Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into azure-functions-triggers-and-bindings

Author: ShawnJackson

articles/active-directory-b2c/partner-trusona.md

@@ -75,7 +75,7 @@ In this scenario, Trusona acts as an Identity Provider (IdP) for Azure AD B2C to
 
 ## Step 1: Onboard with Trusona Authentication Cloud
 
-1.	Sign in to the [Trusona Portal](https://portal.trusona.io).
+1.	Sign in to the [Trusona Portal](https://portal.trusona.com/).
 2.	From the left navigation panel, select **Settings**
 3.	In the Settings menu, select the slider to **Enable OIDC**.
 4.	Select the appropriate **Inputs** and provide the **Redirect URL** `https://{your-tenant-name}.b2clogin.com/{your-tenant-name}.onmicrosoft.com/oauth2/authresp`.

articles/api-management/TOC.yml

@@ -254,8 +254,6 @@
     href: azure-openai-enable-semantic-caching.md
   - name: Authenticate and authorize to Azure OpenAI
     href: api-management-authenticate-authorize-azure-openai.md
-  - name: Protect Azure OpenAI keys
-    href: /semantic-kernel/deploy/use-ai-apis-with-api-management?toc=%2Fazure%2Fapi-management%2Ftoc.json&bc=/azure/api-management/breadcrumb/toc.json
 - name: Manage APIs with policies
   items:
   - name: API Management policies overview

articles/api-management/api-management-authenticate-authorize-azure-openai.md

@@ -165,4 +165,3 @@ Following are high level steps to restrict API access to users or apps that are
 
 * Learn more about [Microsoft Entra ID and OAuth2.0](../active-directory/develop/authentication-vs-authorization.md).  
 * [Authenticate requests to Azure AI services](/azure/ai-services/authentication)
-* [Protect Azure OpenAI keys with API Management](/semantic-kernel/deploy/use-ai-apis-with-api-management?toc=%2Fazure%2Fapi-management%2Ftoc.json&bc=/azure/api-management/breadcrumb/toc.json)

articles/app-service/overview-hosting-plans.md

@@ -23,7 +23,7 @@ Each App Service plan defines:
 - Region (West US, East US, and so on)
 - Number of virtual machine (VM) instances
 - Size of VM instances (small, medium, large)
-- Pricing tier (Free, Shared, Basic, Standard, Premium, PremiumV2, PremiumV3, IsolatedV2)
+- Pricing tier (Free, Shared, Basic, Standard, Premium, PremiumV2, PremiumV3, PremiumV4 IsolatedV2)
 
 ## Pricing tiers
 
@@ -32,7 +32,7 @@ The pricing tier of an App Service plan determines what App Service features you
 | Category | Tiers | Description |
 |:-|:-|:-|
 | Shared compute | Free, Shared | Free and Shared, the two base tiers, run an app on the same Azure VM as other App Service apps, including apps of other customers. These tiers allocate CPU quotas to each app that runs on the shared resources. The resources can't scale out. These tiers are intended for only development and testing purposes. |
-| Dedicated compute | Basic, Standard, Premium, PremiumV2, PremiumV3 | The Basic, Standard, Premium, PremiumV2, and PremiumV3 tiers run apps on dedicated Azure VMs. Only apps in the same App Service plan share the same compute resources. The higher the tier, the more VM instances that are available to you for scale-out. |
+| Dedicated compute | Basic, Standard, Premium, PremiumV2, PremiumV3, PremiumV4 | The Basic, Standard, Premium, PremiumV2, PremiumV3, and PremiumV4 tiers run apps on dedicated Azure VMs. Only apps in the same App Service plan share the same compute resources. The higher the tier, the more VM instances that are available to you for scale-out. |
 | Isolated | IsolatedV2 | The IsolatedV2 tier runs dedicated Azure VMs on dedicated Azure virtual networks. This tier provides network isolation on top of compute isolation to your apps. It provides the maximum scale-out capabilities. |
 
 Each tier also provides a specific subset of App Service features. These features include custom domains and TLS/SSL certificates, autoscaling, deployment slots, backups, Azure Traffic Manager integration, and more. The higher the tier, the more features that are available. To find out which features are supported in each pricing tier, see the [App Service plan details](https://azure.microsoft.com/pricing/details/app-service/windows/#pricing).
@@ -41,29 +41,7 @@ You can find more comparisons of plans in [App Service limits](../azure-resource
 
