Update Azure AI Foundry and Azure AI services references

Author: JKirsch1

articles/app-service/includes/deploy-intelligent-apps/deploy-intelligent-apps-linux-dotnet-pivot.md

@@ -160,9 +160,9 @@ To initialize the kernel, add the following code to the `OpenAI.razor` file.
 
 In this step, you add the using statement and create the kernel in a method that you can use when you send the request to the service.
 
-## 4. Add your AI service
+## 4. Add your Foundry Tool
 
-After the kernel is initialized, you can add your chosen AI service to the kernel. You define your model and pass in your key and endpoint information that the chosen model consumes. If you plan to use a managed identity with Azure OpenAI, add the service by using the example in the next section.
+After the kernel is initialized, you can add your chosen Foundry Tool to the kernel. You define your model and pass in your key and endpoint information that the chosen model consumes. If you plan to use a managed identity with Azure OpenAI, add the service by using the example in the next section.
 
 Use the following code for Azure OpenAI:
 

articles/app-service/includes/quickstart-java/quickstart-java-linux-azure-portal-pivot.md

@@ -160,7 +160,7 @@ To complete this quickstart you need:
 
 1. From the left navigation, select **Deployment Center**.
 
-    ![Screenshot of the App Service in the Azure Portal. The Deployment Center option in the Deployment section of the left navigation is highlighted.](../../media/quickstart-java/azure-portal-configure-app-service-deployment-center.png)  
+    ![Screenshot of the App Service in the Azure portal. The Deployment Center option in the Deployment section of the left navigation is highlighted.](../../media/quickstart-java/azure-portal-configure-app-service-deployment-center.png)  
 
 1.  Under **Settings**, select a **Source**. For this quickstart, select _GitHub_.
 

articles/app-service/includes/tutorial-connect-msi-key-vault/cleanup.md

@@ -7,7 +7,7 @@ ms.custom: devx-track-azurecli
 ms.service: azure-app-service
 ---
 
-1. Configure the Azure AI services secrets as app settings `CS_ACCOUNT_NAME` and `CS_ACCOUNT_KEY`.
+1. Configure the Foundry Tools secrets as app settings `CS_ACCOUNT_NAME` and `CS_ACCOUNT_KEY`.
 
     ```azurecli-interactive
     # Get subscription key for Cognitive Services resource
@@ -64,9 +64,9 @@ At the moment, connection secrets are stored as app settings in your App Service
     az webapp config appsettings set --resource-group $groupName --name $appName --settings CS_ACCOUNT_NAME="@Microsoft.KeyVault(SecretUri=$csResourceKVUri)" CS_ACCOUNT_KEY="@Microsoft.KeyVault(SecretUri=$csKeyKVUri)"
     ```
 
-1. In the browser, navigate to `<app-name>.azurewebsites.net` again. If you get detection results back, then you're connecting to the Azure AI services endpoint with key vault references.
+1. In the browser, navigate to `<app-name>.azurewebsites.net` again. If you get detection results back, then you're connecting to the Azure AI Services endpoint with key vault references.
 
-Congratulations, your app is now connecting to Azure AI services using secrets kept in your key vault, without any changes to your application code.
+Congratulations, your app is now connecting to Foundry Tools using secrets kept in your key vault, without any changes to your application code.
 
 ## Clean up resources
 

articles/app-service/includes/tutorial-connect-msi-key-vault/introduction.md

@@ -7,12 +7,12 @@ ms.custom: devx-track-azurecli
 ms.service: azure-app-service
 ---
 
-[Azure App Service](../../overview.md) can use [managed identities](../../overview-managed-identity.md) to connect to back-end services without a connection string, which eliminates connection secrets to manage and keeps your back-end connectivity secure in a production environment. For back-end services that don't support managed identities and still requires connection secrets, you can use Key Vault to manage connection secrets. This tutorial uses Azure AI services as an example to show you how it's done in practice. When you're finished, you have an app that makes programmatic calls to Azure AI services, without storing any connection secrets inside App Service.
+[Azure App Service](../../overview.md) can use [managed identities](../../overview-managed-identity.md) to connect to back-end services without a connection string, which eliminates connection secrets to manage and keeps your back-end connectivity secure in a production environment. For back-end services that don't support managed identities and still requires connection secrets, you can use Key Vault to manage connection secrets. This tutorial uses Foundry Tools as an example to show you how it's done in practice. When you're finished, you have an app that makes programmatic calls to Foundry Tools, without storing any connection secrets inside App Service.
 
