Merge pull request #4211 from MicrosoftDocs/main

4/17/2025 PM Publish

Author: Taojunshen

.acrolinx-config.edn

@@ -1,2 +1,2 @@
 {:allowed-branchname-matches ["main" "release-.*"]
- :allowed-filename-matches ["(?i)articles/(?:(?!active-directory/saas-apps/toc.yml|role-based-access-control/resource-provider-operations.md|.*policy/samples/|.*resource-graph/samples/))" "(?i)includes/(?:(?!policy/reference/|policy/standards/|resource-graph/samples/))"]}
+ :allowed-filename-matches ["articles"]}

articles/ai-foundry/how-to/costs-plan-manage.md

@@ -111,7 +111,7 @@ Here's an example of how to monitor costs for a project. The costs are used as a
 1. Under the **Project** heading, select **Overview**. 
 1. Select **View cost for resources** from the **Total cost** section. The [Azure portal](https://portal.azure.com) opens to the resource group for your project.
 
-    :::image type="content" source="../media/cost-management/project-costs/project-settings-go-view-costs.png" alt-text="Screenshot of the Azure AI Foundry portal portal showing how to see project settings." lightbox="../media/cost-management/project-costs/project-settings-go-view-costs.png":::
+    :::image type="content" source="../media/cost-management/project-costs/project-settings-go-view-costs.png" alt-text="Screenshot of the Azure AI Foundry portal showing how to see project settings." lightbox="../media/cost-management/project-costs/project-settings-go-view-costs.png":::
 
 1. Expand the **Resource** column to see the costs for each service that's underlying your [project](../concepts/ai-resources.md#organize-work-in-projects-for-customization). But this view doesn't include costs for all resources that you use in a project.
 

articles/ai-foundry/how-to/develop/evaluate-sdk.md

@@ -233,7 +233,7 @@ import os
 from azure.identity import DefaultAzureCredential
 credential = DefaultAzureCredential()
 
-# Initialize Azure AI project and Azure OpenAI conncetion with your environment variables
+# Initialize Azure AI project and Azure OpenAI connection with your environment variables
 azure_ai_project = {
     "subscription_id": os.environ.get("AZURE_SUBSCRIPTION_ID"),
     "resource_group_name": os.environ.get("AZURE_RESOURCE_GROUP"),
@@ -250,7 +250,7 @@ model_config = {
 
 from azure.ai.evaluation import GroundednessProEvaluator, GroundednessEvaluator
 
-# Initialzing Groundedness and Groundedness Pro evaluators
+# Initializing Groundedness and Groundedness Pro evaluators
 groundedness_eval = GroundednessEvaluator(model_config)
 groundedness_pro_eval = GroundednessProEvaluator(azure_ai_project=azure_ai_project, credential=credential)
 

articles/ai-foundry/how-to/develop/run-scans-ai-red-teaming-agent.md

@@ -224,7 +224,7 @@ More advanced users can specify the desired attack strategies instead of using d
 
 Each new attack strategy specified will be applied to the set of baseline adversarial queries used in addition to the baseline adversarial queries.
 
