Merge pull request #6863 from MicrosoftDocs/main

Auto Publish – main to live - 2025-08-30 05:01 UTC

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

articles/ai-foundry/concepts/ai-red-teaming-agent.md

@@ -4,7 +4,7 @@ titleSuffix: Azure AI Foundry
 description: This article provides conceptual overview of the AI Red Teaming Agent.
 ms.service: azure-ai-foundry
 ms.topic: how-to
-ms.date: 04/04/2025
+ms.date: 08/29/2025
 ms.reviewer: minthigpen
 ms.author: lagayhar
 author: lgayhardt

articles/ai-foundry/how-to/develop/trace-application.md

@@ -4,15 +4,15 @@ titleSuffix: Azure AI Foundry
 description: Learn how to trace applications that use OpenAI SDK in Azure AI Foundry
 author: lgayhardt
 ms.author: lagayhar
-ms.reviewer: amibp
-ms.date: 05/19/2025
+ms.reviewer: ychen
+ms.date: 08/29/2025
 ms.service: azure-ai-foundry
 ms.topic: how-to
 ---
 
 # Trace AI applications using OpenAI SDK
 
-Tracing provides deep visibility into execution of your application by capturing detailed telemetry at each execution step. Such helps diagnose issues and enhance performance by identifying problems such as inaccurate tool calls, misleading prompts, high latency, low-quality evaluation scores, and more.  
+Tracing provides deep visibility into execution of your application by capturing detailed telemetry at each execution step. This helps diagnose issues and enhance performance by identifying problems such as inaccurate tool calls, misleading prompts, high latency, low-quality evaluation scores, and more.  
 
 This article explains how to implement tracing for AI applications using **OpenAI SDK** with OpenTelemetry in Azure AI Foundry.
 
@@ -24,47 +24,45 @@ You need the following to complete this tutorial:
 
 * An AI application that uses **OpenAI SDK** to make calls to models hosted in Azure AI Foundry.
 
-
 ## Enable tracing in your project
 
-Azure AI Foundry stores traces in Azure Application Insight resources using OpenTelemetry. By default, new Azure AI Foundry resources don't provision these resources. You can connect projects to an existing Azure Application Insights resource or create a new one from within the project. You do such configuration once per each Azure AI Foundry resource.
+Azure AI Foundry stores traces in Azure Application Insights resources using OpenTelemetry. By default, new Azure AI Foundry resources don't provision these resources. You can connect projects to an existing Azure Application Insights resource or create a new one from within the project. You do this configuration once per each Azure AI Foundry resource.
 
 The following steps show how to configure your resource:
 
 1. Go to [Azure AI Foundry portal](https://ai.azure.com) and navigate to your project.
 
-2. On the side navigation bar, select **Tracing**.
+1. On the side navigation bar, select **Tracing**.
 
-3. If an Azure Application Insights resource isn't associated with your Azure AI Foundry resource, associate one.
+1. If an Azure Application Insights resource isn't associated with your Azure AI Foundry resource, associate one. If you already have an Application Insights resource associated, you won't see the enable page below and you can skip this step.
 
     :::image type="content" source="../../media/how-to/develop/trace-application/configure-app-insight.png" alt-text="A screenshot showing how to configure Azure Application Insights to the Azure AI Foundry resource." lightbox="../../media/how-to/develop/trace-application/configure-app-insight.png":::
 
-4. To reuse an existing Azure Application Insights, use the drop-down **Application Insights resource name** to locate the resource and select **Connect**.
+    1. To reuse an existing Azure Application Insights, use the drop-down **Application Insights resource name** to locate the resource and select **Connect**.
 
-    > [!TIP]
-    > To connect to an existing Azure Application Insights, you need at least contributor access to the Azure AI Foundry resource (or Hub). 
+        > [!TIP]
+        > To connect to an existing Azure Application Insights, you need at least contributor access to the Azure AI Foundry resource (or Hub).
 
-5. To connect to a new Azure Application Insights resource, select the option **Create new**.
+    1. To connect to a new Azure Application Insights resource, select the option **Create new**.
 
-    1. Use the configuration wizard to configure the new resource's name.
+        1. Use the configuration wizard to configure the new resource's name.
 
-    2. By default, the new resource is created in the same resource group where the Azure AI Foundry resource was created. Use the **Advance settings** option to configure a different resource group or subscription.
+        1. By default, the new resource is created in the same resource group where the Azure AI Foundry resource was created. Use the **Advance settings** option to configure a different resource group or subscription.
 
-        > [!TIP]
-        > To create a new Azure Application Insight resource, you also need contributor role to the resource group you selected (or the default one).
+            > [!TIP]
+            > To create a new Azure Application Insights resource, you also need contributor role to the resource group you selected (or the default one).
 