 * [Sample application](https://github.com/Azure-Samples/app-service-language-detector)
 
 > [!TIP]
-> Azure AI services do [support authentication through managed identities](/azure/ai-services/authentication#authorize-access-to-managed-identities), but this tutorial uses the [subscription key authentication](/azure/ai-services/authentication#authenticate-with-a-single-service-resource-key) to demonstrate how you could connect to an Azure service that doesn't support managed identities from App Services.
+> Foundry Tools do [support authentication through managed identities](/azure/ai-services/authentication#authorize-access-to-managed-identities), but this tutorial uses the [subscription key authentication](/azure/ai-services/authentication#authenticate-with-a-single-service-resource-key) to demonstrate how you could connect to an Azure service that doesn't support managed identities from App Services.
 
 ![Architecture diagram for tutorial scenario.](../../media/tutorial-connect-msi-key-vault/architecture.png)
 
@@ -29,7 +29,7 @@ What you will learn:
 > * Enable managed identities
 > * Use managed identities to connect to Key Vault
 > * Use Key Vault references
-> * Access Azure AI services
+> * Access Foundry Tools
 
 ## Prerequisites
 
@@ -39,7 +39,7 @@ Prepare your environment for the Azure CLI.
 
 <a name='create-app-with-connectivity-to-cognitive-services'></a>
 
-## Create app with connectivity to Azure AI services
+## Create app with connectivity to Foundry Tools
 
 1. Create a resource group to contain all of your resources:
 

articles/app-service/overview-ai-integration.md

@@ -20,7 +20,7 @@ Azure App Service makes it easy to integrate AI capabilities into your web appli
 
 App Service offers several advantages for developing and deploying AI-powered applications:
 
-- **Native integration with Azure AI services** - Seamlessly connect to Azure OpenAI and other AI services using managed identities for secure, passwordless authentication
+- **Native integration with Foundry Tools** - Seamlessly connect to Azure OpenAI and other Foundry Tools using managed identities for secure, passwordless authentication
 - **Local SLM support** - Use sidecar extensions to deploy smaller language models directly with your application
 - **Enterprise-grade security** - Implement network isolation, end-to-end encryption, and role-based access control
 - **Simplified DevOps with GitHub integration** - Streamline CI/CD pipelines using GitHub Actions, leverage GitHub Codespaces with integrated GitHub Copilot for AI-assisted development, and create end-to-end workflows from development to production deployment

articles/app-service/scenario-ai-local-small-language-model.md

@@ -1,6 +1,6 @@
 ---
 title: Use local small language models (SLMs) in Azure App Service
-description: Deploy a web app with a local small language model (SLM) as a sidecar container to run AI models entirely within your App Service environment. No outbound calls or external AI service dependencies required.
+description: Deploy a web app with a local small language model (SLM) as a sidecar container to run AI models entirely within your App Service environment. No outbound calls or external Foundry Tool dependencies required.
 author: cephalin
 ms.author: cephalin
 ms.service: azure-app-service
@@ -14,7 +14,7 @@ ms.update-cycle: 180-days
 
 # Use a local SLM (sidecar container)
 
-Deploy a web app with a local small language model (SLM) as a sidecar container to run AI models entirely within your App Service environment. No outbound calls or external AI service dependencies required. This approach is ideal if you have strict data privacy or compliance requirements, as all AI processing and data remain local to your app. App Service offers high-performance, memory-optimized pricing tiers needed for running SLMs in sidecars.
+Deploy a web app with a local small language model (SLM) as a sidecar container to run AI models entirely within your App Service environment. No outbound calls or external Foundry Tool dependencies required. This approach is ideal if you have strict data privacy or compliance requirements, as all AI processing and data remain local to your app. App Service offers high-performance, memory-optimized pricing tiers needed for running SLMs in sidecars.
 
 ## Overview
 
@@ -23,7 +23,7 @@ Small Language Models (SLMs) are compact AI models, such as Microsoft's Phi-3 an
 This architecture provides several advantages:
 
 - **Complete data privacy**: All data and AI processing stays within your App Service environment
-- **Zero external dependencies**: No reliance on external AI services or internet connectivity
+- **Zero external dependencies**: No reliance on external Foundry Tools or internet connectivity
 - **Predictable latency**: Responses are consistently fast with no network overhead
 - **Cost control**: Pay only for App Service compute resources, with no per-token charges
 - **Regulatory compliance**: Meet strict data residency and privacy requirements

articles/app-service/toc.yml

@@ -370,7 +370,7 @@ items:
                   href: tutorial-send-email.md
             - name: Secrets in Key Vault
               items:
-              - name: Connect to Azure AI services
+              - name: Connect to Foundry Tools
                 items: 
                 - name: Use .NET
                   href: tutorial-connect-msi-key-vault.md

articles/app-service/tutorial-ai-openai-search-dotnet.md

@@ -17,14 +17,14 @@ ms.update-cycle: 180-days
 
 # Tutorial: Build a retrieval augmented generation app in Azure App Service with Azure OpenAI and Azure AI Search (.NET)
 
-In this tutorial, you'll create a .NET retrieval augmented generation (RAG) application using .NET Blazor, Azure OpenAI, and Azure AI Search and deploy it to Azure App Service. This application demonstrates how to implement a chat interface that retrieves information from your own documents and leverages Azure AI services to provide accurate, contextually aware answers with proper citations. The solution uses managed identities for passwordless authentication between services. 
+In this tutorial, you'll create a .NET retrieval augmented generation (RAG) application using .NET Blazor, Azure OpenAI, and Azure AI Search and deploy it to Azure App Service. This application demonstrates how to implement a chat interface that retrieves information from your own documents and leverages Foundry Tools to provide accurate, contextually aware answers with proper citations. The solution uses managed identities for passwordless authentication between services. 
 