 <a name="new-pricing-tier-premiumv3"></a>
 
-### PremiumV3 pricing tier
-
-The PremiumV3 pricing tier provides machines with faster processors (minimum 195 [Azure Compute Units](/azure/virtual-machines/acu) per virtual CPU), SSD storage, memory-optimized options, and quadruple memory-to-core ratio compared to the Standard tier.
-
-PremiumV3 also supports higher scale by using increased instance count, while it still provides the advanced capabilities in the Standard tier. PremiumV3 includes all features available in the PremiumV2 tier.
-
-Multiple VM sizes are available for this tier, including 4-to-1 and 8-to-1 memory-to-core ratios:
-
-| App Service plan | Cores (vCPU) | Memory (GiB) |
-|:-|:-|:-|
-| P0v3 | 1 | 4 |
-| P1v3 | 2 | 8 |
-| P1mv3 | 2 | 16 |
-| P2v3 | 4 | 16 |
-| P2mv3 | 4 | 32 |
-| P3v3 | 8 | 32 |
-| P3mv3 | 8 | 64 |
-| P4mv3 | 16 | 128 |
-| P5mv3 | 32 | 256 |
-
-For PremiumV3 pricing information, see [App Service pricing](https://azure.microsoft.com/pricing/details/app-service/).
-
-To get started with the PremiumV3 pricing tier, see [Configure PremiumV3 tier for Azure App Service](app-service-configure-premium-tier.md).
+For pricing information, see [App Service pricing](https://azure.microsoft.com/pricing/details/app-service/).
 
 ## Considerations for running and scaling an app
 
@@ -88,7 +66,7 @@ This section describes how App Service apps are billed. For detailed, region-spe
 Except for the Free tier, an App Service plan carries a charge on the compute resources that it uses:
 
 - **Shared tier**: Each app receives a quota of CPU minutes, so *each app* is charged for the CPU quota.
-- **Dedicated compute tiers (Basic, Standard, Premium, PremiumV2, PremiumV3)**: The App Service plan defines the number of VM instances that the apps are scaled to, so *each VM instance* in the App Service plan is charged. These VM instances are charged the same, regardless of how many apps are running on them. To avoid unexpected charges, see [Delete an App Service plan](app-service-plan-manage.md#delete-an-app-service-plan).
+- **Dedicated compute tiers (Basic, Standard, Premium, PremiumV2, PremiumV3, PremiumV4)**: The App Service plan defines the number of VM instances that the apps are scaled to, so *each VM instance* in the App Service plan is charged. These VM instances are charged the same, regardless of how many apps are running on them. To avoid unexpected charges, see [Delete an App Service plan](app-service-plan-manage.md#delete-an-app-service-plan).
 - **IsolatedV2 tier**: The App Service Environment defines the number of isolated workers that run your apps, and *each worker* is charged.
 
 You aren't charged for using the App Service features that are available to you. These features include configuring custom domains, TLS/SSL certificates, deployment slots, and backups. The exceptions are:
@@ -129,12 +107,12 @@ Isolate your app in a new App Service plan when:
   | B1, S1, P1v2, I1v1 | 8 |
   | B2, S2, P2v2, I2v1 | 16 |
   | B3, S3, P3v2, I3v1 | 32 |
-  | P0v3 | 8 |
-  | P1v3, I1v2 | 16 |
-  | P2v3, I2v2, P1mv3 | 32 |
-  | P3v3, I3v2, P2mv3 | 64 |
+  | P0v3, P0v4 | 8 |
+  | P1v3, P1v4, I1v2 | 16 |
+  | P2v3, P2v4, I2v2, P1mv3, P1mv4 | 32 |
+  | P3v3, P3v4, I3v2, P2mv3 | 64 |
   | I4v2, I5v2, I6v2 | Maximum density bound by vCPU usage |
-  | P3mv3, P4mv3, P5mv3 | Maximum density bound by vCPU usage |
+  | P3mv3, P3mv4, P4mv3, P4mv4, P5mv3, P5mv4 | Maximum density bound by vCPU usage |
 - You want to scale the app independently from the other apps in the existing plan.
 - The app needs resources in a different geographical region. This way, you can allocate a new set of resources for your app and gain greater control of your apps.
 

articles/app-service/troubleshoot-intermittent-outbound-connection-errors.md

@@ -100,7 +100,7 @@ Although PHP doesn't support connection pooling, you can try using persistent da
 #### Python
 
