Merge pull request #311619 from JKirsch1/foundry-rebrand-batch-4

Foundry branding

Author: prmerger-automator[bot]

articles/logic-apps/add-agent-action-create-run-workflow.md

@@ -187,5 +187,5 @@ You can continue with these steps only after you finish the steps from the prece
 
 ## Related content
 
-- [What is Azure AI Foundry](/azure/ai-foundry/what-is-azure-ai-foundry)?
+- [What is Microsoft Foundry](/azure/ai-foundry/what-is-azure-ai-foundry)?
 - [What is Azure Logic Apps](/azure/logic-apps/logic-apps-overview)?

articles/logic-apps/add-agent-tools-connector-actions.md

@@ -91,7 +91,7 @@ The following high-level steps describe the behavior for a basic autonomous agen
    | Logic app | Model source |
    |-----------|--------------|
    | Consumption | [Microsoft Foundry](/azure/ai-foundry/foundry-models/concepts/models) |
-   | Standard | - [Azure OpenAI Service resource](/azure/ai-services/openai/concepts/models) <br>- [Azure AI Foundry project](/azure/ai-foundry/openai/concepts/models) (preview) <br>- [Azure API Management account](/azure/api-management/genai-gateway-capabilities) with an LLM API (preview) |
+   | Standard | - [Azure OpenAI Service resource](/azure/ai-services/openai/concepts/models) <br>- [Microsoft Foundry project](/azure/ai-foundry/openai/concepts/models) (preview) <br>- [Azure API Management account](/azure/api-management/genai-gateway-capabilities) with an LLM API (preview) |
 
 1. Based on the agent instructions, the model helps plan which tools that the agent needs to invoke to perform the necessary tasks.
 

articles/logic-apps/agent-workflows-concepts.md

@@ -17,7 +17,7 @@ ms.update-cycle: 180-days
 
 AI capabilities play a fast and growing role in applications and other software by performing useful, time-saving, or novel tasks like chat interactions. These capabilities can also work with other services, systems, apps, and data sources to help build integration workloads for enterprises and organizations.
 
-This guide provides building blocks, examples, and other resources that show how to use AI services like Azure OpenAI, Azure AI Foundry, and Azure AI Search with Azure Logic Apps to build automated workflows for AI integration solutions.
+This guide provides building blocks, examples, and other resources that show how to use Foundry Tools like Azure OpenAI, Microsoft Foundry, and Azure AI Search with Azure Logic Apps to build automated workflows for AI integration solutions.
 
 ## AI agent and model-powered workflows (Preview)
 
@@ -78,12 +78,12 @@ For more information, see the following resources:
 
 ### Prepare your content
 
-The following built-in actions and connectors help you prepare content for consumption by AI services, data ingestion, and chat interactions.
+The following built-in actions and connectors help you prepare content for consumption by Foundry Tools, data ingestion, and chat interactions.
 
 | Name | Capabilities |
 |------|--------------|
-| **Parse a document** | This built-in action converts content into tokenized string output, so a workflow can read and parse thousands of documents with file types such as PDF, DOCX, CSV, PPT, HTML, and others in multiple languages. <br><br>This action helps you prepare content for consumption by Azure AI services in your workflows. For example, connector operations for Azure AI services such as **Azure OpenAI** and **Azure AI Search** usually expect tokenized input and can handle only a limited number of tokens. |
-| **Chunk text** | This built-in action splits a tokenized string into pieces for easier consumption by subsequent actions in the same workflow. This action helps you prepare content for consumption by Azure AI services in your workflows. Connector operations for Azure AI services such as **Azure OpenAI** and **Azure AI Search** usually expect tokenized input and can handle only a limited number of tokens. |
+| **Parse a document** | This built-in action converts content into tokenized string output, so a workflow can read and parse thousands of documents with file types such as PDF, DOCX, CSV, PPT, HTML, and others in multiple languages. <br><br>This action helps you prepare content for consumption by Foundry Tools in your workflows. For example, connector operations for Foundry Tools such as **Azure OpenAI** and **Azure AI Search** usually expect tokenized input and can handle only a limited number of tokens. |
+| **Chunk text** | This built-in action splits a tokenized string into pieces for easier consumption by subsequent actions in the same workflow. This action helps you prepare content for consumption by Foundry Tools in your workflows. Connector operations for Foundry Tools such as **Azure OpenAI** and **Azure AI Search** usually expect tokenized input and can handle only a limited number of tokens. |
 | **Azure OpenAI** | This built-in connector provides operations for AI capabilities such as ingesting data, generating embeddings, and chat completion that are critical for creating sophisticated AI applications. You can integrate the natural language processing capabilities in Azure OpenAI with the intelligent search capabilities in Azure AI Search and other connectors. These integrations help you access and work with vector stores without needing to write code. |
 