-This following example would generate one attack objective per each of the four risk categories specified. This will first, generate four baseline adversarial prompts which would be sent to your target. Then, each baseline query would get converted into each of the four attack strategies. This will result in a total of 20 attack-response pairs from your AI system. The last attack stratgy is an example of a composition of two attack strategies to create a more complex attack query: the `AttackStrategy.Compose()` function takes in a list of two supported attack strategies and chains them together. The example's composition would first encode the baseline adversarial query into Base64 then apply the ROT13 cipher on the Base64-encoded query. Compositions only support chaining two attack strategies together.
+This following example would generate one attack objective per each of the four risk categories specified. This will first, generate four baseline adversarial prompts which would be sent to your target. Then, each baseline query would get converted into each of the four attack strategies. This will result in a total of 20 attack-response pairs from your AI system. The last attack strategy is an example of a composition of two attack strategies to create a more complex attack query: the `AttackStrategy.Compose()` function takes in a list of two supported attack strategies and chains them together. The example's composition would first encode the baseline adversarial query into Base64 then apply the ROT13 cipher on the Base64-encoded query. Compositions only support chaining two attack strategies together.
 
 ```python
 red_team_agent = RedTeam(

articles/ai-foundry/how-to/develop/sdk-overview.md

@@ -158,7 +158,10 @@ Create a project client in code:
 
 # [Sync](#tab/sync)
 
-:::code language="csharp" source="~/azureai-samples-csharp/scenarios/projects/basic-csharp/Program.cs" id="snippet_get_project":::
+```csharp
+var connectionString = "<your_connection_string>";
+var projectClient = new AIProjectClient(connectionString, new DefaultAzureCredential());
+```
 
 # [Async](#tab/async)
 
@@ -226,7 +229,33 @@ using Azure.AI.OpenAI;
 
 If you have existing code that uses the OpenAI SDK, you can use the project client to create an `AzureOpenAI` client that uses your project's Azure OpenAI connection:
 
-:::code language="csharp" source="~/azureai-samples-csharp/scenarios/projects/basic-csharp/Program.cs" id="azure_openai":::
+```csharp
+var connections = projectClient.GetConnectionsClient();
+ConnectionResponse connection = connections.GetDefaultConnection(ConnectionType.AzureOpenAI, withCredential: true);
+var properties = connection.Properties as ConnectionPropertiesApiKeyAuth;
+
+if (properties == null) {
+    throw new Exception("Invalid auth type, expected API key auth");
+}
+
+// Create and use an Azure OpenAI client
+AzureOpenAIClient azureOpenAIClient = new(
+    new Uri(properties.Target),
+    new AzureKeyCredential(properties.Credentials.Key));
+
+// This must match the custom deployment name you chose for your model
+ChatClient chatClient = azureOpenAIClient.GetChatClient("gpt-4o-mini");
+
+ChatCompletion completion = chatClient.CompleteChat(
+    [
+        new SystemChatMessage("You are a helpful assistant that talks like a pirate."),
+        new UserChatMessage("Does Azure OpenAI support customer managed keys?"),
+        new AssistantChatMessage("Yes, customer managed keys are supported by Azure OpenAI"),
+        new UserChatMessage("Do other Azure AI services support this too?")
+    ]);
+
+Console.WriteLine($"{completion.Role}: {completion.Content[0].Text}");
+```
 
 ::: zone-end
 
@@ -280,7 +309,25 @@ using Azure.AI.Inference;
 
 You can use the project client to get a configured and authenticated `ChatCompletionsClient` or `EmbeddingsClient`:
 
-:::code language="csharp" source="~/azureai-samples-csharp/scenarios/projects/basic-csharp/Program.cs" id="snippet_inference":::
+```csharp
+var connectionString = Environment.GetEnvironmentVariable("AIPROJECT_CONNECTION_STRING");
+var projectClient = new AIProjectClient(connectionString, new DefaultAzureCredential());
+
+ChatCompletionsClient chatClient = projectClient.GetChatCompletionsClient();
+
+var requestOptions = new ChatCompletionsOptions()
+{
+    Messages =
+        {
+            new ChatRequestSystemMessage("You are a helpful assistant."),
+            new ChatRequestUserMessage("How many feet are in a mile?"),
+        },
+    Model = "gpt-4o-mini"
+};
+
+Response<ChatCompletions> response = chatClient.Complete(requestOptions);
+Console.WriteLine(response.Value.Content);
+```
 
 ::: zone-end
 
@@ -405,7 +452,21 @@ using Azure.Search.Documents.Models;
 
 Instantiate the search and/or search index client as desired:
 
-:::code language="csharp" source="~/azureai-samples-csharp/scenarios/projects/basic-csharp/Program.cs" id="azure_aisearch":::
+```csharp
+var connections = projectClient.GetConnectionsClient();
+var connection = connections.GetDefaultConnection(ConnectionType.AzureAISearch, withCredential: true).Value;
+
+var properties = connection.Properties as ConnectionPropertiesApiKeyAuth;
+if (properties == null) {
+    throw new Exception("Invalid auth type, expected API key auth");
+}
+
+SearchClient searchClient = new SearchClient(
+    new Uri(properties.Target),
+    "products",
+    new AzureKeyCredential(properties.Credentials.Key));
+```
+
 
 ::: zone-end
 

articles/ai-foundry/how-to/develop/visualize-traces.md

@@ -101,7 +101,7 @@ For more information on how to send Azure AI Inference traces to Azure Monitor a
 