 * [MySQL](https://dev.mysql.com/doc/connector-python/en/connector-python-connection-pooling.html)
-* [MariaDB](https://mariadb.com/docs/ent/connect/programming-languages/python/connection-pools/)
+* [MariaDB](https://mariadb.com/docs/connectors/mariadb-connector-python/api/pool/)
 * [PostgreSQL](https://www.psycopg.org/docs/pool.html)
 * [Pyodbc](https://github.com/mkleehammer/pyodbc/wiki/The-pyodbc-Module#pooling)
 * [SQLAlchemy](https://docs.sqlalchemy.org/en/20/core/pooling.html)

articles/azure-app-configuration/TOC.yml

@@ -210,6 +210,8 @@
       href: howto-targetingfilter.md
     - name: ASP.NET Core
       href: howto-targetingfilter-aspnet-core.md
+    - name: JavaScript
+      href: howto-targetingfilter-javascript.md
   - name: Use variant feature flags
     items:
     - name: Overview

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

@@ -46,6 +46,8 @@ Chat completion is an AI capability that produces human-like dialogue responses
 | Anthropic      | Claude 3.7 Sonnet     |
 | Google         | Gemini 2.5 Pro        |
 | DeepSeek       | DeepSeek-R1           |
+| xAI            | Grok-3                |
+| xAI            | Grok-3 Mini           |
 
 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).

articles/azure-app-configuration/feature-management-javascript-reference.md

@@ -132,7 +132,7 @@ const featureManager = new FeatureManager(featureProvider);
 
 #### Use Azure App Configuration to dynamically control the state of the feature flag
 
-Azure App Configuration is not only a solution to externalize storage and centralized management of your feature flags, but also it allows to dynamically turn on/off the feature flags.
+Azure App Configuration is not only a solution to externalize storage and centralized management of your feature flags, but also it allows you to dynamically turn on/off the feature flags.
 
 To enable the dynamic refresh for feature flags, you need to configure the `refresh` property of `featureFlagOptions` when loading feature flags from Azure App Configuration.
 
@@ -526,7 +526,7 @@ app.use((req, res, next) => {
 const targetingContextAccessor = {
     getTargetingContext: () => {
         // Get the current request from AsyncLocalStorage
-        const request = requestContext.getStore();
+        const request = requestAccesor.getStore();
         if (!request) {
             return undefined; // Return undefined if there's no current request
         }

articles/azure-app-configuration/feature-management-overview.md

@@ -41,7 +41,7 @@ Feature | .NET | Spring | Python | JavaScript
 ------- | ---- | ------ | ------ | ----------
 Targeting Filter | [GA](./feature-management-dotnet-reference.md#targeting) | GA | [GA](./feature-management-python-reference.md#targeting) | [GA](./feature-management-javascript-reference.md#targeting)
 Targeting Exclusion | [GA](./feature-management-dotnet-reference.md#targeting-exclusion) | GA | [GA](./feature-management-python-reference.md#targeting-exclusion) | [GA](./feature-management-javascript-reference.md#targeting-exclusion)
-Ambient Targeting | [GA](./feature-management-dotnet-reference.md#targeting-in-a-web-application) | WIP | WIP | [Preview](./feature-management-javascript-reference.md#targeting-in-a-web-application)
+Ambient Targeting | [GA](./feature-management-dotnet-reference.md#targeting-in-a-web-application) | WIP | WIP | [GA](./feature-management-javascript-reference.md#targeting-in-a-web-application)
 Time Window Filter | [GA](./feature-management-dotnet-reference.md#microsofttimewindow) | GA | [GA](./feature-management-python-reference.md#microsofttimewindow) | [GA](./feature-management-javascript-reference.md#microsofttimewindow)
 Recurring Time Window | [GA](./feature-management-dotnet-reference.md#microsofttimewindow) | GA | WIP | WIP
 Custom Feature Filter | [GA](./feature-management-dotnet-reference.md#implementing-a-feature-filter) | GA | [GA](./feature-management-python-reference.md#implementing-a-feature-filter) | [GA](./feature-management-javascript-reference.md#implementing-a-feature-filter)

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

@@ -26,12 +26,9 @@ In this section, you create a chat completion configuration in Azure portal usin
  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**.
+    - **Key**: Type **ChatApp:ChatCompletion**.
     - **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)
@@ -40,13 +37,13 @@ In this section, you create a chat completion configuration in Azure portal usin
 
 ## 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.
+You successfully added your chat completion configuration named **ChatApp:ChatCompletion** 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. Follow the [Get started with Azure OpenAI Service](/azure/ai-foundry/openai/how-to/create-resource) 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.