 ### Data indexing and vector databases
@@ -103,7 +103,7 @@ For more information, see the following resources:
 | Resource type | Release | Link |
 |---------------|---------|------|
 | **Documentation** | Various | [Parse or chunk content for Standard workflows in Azure Logic Apps](/azure/logic-apps/parse-document-chunk-text) |
-| **Documentation** | Various | [Connect to Azure AI services from Standard workflows in Azure Logic Apps](/azure/logic-apps/connectors/azure-ai) |
+| **Documentation** | Various | [Connect to Foundry Tools from Standard workflows in Azure Logic Apps](/azure/logic-apps/connectors/azure-ai) |
 | **Documentation** | Various | [Azure OpenAI built-in operations reference](/azure/logic-apps/connectors/built-in/reference/openai) |
 | **Documentation** | Various | [Azure AI Search built-in operations reference](/azure/logic-apps/connectors/built-in/reference/azureaisearch) |
 | **Documentation** | Various | [Connect to SQL database from workflows in Azure Logic Apps](/azure/connectors/connectors-create-api-sqlazure?tabs=standard) |
@@ -120,7 +120,7 @@ For more information, see the following resources:
 
 ## Near real time chat with data
 
-The following sections describe ways that you can set up near-real time chat capabilities for your data using Azure Logic Apps and various AI services.
+The following sections describe ways that you can set up near-real time chat capabilities for your data using Azure Logic Apps and various Foundry Tools.
 
 ### Build Azure OpenAI Assistants with Azure Logic Apps
 
@@ -157,7 +157,7 @@ For more information, see the following resources:
 
 ## Manage intelligent document collection and processing
 
-You can use Azure AI Document Intelligence and Azure Logic Apps to build intelligent document processing workflows. The Document Intelligence connector provides operations that help you extract text and information from various documents. Document Intelligence helps you manage the speed in collecting and processing massive amounts of data stored in forms and documents with a wide variety of data types.
+You can use Azure Document Intelligence in Foundry Tools and Azure Logic Apps to build intelligent document processing workflows. The Document Intelligence connector provides operations that help you extract text and information from various documents. Document Intelligence helps you manage the speed in collecting and processing massive amounts of data stored in forms and documents with a wide variety of data types.
 
 > [!NOTE]
 >
@@ -218,14 +218,14 @@ For more information, see the following resources:
 
 Data is the cornerstone for any AI application and is unique for each organization. When you build an AI application, efficient data ingestion is critical for success. No matter where your data resides, you can integrate AI into new and existing business processes by building Standard workflows that use little or no code.
 
-More than 1,400 enterprise connectors and operations let you use Azure Logic Apps to quickly access and perform tasks with a wide range of services, systems, applications, and databases. When you use these connectors with AI services like Azure OpenAI and Azure AI Search, your organization can transform workloads like the following:
+More than 1,400 enterprise connectors and operations let you use Azure Logic Apps to quickly access and perform tasks with a wide range of services, systems, applications, and databases. When you use these connectors with Foundry Tools like Azure OpenAI and Azure AI Search, your organization can transform workloads like the following:
 
 - Automate routine tasks.
 - Enhance customer interactions with chat capabilities.
 - Provide access to organizational data when necessary.
 - Generate intelligent insights or responses.
 
