-to-app-configuration).
+
+    | **Key**                          | **Value**                                                      |
+    |----------------------------------|----------------------------------------------------------------|
+    |  _ChatApp:AzureOpenAI:APIKey_  | _The Key Vault reference of your Azure OpenAI resource API key_  |
+
+1. Continue to the following instructions to implement the chat completion configuration into your application for the language or platform you're using.
+
+    - [.NET](./quickstart-chat-completion-dotnet.md)