Address PR comments by switching Foundry Tools back to AI services

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 Foundry Tool
+## 4. Add your AI service
 
-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.
+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.
 
 Use the following code for Azure OpenAI:
 

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 Foundry Tool 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 AI service 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 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.
+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.
 
 ## 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 Foundry Tools or internet connectivity
+- **Zero external dependencies**: No reliance on external AI services 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/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 Foundry Tools 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 AI services in Azure 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 Foundry Tools.
+> * Deploy a Blazor application that uses RAG pattern with AI services in Azure.
 > * 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 Foundry Tools 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 AI services in Azure 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 Foundry Tools.
+> * Deploy a Spring Boot application that uses RAG pattern with AI services in Azure.
 > * 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 Foundry Tools 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 AI services in Azure 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 Foundry Tools.
+> * Deploy an Express.js application that uses RAG pattern with AI services in Azure.
 > * 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-python.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 (FastAPI)
 
-In this tutorial, you'll create a Python Retrieval Augmented Generation (RAG) application using FastAPI, 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. 
+In this tutorial, you'll create a Python Retrieval Augmented Generation (RAG) application using FastAPI, 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 AI services in Azure 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 FastAPI chat interface in introduction.":::
 
 In this tutorial, you learn how to:
 
 > [!div class="checklist"]
-> * Deploy a FastAPI application that uses RAG pattern with Foundry Tools.
+> * Deploy a FastAPI application that uses RAG pattern with AI services in Azure.
 > * 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.