-For example, when you integrate AI services by using the **Azure OpenAI** and **Azure AI Search** connector operations in your workflows, your organization can seamlessly implement the RAG pattern. RAG minimizes cost by using an information retrieval system to reference domain-specific or authoritative knowledge and augment an LLM's training without having to retrain the model. For more information, see the [Retrieval-augmented generation (RAG)](#rag-details) and the following resources:
+For example, when you integrate Foundry Tools by using the **Azure OpenAI** and **Azure AI Search** connector operations in your workflows, your organization can seamlessly implement the RAG pattern. RAG minimizes cost by using an information retrieval system to reference domain-specific or authoritative knowledge and augment an LLM's training without having to retrain the model. For more information, see the [Retrieval-augmented generation (RAG)](#rag-details) and the following resources:
 
 | Resource type | Link |
 |---------------|------|
@@ -240,9 +240,9 @@ Each template follows a common workflow pattern that supports a specific scenari
 
 The following table describes some example workflow templates:
 
-| Document source | Template description | AI services used |
-|-----------------|----------------------|------------------|
-| Azure AI Document Intelligence | Standard: Analyze complex documents using Azure OpenAI. | Azure OpenAI |
+| Document source | Template description | Foundry Tools used |
+|-----------------|----------------------|--------------------|
+| Document Intelligence | Standard: Analyze complex documents using Azure OpenAI. | Azure OpenAI |
 | Azure Blob Storage | Standard: <br>- Ingest and index files using the RAG pattern. <br>- Ingest and vectorize documents into Azure Cosmos DB for NoSQL using the RAG pattern. | - Azure OpenAI <br>- Azure AI Search |
 | Azure File Storage | Standard: <br>- Ingest documents into AI Search on a schedule. <br>- Ingest and index files on a schedule using the RAG pattern. <br>- Ingest and index files using the RAG pattern. | - Azure OpenAI <br>- Azure AI Search |
 | Request-based | Standard: <br>- Chat with your documents using the RAG pattern. <br>- Ingest and index documents using the RAG pattern. | - Azure OpenAI <br>- Azure AI Search |
@@ -260,9 +260,9 @@ For more information, see the following resources:
 | **Documentation** | [Create a Standard workflow in single-tenant Azure Logic Apps](/azure/logic-apps/create-single-tenant-workflows-azure-portal) |
 | **Documentation** | [Create and publish workflow templates for Azure Logic Apps](/azure/logic-apps/create-publish-workflow-templates) |
 | **Documentation** | [Parse or chunk content for Standard workflows in Azure Logic Apps](/azure/logic-apps/parse-document-chunk-text) |
-| **Documentation** | [Connect to Azure AI services from Standard workflows in Azure Logic Apps](/azure/logic-apps/connectors/azure-ai) |
+| **Documentation** | [Connect to Foundry Tools from Standard workflows in Azure Logic Apps](/azure/logic-apps/connectors/azure-ai) |
 
 ## Related content
 
 - [Parse or chunk content for Standard workflows in Azure Logic Apps](/azure/logic-apps/parse-document-chunk-text)
-- [Connect to Azure AI services from Standard workflows in Azure Logic Apps](/azure/logic-apps/connectors/azure-ai)
+- [Connect to Foundry Tools from Standard workflows in Azure Logic Apps](/azure/logic-apps/connectors/azure-ai)

articles/logic-apps/ai-resources.md

@@ -1,5 +1,5 @@
 ---
-title: Connect to Azure AI services from Workflows
+title: Connect to Foundry Tools from Workflows
 description: Learn to integrate workflows with Azure OpenAI and Azure AI Search services using Azure Logic Apps.
 author: ecfan
 services: logic-apps
@@ -11,7 +11,7 @@ ms.collection: ce-skilling-ai-copilot
 ms.update-cycle: 180-days
 ---
 
-# Connect to Azure AI services from workflows in Azure Logic Apps
+# Connect to Foundry Tools from workflows in Azure Logic Apps
 
 [!INCLUDE [logic-apps-sku-consumption-standard](../../../includes/logic-apps-sku-consumption-standard.md)]
 
@@ -22,7 +22,7 @@ This guide provides an overview and examples that show how to use **Azure OpenAI
 - [What is Azure OpenAI Service](/azure/ai-services/openai/overview)
 - [What is Azure AI Search](/azure/search/search-what-is-azure-search)
 
-## Why use Azure Logic Apps with AI services?
+## Why use Azure Logic Apps with Foundry Tools?
 
 Usually, building AI solutions involves several key steps and requires a few building blocks. Primarily, you need to have a dynamic ingestion pipeline and a chat interface that can communicate with large language models (LLMs) and vector databases.
 