-    3. Select **Create** to create the resource and connect it to the Azure AI Foundry resource.
+        1. Select **Create** to create the resource and connect it to the Azure AI Foundry resource.
 
-4. Once the connection is configured, you are ready to use tracing in any project within the resource.
+    1. Once the connection is configured, you're ready to use tracing in any project within the resource.
 
-5. Go to the landing page of your project and copy the project's endpoint URI. You need it later in the tutorial.
+1. Go to the landing page of your project and copy the project's endpoint URI. You need it later.
 
     :::image type="content" source="../../media/how-to/projects/fdp-project-overview.png" alt-text="A screenshot showing how to copy the project endpoint URI." lightbox="../../media/how-to/projects/fdp-project-overview.png":::
 
     > [!IMPORTANT]
     > Using a project's endpoint requires configuring Microsoft Entra ID in your application. If you don't have Entra ID configured, use the Azure Application Insights connection string as indicated in step 3 of the tutorial.
 
-
 ## Instrument the OpenAI SDK
 
 When developing with the OpenAI SDK, you can instrument your code so traces are sent to Azure AI Foundry. Follow these steps to instrument your code:
@@ -129,15 +127,15 @@ When developing with the OpenAI SDK, you can instrument your code so traces are
 
     :::image type="content" source="../../media/how-to/develop/trace-application/tracing-display-simple.png" alt-text="A screenshot showing how a simple chat completion request is displayed in the trace." lightbox="../../media/how-to/develop/trace-application/tracing-display-simple.png":::
 
-1. It may be useful to capture sections of your code that mixes business logic with models when developing complex applications. OpenTelemetry uses the concept of spans to capture sections you're interested in. To start generating your own spans, get an instance of the current **tracer** object.
+1. It might be useful to capture sections of your code that mixes business logic with models when developing complex applications. OpenTelemetry uses the concept of spans to capture sections you're interested in. To start generating your own spans, get an instance of the current **tracer** object.
 
     ```python
     from opentelemetry import trace
 
     tracer = trace.get_tracer(__name__)
     ```
 
-1. Then, use decorators in your method to capture specific scenarios in your code that you are interested in. Such decorators generate spans automatically. The following code example instruments a method called `assess_claims_with_context` with iterates over a list of claims and verify if the claim is supported by the context using an LLM. All the calls made in this method are captured within the same span:
+1. Then, use decorators in your method to capture specific scenarios in your code that you're interested in. These decorators generate spans automatically. The following code example instruments a method called `assess_claims_with_context` that iterates over a list of claims and verifies if the claim is supported by the context using an LLM. All the calls made in this method are captured within the same span:
 
     ```python
     def build_prompt_with_context(claim: str, context: str) -> str:
@@ -170,7 +168,7 @@ When developing with the OpenAI SDK, you can instrument your code so traces are
 
     :::image type="content" source="../../media/how-to/develop/trace-application/tracing-display-decorator.png" alt-text="A screenshot showing how a method using a decorator is displayed in the trace." lightbox="../../media/how-to/develop/trace-application/tracing-display-decorator.png":::
 
-1. You may also want to add extra information to the current span. OpenTelemetry uses the concept of **attributes** for that. Use the `trace` object to access them and include extra information. See how the `assess_claims_with_context` method has been modified to include an attribute:
+1. You might also want to add extra information to the current span. OpenTelemetry uses the concept of **attributes** for that. Use the `trace` object to access them and include extra information. See how the `assess_claims_with_context` method has been modified to include an attribute:
 
     ```python
     @tracer.start_as_current_span("assess_claims_with_context")
@@ -188,12 +186,11 @@ When developing with the OpenAI SDK, you can instrument your code so traces are
             responses.append(response.choices[0].message.content.strip('., '))
 
         return responses
-    ``` 
-
+    ```
 
 ## Trace to console
 
-It may be useful to also trace your application and send the traces to the local execution console. Such approach may be beneficial when running unit tests or integration tests in your application using an automated CI/CD pipeline. Traces can be sent to the console and captured by your CI/CD tool to further analysis.
+It might be useful to also trace your application and send the traces to the local execution console. This approach might be beneficial when running unit tests or integration tests in your application using an automated CI/CD pipeline. Traces can be sent to the console and captured by your CI/CD tool for further analysis.
 
