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articles/advisor/view-recommendations.md

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title: View Azure Advisor recommendations that matter to you
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description: View and filter Azure Advisor recommendations to reduce noise.
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ms.topic: article
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ms.date: 04/03/2019
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ms.date: 01/02/2024
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---
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# View Azure Advisor recommendations that matter to you
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1. Select **Configuration** from the menu.
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![Advisor configuration menu](./media/view-recommendations/configuration.png)
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:::image type="content" source="./media/view-recommendations/configuration.png" alt-text="Screenshot of Azure Advisor showing configuration pane.":::
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1. Check the box in the **Include** column for any subscriptions or resource groups to receive Advisor recommendations. If the box is disabled, you may not have permission to make a configuration change on that subscription or resource group. Learn more about [permissions in Azure Advisor](permissions.md).
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1. Click **Apply** at the bottom after you make a change.
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## Filtering your view in the Azure portal
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Configuration settings remain active until changed. If you want to limit the view of recommendations for a single viewing, you can use the drop downs provided at the top of the Advisor panel. From the Overview, High Availability, Security, Performance, Cost, and All Recommendation panels, you can select the Subscriptions, Resource Types, and recommendation status that you want to see.
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Configuration settings remain active until changed. If you want to limit the view of recommendations for a single viewing, you can use the drop downs provided at the top of the Advisor panel. You can filter recommendations by subscription, resource group, workload, resource type, recommendation status and impact. These filters are available for Overview, Score, Cost, Security, Reliability, Operational Excellence, Performance and All Recommendations pages.
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:::image type="content" source="./media/view-recommendations/filtering.png" alt-text="Screenshot of Azure Advisor showing filtering options.":::
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> [!NOTE]
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> Contact your account team to add new workloads to the workload filter or edit workload names.
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## Dismissing and postponing recommendations
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Azure Advisor allows you to dismiss or postpone recommendations on a single resource. If you dismiss a recommendation, you do not see it again unless you manually activate it. However, postponing a recommendation allows you to specify a duration after which the recommendation is automatically activated again. Postponing can be done in the Azure portal or programmatically.
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:::image type="content" source="./media/view-recommendations/postpone-dismiss.png" alt-text="Screenshot of the Use Managed Disks window showing the select column and Postpone and Dismiss actions for a single recommendation highlighted.":::
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### Postpone or dismiss a multiple recommendations in the Azure portal
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### Postpone or dismiss multiple recommendations in the Azure portal
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1. Open [Azure Advisor](https://aka.ms/azureadvisordashboard) in the Azure portal.
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1. Select a recommendation category to view your recommendations.

articles/ai-services/language-service/custom-text-classification/includes/roles-for-resource-and-storage.md

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5. Select **Select members**
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6. Select your subscription, and **Language** as the managed identity. You can search for user names in the **Select** field.
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> ![IMPORTANT]
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> [!IMPORTANT]
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> If you have a virtual network or private endpoint, be sure to select **Allow Azure services on the trusted services list to access this storage account** in the Azure portal.

articles/ai-services/openai/concepts/use-your-data.md

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After ingesting your data, you can start chatting with the model on your data using the chat playground in Azure OpenAI studio, or the following methods:
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* [Web app](#using-the-web-app)
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* [REST API](../reference.md#azure-ai-search)
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* [C#](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/openai/Azure.AI.OpenAI/tests/Samples/Sample08_UseYourOwnData.cs)
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* [C#](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/openai/Azure.AI.OpenAI/tests/Samples/AzureOnYourData.cs)
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* [Java](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/openai/azure-ai-openai/src/samples/java/com/azure/ai/openai/ChatCompletionsWithYourData.java)
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* [JavaScript](https://github.com/Azure/azure-sdk-for-js/blob/main/sdk/openai/openai/samples/v1-beta/javascript/bringYourOwnData.js)
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* [PowerShell](../use-your-data-quickstart.md?tabs=command-line%2Cpowershell&pivots=programming-language-powershell#example-powershell-commands)

articles/ai-services/openai/how-to/gpt-with-vision.md

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],
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"choices": [
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"finish_details": {
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"type": "stop",
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"stop": "<|fim_suffix|>"
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},
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"finish_reason":"stop",
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"index": 0,
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"message": {
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"role": "assistant",
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```
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Every response includes a `"finish_details"` field. The subfield `"type"` has the following possible values:
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Every response includes a `"finish_reason"` field. It has the following possible values:
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- `stop`: API returned complete model output.
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- `max_tokens`: Incomplete model output due to the `max_tokens` input parameter or model's token limit.
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- `length`: Incomplete model output due to the `max_tokens` input parameter or model's token limit.
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- `content_filter`: Omitted content due to a flag from our content filters.
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## Detail parameter settings in image processing: Low, High, Auto
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The detail parameter in the model offers three choices: `low`, `high`, or `auto`, to adjust the way the model interprets and processes images. The default setting is auto, where the model decides between low or high based on the size of the image input.
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Every response includes a `"finish_details"` field. The subfield `"type"` has the following possible values:
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Every response includes a `"finish_reason"` field. It has the following possible values:
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- `stop`: API returned complete model output.
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- `max_tokens`: Incomplete model output due to the `max_tokens` input parameter or model's token limit.
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- `length`: Incomplete model output due to the `max_tokens` input parameter or model's token limit.
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If `finish_details.type` is `stop`, then there is another `"stop"` property that specifies the token that caused the output to end.
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## Use Vision enhancement with video
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GPT-4 Turbo with Vision provides exclusive access to Azure AI Services tailored enhancements. The **video prompt** integration uses Azure AI Vision video retrieval to sample a set of frames from a video and create a transcript of the speech in the video. It enables the AI model to give summaries and answers about video content.
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"parameters": {
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"computerVisionBaseUrl": "<your_computer_vision_endpoint>",
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"indexName": "<name_of_your_index>",
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Every response includes a `"finish_details"` field. The subfield `"type"` has the following possible values:
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Every response includes a `"finish_reason"` field. It has the following possible values:
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- `stop`: API returned complete model output.
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- `max_tokens`: Incomplete model output due to the `max_tokens` input parameter or model's token limit.
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- `length`: Incomplete model output due to the `max_tokens` input parameter or model's token limit.
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### Pricing example for Video prompts
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The pricing for GPT-4 Turbo with Vision is dynamic and depends on the specific features and inputs used. For a comprehensive view of Azure OpenAI pricing see [Azure OpenAI Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/).
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articles/ai-services/policy-reference.md

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title: Built-in policy definitions for Azure AI services
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description: Lists Azure Policy built-in policy definitions for Azure AI services. These built-in policy definitions provide common approaches to managing your Azure resources.
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ms.date: 12/13/2023
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ms.date: 01/02/2024
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author: nitinme
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articles/aks/policy-reference.md

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title: Built-in policy definitions for Azure Kubernetes Service
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description: Lists Azure Policy built-in policy definitions for Azure Kubernetes Service. These built-in policy definitions provide common approaches to managing your Azure resources.
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ms.date: 01/02/2024
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articles/api-management/policy-reference.md

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title: Built-in policy definitions for Azure API Management
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description: Lists Azure Policy built-in policy definitions for Azure API Management. These built-in policy definitions provide approaches to managing your Azure resources.
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ms.date: 01/02/2024
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author: dlepow
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