@@ -37,7 +37,7 @@ Usually, building AI solutions involves several key steps and requires a few bui
 
 You can assemble various components, not only to perform data ingestion but also to provide a robust backend for the chat interface. This backend facilitates entering prompts and generates dependable responses during interactions. However, creating the code to manage and control all these elements can pose challenges, which is the case for most solutions.
 
-Azure Logic Apps offers a low code approach and simplifies backend management by providing prebuilt connectors that you use as building blocks to streamline the backend process. This approach lets you focus on sourcing your data and making sure that search results provide current and relevant information. With these AI connectors, your workflow acts as an orchestration engine that transfers data between AI services and other components that you want to integrate.
+Azure Logic Apps offers a low code approach and simplifies backend management by providing prebuilt connectors that you use as building blocks to streamline the backend process. This approach lets you focus on sourcing your data and making sure that search results provide current and relevant information. With these AI connectors, your workflow acts as an orchestration engine that transfers data between Foundry Tools and other components that you want to integrate.
 
 For more information, see the following resources:
 
@@ -94,21 +94,21 @@ The **Azure AI Search** connector has different versions, based on [logic app ty
 
 ### Authentication
 
-The AI managed connectors require an API key for authentication. However, the AI built-in connectors support multiple authentication types for your AI service endpoint. These options provide robust authentication that meets most customers' needs. Both built-in connectors can also directly connect to Azure OpenAI and Azure AI Search resources inside virtual networks or behind firewalls.
+The AI managed connectors require an API key for authentication. However, the AI built-in connectors support multiple authentication types for your Azure AI Services endpoint. These options provide robust authentication that meets most customers' needs. Both built-in connectors can also directly connect to Azure OpenAI and Azure AI Search resources inside virtual networks or behind firewalls.
 
-The following table describes the built-in connector authentication options, all which require that you provide the URL for the AI service endpoint:
+The following table describes the built-in connector authentication options, all which require that you provide the URL for the Azure AI Services endpoint:
 
 | Authentication type | Description |
 |---------------------|-------------|
-| **URL and key-based authentication** | Provide the API key or admin generated by the AI service. |
+| **URL and key-based authentication** | Provide the API key or admin generated by the Foundry Tool. |
 | **Active Directory OAuth** (Microsoft Entra ID) | Provide information such as your Entra tenant, client ID, and password to authenticate as an Entra user. |
 | **Managed identity** | After you set up managed identity authentication on your AI service resource and your logic app resource, you can use that identity to authenticate access for the connector. |
 
 [!INCLUDE [highest-security-level-guidance](../includes/highest-security-level-guidance.md)]
 
 For more information, see the following resources:
 
-- [Authenticate requests to Azure AI services](/azure/ai-services/authentication)
+- [Authenticate requests to Foundry Tools](/azure/ai-services/authentication)
 - [What is Microsoft Entra ID](/entra/fundamentals/whatis)
 - [What are managed identities for Azure resources](/entra/identity/managed-identities-azure-resources/overview)
 - [Authenticate access and connections to Azure resources with managed identities in Azure Logic Apps](../authenticate-with-managed-identity.md?tabs=standard)

articles/logic-apps/connectors/azure-ai.md

@@ -90,7 +90,7 @@ To follow the example, download the [sample prompt template and inputs](https://
 
 - An [Azure OpenAI Service resource](/azure/ai-services/openai/how-to/create-resource?pivots=web-portal) with a deployed model such as GPT-3.5 or GPT-4.
 
-  - The example in this how-to guide provides test data that you can use to try out the workflow. To chat with your own data by using the Azure OpenAI Service models, you have to create an Azure AI Foundry project and add your own data source. For more information, see the following documentation:
+  - The example in this how-to guide provides test data that you can use to try out the workflow. To chat with your own data by using the Azure OpenAI Service models, you have to create a Microsoft Foundry project and add your own data source. For more information, see the following documentation:
 
     - [Quickstart: Chat with Azure OpenAI models using your own data](/azure/ai-services/openai/use-your-data-quickstart)
 
@@ -340,4 +340,4 @@ If you don't need the resources that you created for this guide, make sure to de
 
 ## Related content
 
-- [Connect to Azure AI services from workflows in Azure Logic Apps](/azure/logic-apps/connectors/azure-ai)
+- [Connect to Foundry Tools from workflows in Azure Logic Apps](/azure/logic-apps/connectors/azure-ai)