Merge pull request #6342 from MicrosoftDocs/main

Auto Publish – main to live - 2025-08-01 11:00 UTC

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

articles/ai-foundry/concepts/evaluation-evaluators/custom-evaluators.md

@@ -6,7 +6,7 @@ author: lgayhardt
 ms.author: lagayhar
 manager: scottpolly
 ms.reviewer: mithigpe
-ms.date: 05/19/2025
+ms.date: 07/31/2025
 ms.service: azure-ai-foundry
 ms.topic: reference
 ms.custom:
@@ -28,7 +28,7 @@ Sometimes a large language model isn't needed for certain evaluation metrics. Th
 class AnswerLengthEvaluator:
     def __init__(self):
         pass
-    # A class is made a callable my implementing the special method __call__
+    # A class is made callable by implementing the special method __call__
     def __call__(self, *, answer: str, **kwargs):
         return {"answer_length": len(answer)}
 ```

articles/ai-foundry/concepts/evaluation-evaluators/risk-safety-evaluators.md

@@ -6,7 +6,7 @@ author: lgayhardt
 ms.author: lagayhar
 manager: scottpolly
 ms.reviewer: mithigpe
-ms.date: 05/19/2025
+ms.date: 07/31/2025
 ms.service: azure-ai-foundry
 ms.topic: reference
 ms.custom:
@@ -62,7 +62,7 @@ azure_ai_project = {
 }
 ## Using Azure AI Foundry Development Platform, example: AZURE_AI_PROJECT=https://your-account.services.ai.azure.com/api/projects/your-project
 azure_ai_project = os.environ.get("AZURE_AI_PROJECT")
-credential=DefaultAzureCredential()
+credential = DefaultAzureCredential()
 ```
 
 ## Hateful and unfair content

articles/ai-foundry/concepts/evaluation-evaluators/textual-similarity-evaluators.md

@@ -6,7 +6,7 @@ author: lgayhardt
 ms.author: lagayhar
 manager: scottpolly
 ms.reviewer: changliu2
-ms.date: 05/19/2025
+ms.date: 07/31/2025
 ms.service: azure-ai-foundry
 ms.topic: reference
 ms.custom:
@@ -30,7 +30,7 @@ load_dotenv()
 
 model_config = AzureOpenAIModelConfiguration(
     azure_endpoint=os.environ["AZURE_ENDPOINT"],
-    api_key=os.environ.get["AZURE_API_KEY"],
+    api_key=os.environ.get("AZURE_API_KEY"),
     azure_deployment=os.environ.get("AZURE_DEPLOYMENT_NAME"),
     api_version=os.environ.get("AZURE_API_VERSION"),
 )
@@ -50,7 +50,7 @@ from azure.ai.evaluation import SimilarityEvaluator
 
 similarity = SimilarityEvaluator(model_config=model_config, threshold=3)
 similarity(
-    query="Is Marie Curie is born in Paris?", 
+    query="Is Marie Curie born in Paris?", 
     response="According to wikipedia, Marie Curie was not born in Paris but in Warsaw.",
     ground_truth="Marie Curie was born in Warsaw."
 )
@@ -134,7 +134,6 @@ The numerical score is a 0-1 float and a higher score is better. Given a numeric
 ```python
 from azure.ai.evaluation import GleuScoreEvaluator
 
-
 gleu_score = GleuScoreEvaluator(threshold=0.2)
 gleu_score(
     response="According to wikipedia, Marie Curie was not born in Paris but in Warsaw.",
@@ -168,7 +167,6 @@ rouge(
     response="According to wikipedia, Marie Curie was not born in Paris but in Warsaw.",
     ground_truth="Marie Curie was born in Warsaw."
 )
-
 ```
 
