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@@ -30,6 +37,10 @@ This guide walks you through how to investigate failures, common errors in the `
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- Additionally, if you're using a virtual network or private link, and your evaluation run upload fails because of that, check out this [guide](https://docs.microsoft.com/azure/machine-learning/how-to-enable-studio-virtual-network#access-data-using-the-studio).
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### Troubleshoot Column Mapping Issues
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- When using `column_mapping` parameter in evaluators, ensure all keys and values are non-empty strings and contain only alphanumeric characters. Empty strings, non-string values, or non-alphanumeric characters can cause serialization errors and issues in downstream applications. Example of valid mapping: `{"query": "${data.query}", "response": "${data.response}"}`.
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### Troubleshoot Safety Evaluator Issues
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- Risk and safety evaluators depend on the Azure AI Studio safety evaluation backend service. For a list of supported regions, please refer to the documentation [here](https://aka.ms/azureaisafetyeval-regionsupport).
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Identify the type of simulations being run (adversarial or non-adversarial).
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Adjust parameters such as `api_call_retry_sleep_sec`, `api_call_delay_sec`, and `concurrent_async_task`. Please note that rate limits to llm calls can be both tokens per minute and requests per minute.
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## Handle RedTeam errors
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### Target resource not found
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When initializing an Azure OpenAI model directly as `target` for a `RedTeam` scan, ensure `azure_endpoint` is specified in the format `https://<hub>.openai.azure.com/openai/deployments/<deployment_name>/chat/completions?api-version=2025-01-01-preview`. If using `AzureOpenAI`, `endpoint` should be specified in the format `https://<hub>.openai.azure.com/`.
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### Insufficient Storage Permissions
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If you see an error like `WARNING: Failed to log artifacts to MLFlow: (UserError) Failed to upload evaluation run to the cloud due to insufficient permission to access the storage`, you need to ensure that proper permissions are assigned to the storage account linked to your Azure AI Project.
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To fix this issue:
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1. Open the associated resource group being used in your Azure AI Project in the Azure Portal
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2. Look up the storage accounts associated with that resource group
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3. Open each storage account and click on "Access control (IAM)" on the left side navigation
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4. Add permissions for the desired users with the "Storage Blob Data Contributor" role
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If you have Azure CLI, you can use the following command:
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```Shell
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# <mySubscriptionID>: Subscription ID of the Azure AI Studio hub's linked storage account (available in Azure AI hub resource view in Azure Portal).
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# <myResourceGroupName>: Resource group of the Azure AI Studio hub's linked storage account.
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# <user-id>: User object ID for role assignment (retrieve with "az ad user show" command).
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az role assignment create --role "Storage Blob Data Contributor" --scope /subscriptions/<mySubscriptionID>/resourceGroups/<myResourceGroupName> --assignee-principal-type User --assignee-object-id "<user-id>"
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```
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## Logging
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You can set logging level via environment variable `PF_LOGGING_LEVEL`, valid values includes `CRITICAL`, `ERROR`, `WARNING`, `INFO`, `DEBUG`, default to `INFO`.
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