Merge pull request #6602 from MicrosoftDocs/main

Auto Publish – main to live - 2025-08-15 17:08 UTC

Author: learn-build-service-prod[bot]

articles/ai-foundry/agents/how-to/tools/azure-ai-search.md

@@ -166,6 +166,13 @@ You can add the Azure AI Search tool to an agent programmatically using the code
 
 1. The index is created and connected to the [Azure AI Search](/azure/search/) service. You can now use the index with the Azure AI Search tool in your agent. You can also use the index outside of the agent, such as the Azure AI Search REST API or SDKs.
 
+
+## Limitations
+
+Currently, if you want to use the Azure AI Search tool in the Azure AI Foundry portal behind a virtual network, you must create an agent using the SDK or REST API. After creating the agent in a code-based manner, you can then use it in the portal. 
+
+The Azure AI Search tool can only include one search index. If you want to utilize multiple indexes, consider using [connected agents](../connected-agents.md) with an Azure AI search index configured with each agent.
+
 ## Next steps
 
 * See examples on how to use the [Azure AI Search tool](azure-ai-search-samples.md). 

articles/ai-foundry/concepts/prompt-flow.md

@@ -7,6 +7,7 @@ ms.custom:
   - ignite-2023
   - build-2024
   - ignite-2024
+  - hub-only
 ms.topic: concept-article
 ms.date: 06/30/2025
 ms.reviewer: none
@@ -24,6 +25,8 @@ Prompt flow is a development tool designed to streamline the entire development
 
 Prompt flow is available independently as an open-source project on [GitHub](https://github.com/microsoft/promptflow), with its own SDK and [VS Code extension](https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow). Prompt flow is also available and recommended to use as a feature within both [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs) and [Azure Machine Learning studio](https://ml.azure.com). This set of documentation focuses on prompt flow in Azure AI Foundry portal.
 
+[!INCLUDE [hub-only-prereq](../includes/uses-hub-only.md)]
+
 Definitions:
 
 - *Prompt flow* is a feature that can be used to generate, customize, or run a flow.

articles/ai-foundry/foundry-models/concepts/models.md

@@ -108,6 +108,14 @@ Microsoft models include various model groups such as MAI models, Phi models, he
 
 See [the Microsoft model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=phi). There are also several Microsoft models available [from partners and community](#microsoft).
 
+### Mistral models sold directly by Azure
+
+| Model | Type | Capabilities | Project type 
+| ------ | ---- | ------------ | ------------ |
+| [mistral-document-ai-2505]() | Image-to-Text | - **Input:** image or PDF pages ( 30 pages, max 30MB PDF file) <br /> - **Output:** text  <br /> - **Languages:** en <br />  - **Tool calling:** no  <br /> - **Response formats:** Text, JSON, Markdown  | Foundry |
+
+See [the Mistral model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=mistral).  There are also several Mistral models available [from partners and community](#mistral-ai).
+
 
 ### xAI models sold directly by Azure
 

articles/ai-foundry/how-to/fine-tune-managed-compute.md

@@ -43,7 +43,7 @@ To improve model performance, you might consider fine-tuning a foundation model
 1. If you're not already in your project, select it. 
 1. Select **Fine-tuning** from the left pane.
 
-    1. Select **Fine-tune model** and add the model that you want to fine-tune. This article uses _Phi-3-mini-4k-instruct_ for illustration.
+    1. Select **Fine-tune a model** and add the model that you want to fine-tune. This article uses _Phi-3-mini-4k-instruct_ for illustration.
     1. Select **Next** to see the available fine-tune options. Some foundation models support only the __Managed compute__ option.
 
 1. Alternatively, you could select **Model catalog** from the left sidebar of your project and find the model card of the foundation model that you want to fine-tune.
@@ -60,7 +60,7 @@ In this section, you go through the steps to configure fine-tuning for your mode
 
 1. Provide a name for the fine-tuned model on the "Basic settings" page, and select **Next** to go to the "Compute" page.
 
-1. Select the Azure Machine Learning compute cluster to use for fine-tuning the model. Fine-tuning runs on GPU compute. Ensure that you have sufficient compute quota for the compute SKUs you plan to use.
+1. Select a Virtual machine size to use for fine-tuning the model. Fine-tuning runs on GPU compute. Ensure that you have sufficient compute quota for the compute SKUs you plan to use.
 