 ### ROUGE score output
@@ -203,7 +201,6 @@ meteor_score(
     response="According to wikipedia, Marie Curie was not born in Paris but in Warsaw.",
     ground_truth="Marie Curie was born in Warsaw."
 )
-
 ```
 
 ### METEOR score output

articles/ai-foundry/how-to/benchmark-model-in-catalog.md

@@ -7,7 +7,7 @@ ms.service: azure-ai-foundry
 ms.custom:
   - ai-learning-hub
 ms.topic: how-to
-ms.date: 05/19/2025
+ms.date: 07/31/2025
 ms.reviewer: changliu2
 reviewer: changliu2
 ms.author: lagayhar  
@@ -22,7 +22,7 @@ In this article, you learn to streamline your model selection process in the Azu
 
 - [Quality, safety, cost, and performance leaderboards](#access-model-leaderboards) to quickly identify the model leaders along a single criterion (quality, cost, or throughput);
 - [Trade-off charts](#compare-models-in-the-trade-off-charts) to see how models perform on one metric versus another, such as quality versus cost, among different selection criteria;
-- [Leaderboards by scenario](#view-leaderboards-by-scenario) to find the best leaderboards that suite your scenario.
+- [Leaderboards by scenario](#view-leaderboards-by-scenario) to find the best leaderboards that suit your scenario.
 
 ## Prerequisites
 

articles/ai-foundry/how-to/continuous-evaluation-agents.md

@@ -5,7 +5,7 @@ description: This article provides instructions on how to continuously evaluate
 manager: scottpolly
 ms.service: azure-ai-foundry
 ms.topic: how-to
-ms.date: 05/19/2025
+ms.date: 07/31/2025
 ms.reviewer: amibp
 ms.author: lagayhar  
 author: lgayhardt
@@ -20,6 +20,7 @@ Continuous evaluation for Agents provides near real-time observability and monit
 ## Getting Started
 
 ### Prerequisites
+
 [!INCLUDE [uses-fdp-only](../includes/uses-fdp-only.md)] 
 
 - An agent created within the project
@@ -51,22 +52,22 @@ project_client = AIProjectClient.from_connection_string(
 agent = project_client.agents.create_agent(
     model=os.environ["MODEL_DEPLOYMENT_NAME"],
     name="my-assistant",
-    instructions="You are helpful assistant",
+    instructions="You are a helpful assistant",
     tools=file_search_tool.definitions,
     tool_resources=file_search_tool.resources,
 )
 
 # Create thread and process user message
-thread = project.agents.create_thread()
-project.agents.create_message(thread_id=thread.id, role="user", content="Hello, what Contoso products do you know?")
-run = project.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
+thread = project_client.agents.create_thread()
+project_client.agents.create_message(thread_id=thread.id, role="user", content="Hello, what Contoso products do you know?")
+run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
 
 # Handle run status
 if run.status == "failed":
     print(f"Run failed: {run.last_error}")
 
 # Print thread messages
-for message in project.agents.list_messages(thread_id=thread.id).text_messages:
+for message in project_client.agents.list_messages(thread_id=thread.id).text_messages:
     print(message)
 
 ```
@@ -89,12 +90,12 @@ evaluators={
 
 ```python
 
-project.evaluation.create_agent_evaluation(
+project_client.evaluation.create_agent_evaluation(
     AgentEvaluationRequest(  
         thread=thread.id,  
         run=run.id,   
         evaluators=evaluators,
-        appInsightsConnectionString = project.telemetry.get_connection_string(),
+        appInsightsConnectionString = project_client.telemetry.get_connection_string(),
     )
 )
 
@@ -144,7 +145,7 @@ except HttpResponseError as err:
 
 ### Capture reasoning explanations for your evaluation result
 
-AI-assisted evaluators employ chain-of-thought reasoning to generate an explanation for the score in your evaluation result. To enable this on, set redact_score_properties to True in the AgentEvaluationRedactionConfiguration object and pass that as part of your request.
+AI-assisted evaluators employ chain-of-thought reasoning to generate an explanation for the score in your evaluation result. To enable this, set redact_score_properties to False in the AgentEvaluationRedactionConfiguration object and pass that as part of your request.
 
 This helps you understand the reasoning behind the scores for each metric.
 
@@ -155,7 +156,7 @@ This helps you understand the reasoning behind the scores for each metric.
 
 from azure.ai.projects.models import AgentEvaluationRedactionConfiguration
 
-project.evaluation.create_agent_evaluation(
+project_client.evaluation.create_agent_evaluation(
     AgentEvaluationRequest(  
         thread=thread.id,  
         run=run.id,   
@@ -171,24 +172,24 @@ project.evaluation.create_agent_evaluation(
 
 ### Customize your sampling configuration
 
-You can customize the sampling configuration by defining an `AgentEvaluationSamplingConfiguration` and specify your preferred sampling percent and maximum requests hour within the system limit of 1000/hour.
+You can customize the sampling configuration by defining an `AgentEvaluationSamplingConfiguration` and specify your preferred sampling percent and maximum requests per hour within the system limit of 1000/hour.
 