 :::image type="content" source="media/tutorial-ai-openai-search-dotnet/chat-interface.png" alt-text="Screenshot showing the Blazor chat interface in introduction.":::
 
 In this tutorial, you learn how to:
 
 > [!div class="checklist"]
-> * Deploy a Blazor application that uses RAG pattern with Azure AI services.
+> * Deploy a Blazor application that uses RAG pattern with Foundry Tools.
 > * Configure Azure OpenAI and Azure AI Search for hybrid search.
 > * Upload and index documents for use in your AI-powered application.
 > * Use managed identities for secure service-to-service communication.

articles/app-service/tutorial-ai-openai-search-java.md

@@ -18,7 +18,7 @@ ms.update-cycle: 180-days
 
 # Tutorial: Build a retrieval augmented generation app in Azure App Service with Azure OpenAI and Azure AI Search (Spring Boot)
 
-In this tutorial, you'll create a Java Retrieval Augmented Generation (RAG) application using Spring Boot, Azure OpenAI, and Azure AI Search and deploy it to Azure App Service. This application demonstrates how to implement a chat interface that retrieves information from your own documents and leverages Azure AI services to provide accurate, contextually aware answers with proper citations. The solution uses managed identities for passwordless authentication between services. 
+In this tutorial, you'll create a Java Retrieval Augmented Generation (RAG) application using Spring Boot, Azure OpenAI, and Azure AI Search and deploy it to Azure App Service. This application demonstrates how to implement a chat interface that retrieves information from your own documents and leverages Foundry Tools to provide accurate, contextually aware answers with proper citations. The solution uses managed identities for passwordless authentication between services. 
 
 > [!TIP]
 > While this tutorial uses Spring Boot, the core concepts of building a RAG application with Azure OpenAI and Azure AI Search apply to any Java web application. If you're using a different hosting option on App Service, such as Tomcat or JBoss EAP, you can adapt the authentication patterns and Azure SDK usage shown here to your preferred framework.
@@ -28,7 +28,7 @@ In this tutorial, you'll create a Java Retrieval Augmented Generation (RAG) appl
 In this tutorial, you learn how to:
 
 > [!div class="checklist"]
-> * Deploy a Spring Boot application that uses RAG pattern with Azure AI services.
+> * Deploy a Spring Boot application that uses RAG pattern with Foundry Tools.
 > * Configure Azure OpenAI and Azure AI Search for hybrid search.
 > * Upload and index documents for use in your AI-powered application.
 > * Use managed identities for secure service-to-service communication.

articles/app-service/tutorial-ai-openai-search-nodejs.md

@@ -18,14 +18,14 @@ ms.update-cycle: 180-days
 
 # Tutorial: Build a retrieval augmented generation app in Azure App Service with Azure OpenAI and Azure AI Search (Express.js)
 
-In this tutorial, you'll create a Node.js Retrieval Augmented Generation (RAG) application using Express.js, Azure OpenAI, and Azure AI Search and deploy it to Azure App Service. This application demonstrates how to implement a chat interface that retrieves information from your own documents and leverages Azure AI services to provide accurate, contextually aware answers with proper citations. The solution uses managed identities for passwordless authentication between services. 
+In this tutorial, you'll create a Node.js Retrieval Augmented Generation (RAG) application using Express.js, Azure OpenAI, and Azure AI Search and deploy it to Azure App Service. This application demonstrates how to implement a chat interface that retrieves information from your own documents and leverages Foundry Tools to provide accurate, contextually aware answers with proper citations. The solution uses managed identities for passwordless authentication between services. 
 
 :::image type="content" source="media/tutorial-ai-openai-search-dotnet/chat-interface.png" alt-text="Screenshot showing the Express.js chat interface in introduction.":::
 
 In this tutorial, you learn how to:
 
 > [!div class="checklist"]
-> * Deploy an Express.js application that uses RAG pattern with Azure AI services.
+> * Deploy an Express.js application that uses RAG pattern with Foundry Tools.
 > * Configure Azure OpenAI and Azure AI Search for hybrid search.
 > * Upload and index documents for use in your AI-powered application.
 > * Use managed identities for secure service-to-service communication.