     :::image type="content" source="../media/how-to/fine-tune-managed-compute/fine-tune-compute.png" alt-text="Screenshot showing settings for the compute to use for fine-tuning." lightbox="../media/how-to/fine-tune-managed-compute/fine-tune-compute.png":::
 

articles/ai-foundry/how-to/flow-bulk-test-evaluation.md

@@ -6,6 +6,7 @@ ms.service: azure-ai-foundry
 ms.custom:
   - ignite-2023
   - build-2024
+  - hub-only
 ms.topic: how-to
 ms.date: 5/21/2024
 ms.reviewer: none
@@ -32,6 +33,8 @@ In this article you learn to:
 
 ## Prerequisites
 
+[!INCLUDE [hub-only-prereq](../includes/hub-only-prereq.md)]
+
 For a batch run and to use an evaluation method, you need to have the following ready:
 
 - A test dataset for batch run. Your dataset should be in one of these formats: `.csv`, `.tsv`, or `.jsonl`. Your data should also include headers that match the input names of your flow. If your flow inputs include a complex structure like a list or dictionary, use `jsonl` format to represent your data. 

articles/ai-foundry/how-to/flow-develop-evaluation.md

@@ -6,6 +6,7 @@ ms.service: azure-ai-foundry
 ms.custom:
   - ignite-2023
   - build-2024
+  - hub-only
 ms.topic: how-to
 ms.date: 3/31/2025
 ms.reviewer: mithigpe
@@ -26,6 +27,10 @@ In prompt flow, you can customize or create your own evaluation flow tailored to
 - How to develop an evaluation method.
 - Understand inputs, outputs, and logging metrics for prompt flow evaluations.
 
+## Prerequisites
+
+[!INCLUDE [hub-only-prereq](../includes/hub-only-prereq.md)]
+
 ## Starting to develop an evaluation method
 
 There are two ways to develop your own evaluation methods:

articles/ai-foundry/how-to/flow-process-image.md

@@ -5,6 +5,7 @@ description: Learn how to use images in prompt flow.
 ms.service: azure-ai-foundry
 ms.custom:
   - build-2024
+  - hub-only
 ms.topic: how-to
 ms.date: 06/30/2025
 ms.reviewer: none
@@ -28,6 +29,8 @@ In this article, you learn:
 > - How to create a batch run using image data.  
 > - How to consume online endpoint with image data.
 
+[!INCLUDE [uses-hub-only](../includes/uses-hub-only.md)]
+
 ## Image type in prompt flow
 
 Prompt flow input and output support Image as a new data type.

articles/ai-foundry/how-to/flow-tune-prompts-using-variants.md

@@ -6,6 +6,7 @@ ms.service: azure-ai-foundry
 ms.custom:
   - ignite-2023
   - build-2024
+  - hub-only
 ms.topic: how-to
 ms.date: 3/31/2025
 ms.reviewer: none
@@ -25,6 +26,8 @@ Crafting a good prompt is a challenging task that requires much creativity, clar
 
 Variants can help you test the model’s behavior under different conditions, such as different wording, formatting, context, temperature, or top-k. You can compare and find the best prompt and configuration that maximizes the model's accuracy, diversity, or coherence.
 
+[!INCLUDE [uses-hub-only](../includes/uses-hub-only.md)]
+
 ## Variants in Prompt flow
 
 With prompt flow, you can use variants to tune your prompt. A variant refers to a specific version of a tool node that has distinct settings. Currently, variants are supported only in the [LLM tool](prompt-flow-tools/llm-tool.md). For example, in the LLM tool, a new variant can represent either a different prompt content or different connection settings.

articles/ai-foundry/how-to/prompt-flow-tools/embedding-tool.md

@@ -6,6 +6,7 @@ ms.service: azure-ai-foundry
 ms.custom:
   - ignite-2023
   - build-2024
+  - hub-only
 ms.topic: reference
 ms.date: 6/30/2025
 ms.reviewer: none
@@ -21,9 +22,13 @@ ms.update-cycle: 180-days
 
 The prompt flow Embedding tool enables you to convert text into dense vector representations for various natural language processing tasks.
 
-> [!NOTE]
+> [!TIP]
 > For chat and completion tools, learn more about the large language model [(LLM) tool](llm-tool.md).
 
+## Prerequisites
+
+[!INCLUDE [hub-only-prereq](../../includes/hub-only-prereq.md)]
+
 ## Build with the Embedding tool
 
 1. Create or open a flow in [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs). For more information, see [Create a flow](../flow-develop.md).

articles/ai-foundry/how-to/prompt-flow-tools/index-lookup-tool.md

@@ -5,6 +5,7 @@ description: This article introduces you to the Index Lookup tool for flows in A
 ms.service: azure-ai-foundry
 ms.custom:
   - build-2024
+  - hub-only
 ms.topic: reference
 ms.date: 6/30/2025
 ms.reviewer: none
@@ -20,6 +21,10 @@ ms.update-cycle: 180-days
 
 The prompt flow Index Lookup tool enables the use of common vector indices (such as Azure AI Search, Faiss, and Pinecone) for retrieval augmented generation in prompt flow. The tool automatically detects the indices in the workspace and allows the selection of the index to be used in the flow.
 
+## Prerequisites
+
+[!INCLUDE [hub-only-prereq](../../includes/hub-only-prereq.md)]
+
 ## Build with the Index Lookup tool
 
 1. Create or open a flow in [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs). For more information, see [Create a flow](../flow-develop.md).