 ```python
 
-from azure.ai.projects.models
+from azure.ai.projects.models 
 
 sampling_config = AgentEvaluationSamplingConfiguration (  
     name = agent.id,  
     samplingPercent = 15,       # Percentage of sampling per hour (0-100)
     maxRequestRate = 250,       # Maximum request rate per hour (0-1000)
 )                                
-project.evaluation.create_agent_evaluation(
+project_client.evaluation.create_agent_evaluation(
     AgentEvaluationRequest(  
         thread=thread.id,  
         run=run.id,   
         evaluators=evaluators,  
         samplingConfiguration = sampling_config,  
-        appInsightsConnectionString = project.telemetry.get_connection_string(),
+        appInsightsConnectionString = project_client.telemetry.get_connection_string(),
     )
 )
 ```
@@ -198,7 +199,7 @@ project.evaluation.create_agent_evaluation(
 
 ## Viewing continuous evaluation results
 
-After you deployed your application to production with continuous evaluation setup, you can [monitor the quality and safety of your agent with Azure AI Foundry and Azure Monitor](./monitor-applications.md).
+After you deploy your application to production with continuous evaluation setup, you can [monitor the quality and safety of your agent with Azure AI Foundry and Azure Monitor](./monitor-applications.md).
 
 ## Related content
 

articles/ai-foundry/how-to/monitor-applications.md

@@ -5,7 +5,7 @@ description: This article provides instructions on how to continuously monitor G
 manager: scottpolly
 ms.service: azure-ai-foundry
 ms.topic: how-to
-ms.date: 05/19/2025
+ms.date: 07/31/2025
 ms.reviewer: amibp
 ms.author: lagayhar  
 author: lgayhardt
@@ -15,7 +15,7 @@ author: lgayhardt
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
-Monitoring your generative AI applications has never been more important, due to the complexity and rapid evolvement of the AI industry. Azure AI Foundry Observability, integrated with Azure Monitor Application Insights, enables you to continuously monitor your deployed AI applications to ensure that they're performant, safe, and produce high-quality results in production. In addition to the continuous monitoring capabilities, we also provide [continuous evaluation capabilities for Agents](./continuous-evaluation-agents.md) to add further enhance the Foundry Observability dashboard with visibility into additional critical quality and safety metrics.
+Monitoring your generative AI applications has never been more important, due to the complexity and rapid evolution of the AI industry. Azure AI Foundry Observability, integrated with Azure Monitor Application Insights, enables you to continuously monitor your deployed AI applications to ensure that they're performant, safe, and produce high-quality results in production. In addition to the continuous monitoring capabilities, we also provide [continuous evaluation capabilities for Agents](./continuous-evaluation-agents.md) to further enhance the Foundry Observability dashboard with visibility into additional critical quality and safety metrics.
 
 [!INCLUDE [uses-fdp-only](../includes/uses-fdp-only.md)] 
 ## How to enable monitoring
@@ -57,7 +57,7 @@ Follow these steps to access and utilize the built-in monitoring view in your AI
 
 Application Insights is a powerful tool for application performance monitoring (APM) that provides insights into the health and performance of your applications.
 
-You can open the **Application analytics** dashboard in Azure Monitor Application Insights workbooks gallery by selecting on **View in Azure Monitor Application Insights** link at the end of the page.
+You can open the **Application analytics** dashboard in Azure Monitor Application Insights workbooks gallery by selecting the **View in Azure Monitor Application Insights** link at the end of the page.
 
 This dashboard is opened as an editable workbook where you can customize the workbook and save according to your needs.
 
@@ -70,14 +70,14 @@ This dashboard is opened as an editable workbook where you can customize the wor
 3. Save your latest changes and create different views as needed by selecting **Save**.
     :::image type="content" source="../media/how-to/monitor-applications/customize-dashboard-4.png" alt-text="Screenshot of workbooks tab under monitoring highlighting the save button and tab in Azure portal." lightbox="../media/how-to/monitor-applications/customize-dashboard-4.png":::
 
-4. Share with your team by selecting "Share" icon in the command bar.
+4. Share with your team by selecting the **Share** icon in the command bar.
     :::image type="content" source="../media/how-to/monitor-applications/customize-dashboard-5.png" alt-text="Screenshot of workbooks tab under monitoring highlighting share workbook button and tab in Azure portal." lightbox="../media/how-to/monitor-applications/customize-dashboard-5.png":::
 
 ## Explore and analyze with Kusto Query Language (KQL)
 
 [KQL (Kusto Query Language)](/kusto/query/) is a powerful query language used in Azure to explore, analyze, and visualize large volumes of telemetry and log data.
 
-In the **Application analytics** dashboard view, you can **Open query link** by selecting on the icon in the top right for a particular tile or chart.
+In the **Application analytics** dashboard view, you can **Open query link** by selecting the icon in the top right for a particular tile or chart.
 
 :::image type="content" source="../media/how-to/monitor-applications/query-link.png" alt-text="Screenshot of application analytics dashboard view highlighting the open query link button in Azure portal." lightbox="../media/how-to/monitor-applications/query-link.png":::
 
@@ -91,7 +91,7 @@ You can define Azure Alert rules based on the previous KQL queries to proactivel
 
 :::image type="content" source="../media/how-to/monitor-applications/create-new-alert-rule-1.png" alt-text="Screenshot of logs highlighting new alert rule button in Azure portal." lightbox="../media/how-to/monitor-applications/create-new-alert-rule-1.png":::
 
-Selecting on the **New alert rule** button opens a wizard to create an alert rule on the related signal.
+Selecting the **New alert rule** button opens a wizard to create an alert rule on the related signal.
 
 :::image type="content" source="../media/how-to/monitor-applications/create-new-alert-rule-2.png" alt-text="Screenshot of create an alert rule wizard in Azure portal." lightbox="../media/how-to/monitor-applications/create-new-alert-rule-2.png":::