Merge pull request #4204 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-ai-docs (branch main)

Author: Taojunshen

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/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/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 

articles/ai-services/agents/includes/quickstart-csharp.md

@@ -35,8 +35,17 @@ ms.date: 03/28/2025
 | Run       | Activation of an agent to begin running based on the contents of Thread. The agent uses its configuration and Thread’s Messages to perform tasks by calling models and tools. As part of a Run, the agent appends Messages to the Thread. |
 | Run Step  | A detailed list of steps the agent took as part of a Run. An agent can call tools or create Messages during its run. Examining Run Steps allows you to understand how the agent is getting to its results.                                |
 
+Create a .NET Console project.
+
+```console
+dotnet new console
+```
+
 Install the .NET package to your project. For example if you're using the .NET CLI, run the following command.
 
+>[!Note]
+> Azure.AI.Projects is only available as a prelease version. Please use the "-prerelease" flag to add the package until a release version becomes available.
+
 ```console
 dotnet add package Azure.AI.Projects --prerelease
 dotnet add package Azure.Identity

articles/ai-services/computer-vision/how-to/model-customization.md

@@ -261,7 +261,7 @@ To train a custom model, you need to associate it with a **Dataset** where you p
 
 To create a new dataset, select **add new dataset**. In the popup window, enter a name and select a dataset type for your use case. **Image classification** models apply content labels to the entire image, while **Object detection** models apply object labels to specific locations in the image. **Product recognition** models are a subcategory of object detection models that are optimized for detecting retail products.
 
-:::image type="content" source="../media/customization/create-dataset.png" alt-text="Screenshoot of dialog box to Create new dataset.":::
+:::image type="content" source="../media/customization/create-dataset.png" alt-text="Screenshot of dialog box to Create new dataset.":::
 
 Then, select the container from the Azure Blob Storage account where you stored the training images. Check the box to allow Vision Studio to read and write to the blob storage container. This is a necessary step to import labeled data. Create the dataset.
 

articles/ai-services/computer-vision/includes/quickstarts-sdk/csharp-sdk.md

@@ -128,7 +128,7 @@ Amount Per Serving
 Trans Fat 0g
 Calories 190
 Cholesterol 0mg
-ories from Fat 110
+Calories from Fat 110
 Sodium 20mg
 nt Daily Values are based on Vitamin A 50%
 calorie diet.