 Configure tracing as follows:
 
@@ -271,6 +268,6 @@ Configure tracing as follows:
     }
     ```
 
-## Next steps
+## Related content
 
 * [Trace agents using Azure AI Foundry SDK](trace-agents-sdk.md)

articles/ai-foundry/how-to/view-ai-red-teaming-results.md

@@ -6,7 +6,7 @@ ms.service: azure-ai-foundry
 ms.custom:
   - references_regions
 ms.topic: how-to
-ms.date: 06/03/2025
+ms.date: 08/29/2025
 ms.reviewer: minthigpen
 ms.author: lagayhar
 author: lgayhardt

articles/ai-services/document-intelligence/prebuilt/batch-analysis.md

@@ -5,7 +5,7 @@ description: Learn about the Document Intelligence Batch analysis API
 author: laujan
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 02/25/2025
+ms.date: 08/28/2025
 ms.author: lajanuar
 monikerRange: '>=doc-intel-4.0.0'
 ---
@@ -53,7 +53,7 @@ Review [Managed identities for Document Intelligence](../authentication/managed-
 
 Review [**Create SAS tokens**](../authentication/create-sas-tokens.md) to learn more about generating SAS tokens and how they work.
 
-## Calling the batch analysis API
+## Call the batch analysis API
 
 ### 1. Specify the input files
 
@@ -62,16 +62,19 @@ The batch API supports two options for specifying the files to be processed.
 * If you want to process all the files in a container or a folder, and the number of files is less than the 10000 limit, use the ```azureBlobSource``` object in your request.
 
     ```bash
-    POST /documentModels/{modelId}:analyzeBatch
+    POST {endpoint}/documentintelligence/documentModels/{modelId}:analyzeBatch?api-version=2024-11-30
 
     {
       "azureBlobSource": {
-        "containerUrl": "https://myStorageAccount.blob.core.windows.net/myContainer?mySasToken",
-        ...
-      },
-     ...
+        "containerUrl": "https://myStorageAccount.blob.core.windows.net/myContainer?mySasToken"
+      
+    },
+    {
+       "resultContainerUrl": "https://myStorageAccount.blob.core.windows.net/myOutputContainer?mySasToken",
+       "resultPrefix": "trainingDocsResult/"
     }
 
+
     ```
 
 * If you don't want to process all the files in a container or folder, but rather specific files in that container or folder, use the ```azureBlobFileListSource``` object. This operation requires a File List JSONL file which lists the files to be processed. Store the JSONL file in the root folder of the container. Here's an example JSONL file with two files listed:
@@ -88,7 +91,7 @@ Use a file list `JSONL` file with the following conditions:
   * When you want more control over which files get processed in each batch request;
 
    ```bash
-   POST /documentModels/{modelId}:analyzeBatch
+   POST {endpoint}/documentintelligence/documentModels/{modelId}:analyzeBatch?api-version=2024-11-30
 
    {
      "azureBlobFileListSource": {
@@ -119,13 +122,14 @@ Remember to replace the following sample container URL values with real values f
 
 This example shows a POST request with `azureBlobSource` input
 ```bash
-POST /documentModels/{modelId}:analyzeBatch
+POST {endpoint}/documentintelligence/documentModels/{modelId}:analyzeBatch?api-version=2024-11-30
 
 {
   "azureBlobSource": {
     "containerUrl": "https://myStorageAccount.blob.core.windows.net/myContainer?mySasToken",
     "prefix": "inputDocs/"
   },
+  {
   "resultContainerUrl": "https://myStorageAccount.blob.core.windows.net/myOutputContainer?mySasToken",
   "resultPrefix": "batchResults/",
   "overwriteExisting": true
@@ -137,13 +141,14 @@ This example shows a POST request with `azureBlobFileListSource` and a file list
 
 
 ```bash
-POST /documentModels/{modelId}:analyzeBatch
+POST {endpoint}/documentintelligence/documentModels/{modelId}:analyzeBatch?api-version=2024-11-30
 
 {
    "azureBlobFileListSource": {
       "containerUrl": "https://myStorageAccount.blob.core.windows.net/myContainer?mySasToken",
       "fileList": "myFileList.jsonl"
     },
+{
   "resultContainerUrl": "https://myStorageAccount.blob.core.windows.net/myOutputContainer?mySasToken",
   "resultPrefix": "batchResults/",
   "overwriteExisting": true
@@ -155,7 +160,7 @@ Here's an example **successful** response
 
 ```bash
 202 Accepted
-Operation-Location: /documentModels/{modelId}/analyzeBatchResults/{resultId}
+Operation-Location: /documentintelligence/documentModels/{modelId}/analyzeBatchResults/{resultId}?api-version=2024-11-30
 ```
 
 ### 4. Retrieve API results
@@ -164,7 +169,7 @@ Use the `GET` operation to retrieve batch analysis results after the POST operat
 
 
 ```bash
-GET /documentModels/{modelId}/analyzeBatchResults/{resultId}
+GET {endpoint}/documentintelligence/documentModels/{modelId}/analyzeBatchResults/{resultId}?api-version=2024-11-30
 200 OK
 
 {