 From Azure AI Foundry project, you can also open your custom dashboard that provides you with insights specifically to help you monitor your generative AI application.
 
-In this Azure Workbook, you can view your Gen AI spans and jump into the Azure Monitor **End-to-end transaction details view** view to deep dive and investigate.
+In this Azure Workbook, you can view your Gen AI spans and jump into the Azure Monitor **End-to-end transaction details view** to deep dive and investigate.
 
 Learn more about using this workbook to monitor your application, see [Azure Workbook documentation](/azure/azure-monitor/visualize/workbooks-create-workbook).
 

articles/ai-foundry/model-inference/concepts/content-filter.md

@@ -14,7 +14,7 @@ manager: nitinme
 # Content filtering for model inference in Azure AI services
 
 > [!IMPORTANT]
-> The content filtering system isn't applied to prompts and completions processed by the audio models such as Whisper in Azure OpenAI Service. Learn more about the [Audio models in Azure OpenAI](../../../ai-services/openai/concepts/models.md?tabs=standard-audio#standard-models-by-endpoint).
+> The content filtering system isn't applied to prompts and completions processed by the audio models such as Whisper in Azure OpenAI Service. Learn more about the [Audio models in Azure OpenAI](../../../ai-services/openai/concepts/models.md?tabs=standard-audio#standard-deployment-regional-models-by-endpoint).
 
 Azure AI model inference in Azure AI Services includes a content filtering system that works alongside core models and it's powered by [Azure AI Content Safety](https://azure.microsoft.com/products/cognitive-services/ai-content-safety). This system works by running both the prompt and completion through an ensemble of classification models designed to detect and prevent the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. Variations in API configurations and application design might affect completions and thus filtering behavior.
 

articles/ai-foundry/model-inference/concepts/endpoints.md

@@ -38,7 +38,7 @@ To learn more about how to create deployments see [Add and configure model deplo
 
 ## Azure AI inference endpoint
 
-The Azure AI inference endpoint allows customers to use a single endpoint with the same authentication and schema to generate inference for the deployed models in the resource. This endpoint follows the [Azure AI model inference API](.././reference/reference-model-inference-api.md) which all the models in Azure AI model inference support. It support the following modalidities:
+The Azure AI inference endpoint allows customers to use a single endpoint with the same authentication and schema to generate inference for the deployed models in the resource. This endpoint follows the [Azure AI model inference API](.././reference/reference-model-inference-api.md) which all the models in Azure AI model inference support. It support the following modalities:
 
 * Text embeddings
 * Image embeddings

articles/ai-foundry/model-inference/how-to/configure-deployment-policies.md

@@ -31,7 +31,7 @@ Follow these steps to create and assign an example custom policy to control mode
 
 2. From the left side of the Azure Policy Dashboard, select **Authoring**, **Definitions**, and then select **+ Policy definition** from the top of the page.
 
-    :::image type="content" source="../media/configure-deployment-policies/create-new-policy.png" alt-text="An screenshot showing how to create a new policy definition in Azure Policies." lightbox="../media/configure-deployment-policies/create-new-policy.png":::
+    :::image type="content" source="../media/configure-deployment-policies/create-new-policy.png" alt-text="A screenshot showing how to create a new policy definition in Azure Policies." lightbox="../media/configure-deployment-policies/create-new-policy.png":::
 
 3. In the **Policy Definition** form, use the following values:
 
@@ -157,7 +157,7 @@ To monitor compliance with the policy, follow these steps:
 
 1. From the left side of the Azure Policy Dashboard, select **Compliance**. Each policy assignment is listed with the compliance status. To view more details, select the policy assignment. The following example shows the compliance report for a policy that blocks deployments of type *Global standard*.
 
-    :::image type="content" source="../media/configure-deployment-policies/policy-compliance.png" alt-text="An screenshot showing an example of a policy compliance report for a policy that blocks Global standard deployment SKUs." lightbox="../media/configure-deployment-policies/policy-compliance.png":::
+    :::image type="content" source="../media/configure-deployment-policies/policy-compliance.png" alt-text="A screenshot showing an example of a policy compliance report for a policy that blocks Global standard deployment SKUs." lightbox="../media/configure-deployment-policies/policy-compliance.png":::
 
 ## Update the policy assignment
 

articles/ai-foundry/model-inference/includes/use-chat-completions/csharp.md

@@ -337,7 +337,7 @@ foreach (ChatCompletionsToolCall tool in toolsCall)
         Dictionary<string, object> toolArguments = JsonSerializer.Deserialize<Dictionary<string, object>>(toolArgumentsString);
 
         // Here you have to call the function defined. In this particular example we use 
-        // reflection to find the method we definied before in an static class called 
+        // reflection to find the method we definied before in a static class called 
         // `ChatCompletionsExamples`. Using reflection allows us to call a function 
         // by string name. Notice that this is just done for demonstration purposes as a 
         // simple way to get the function callable from its string name. Then we can call