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‎.openpublishing.redirection.json

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},
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{
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"source_path_from_root": "/articles/cosmos-db/how-to-move-regions.md",
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"redirect_url": "/azure/operational-excellence/relocation-cosmos-db",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/site-recovery/move-vaults-across-regions.md",
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"redirect_url": "/azure/operational-excellence/relocation-site-recovery",
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"redirect_document_id": false

‎articles/active-directory-b2c/faq.yml

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* API connectors
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* Conditional Access
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- question: |
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I am using rolling refresh tokens for my application and I am getting an invalid_grant error on redeeming newly acquired refresh tokens well within their set validity period. Why does this happen?
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answer: |
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While determining validity for rolling refresh tokens, B2C will consider the initial login time of the user in the application also to calculate the token validity skew. If the user haven't logged out of the application for a very long time, this skew value will exceed the validity period of the token and hence for security reasons the tokens will be considered as invalid. Hence the error. Inform the user to perform a proper logout and login back into the application and this should reset the skew. This scenario is not applicable if refresh token rolling is set as infinite rolling.
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I've revoked the refresh token using Microsoft Graph invalidateAllRefreshTokens, or Microsoft Graph PowerShell, Revoke-MgUserSignInSession. Why is Azure AD B2C still accepting the old refresh token?
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1. Wait for 10 minutes.
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1. Retrieve the `RefreshToken` again.
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> [!TIP]
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> With Azure AD B2C [custom policy](custom-policy-overview.md), you can reduce the above mentioned skew time of 5 minutes (300000 milliseconds) by adjusting the value for InputParameter "TreatAsEqualIfWithinMillseconds" under claim transformation Id "AssertRefreshTokenIssuedLaterThanValidFromDate". This claim transformation can be found in the TrustFrameworkBase.xml file under latest custom policy [stater-pack](https://learn.microsoft.com/en-us/azure/active-directory-b2c/tutorial-create-user-flows?pivots=b2c-custom-policy#get-the-starter-pack).
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- question: |
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I use multiple tabs in a web browser to sign in to multiple applications that I registered in the same Azure AD B2C tenant. When I try to perform a single sign-out, not all of the applications are signed out. Why does this happen?

‎articles/active-directory-b2c/javascript-and-page-layout.md

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[!INCLUDE [active-directory-b2c-choose-user-flow-or-custom-policy](../../includes/active-directory-b2c-choose-user-flow-or-custom-policy.md)]
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With Azure Active Directory B2C (Azure AD B2C) [HTML templates](customize-ui-with-html.md), you can craft your users' identity experiences. Your HTML templates can contain only certain HTML tags and attributes. Basic HTML tags, such as &lt;b&gt;, &lt;i&gt;, &lt;u&gt;, &lt;h1&gt;, and &lt;hr&gt; are allowed. More advanced tags such as &lt;script&gt;, and &lt;iframe&gt; are removed for security reasons but the `<script>` tag should be added in the `<head>` tag.
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With Azure Active Directory B2C (Azure AD B2C) [HTML templates](customize-ui-with-html.md), you can craft your users' identity experiences. Your HTML templates can contain only certain HTML tags and attributes. Basic HTML tags, such as &lt;b&gt;, &lt;i&gt;, &lt;u&gt;, &lt;h1&gt;, and &lt;hr&gt; are allowed. More advanced tags such as &lt;script&gt;, and &lt;iframe&gt; are removed for security reasons but the `<script>` tag should be added in the `<head>` tag. From selfasserted page layout version 2.1.21 / unifiedssp version 2.1.10 / multifactor version 1.2.10 onwards B2C doesn't support adding scripts in `<body>` tag (as this can pose a risk for cross site scripting attack). Migrating existing scripts from `<body>` to `<head>` may at-times require rewriting existing scripts with mutation observers for proper working.
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The `<script>` tag should be added in the `<head>` tag in two ways:
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‎articles/ai-services/computer-vision/concept-model-customization.md

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| Max # training images | 1,000,000 | 200,000 |
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| Max # evaluation images | 100,000 | 100,000 |
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| Min # training images per category | 2 | 2 |
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| Max # tags per image | multiclass: 1 | NA |
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| Max # regions per image | NA | 1,000 |
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| Max # tags per image | 1 | N/A |
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| Max # regions per image | N/A | 1,000 |
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| Max # categories | 2,500 | 1,000 |
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| Min # categories | 2 | 1 |
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| Max image size (Training) | 20 MB | 20 MB |

‎articles/ai-services/content-safety/overview.md

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The following are a few scenarios in which a software developer or team would require a content moderation service:
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- User prompts submitted to a generative AI service.
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- Content produced by generative AI models.
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- Online marketplaces that moderate product catalogs and other user-generated content.
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- Gaming companies that moderate user-generated game artifacts and chat rooms.
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- Social messaging platforms that moderate images and text added by their users.

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

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#### [REST](#tab/rest)
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Send a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/chat/completions?api-version=2023-12-01-preview` where
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Send a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/chat/completions?api-version=2024-02-15-preview` where
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- RESOURCE_NAME is the name of your Azure OpenAI resource
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- DEPLOYMENT_NAME is the name of your GPT-4 Turbo with Vision model deployment
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api_base = '<your_azure_openai_endpoint>' # your endpoint should look like the following https://YOUR_RESOURCE_NAME.openai.azure.com/
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api_key="<your_azure_openai_key>"
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deployment_name = '<your_deployment_name>'
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api_version = '2023-12-01-preview' # this might change in the future
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api_version = '2024-02-15-preview' # this might change in the future
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client = AzureOpenAI(
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api_key=api_key,
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api_version=api_version,
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base_url=f"{api_base}openai/deployments/{deployment_name}/extensions",
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base_url=f"{api_base}openai/deployments/{deployment_name}",
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)
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```
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#### [REST](#tab/rest)
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Send a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/extensions/chat/completions?api-version=2023-12-01-preview` where
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Send a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/chat/completions?api-version=2024-02-15-preview` where
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- RESOURCE_NAME is the name of your Azure OpenAI resource
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#### [REST](#tab/rest)
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1. Prepare a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/extensions/chat/completions?api-version=2023-12-01-preview` where
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1. Prepare a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/chat/completions?api-version=2024-02-15-preview` where
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- RESOURCE_NAME is the name of your Azure OpenAI resource
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‎articles/ai-studio/how-to/deploy-models-mistral.md

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- An [Azure AI Studio hub](../how-to/create-azure-ai-resource.md).
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> [!IMPORTANT]
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> The serverless API model deployment offering for eligible models in the Mistral family is only available in hubs created in the **East US 2** and **Sweden Central** regions. For _Mistral Large_, the serverless API model deployment offering is also available in the **France Central** region.
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> The serverless API model deployment offering for eligible models in the Mistral family is only available in hubs created in the **East US 2** and **Sweden Central** regions.
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- An [Azure AI Studio project](../how-to/create-projects.md).
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- Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Studio. To perform the steps in this article, your user account must be assigned the __Azure AI Developer role__ on the resource group. For more information on permissions, see [Role-based access control in Azure AI Studio](../concepts/rbac-ai-studio.md).
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:::image type="content" source="../media/deploy-monitor/mistral/mistral-large-deploy-pay-as-you-go.png" alt-text="A screenshot showing how to deploy a model as a serverless API." lightbox="../media/deploy-monitor/mistral/mistral-large-deploy-pay-as-you-go.png":::
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1. Select the project in which you want to deploy your model. To deploy the Mistral model, your project must be in the *EastUS2* or *Sweden Central* region. For the Mistral Large model, you can also deploy in a project that's in the *France Central* region.
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1. Select the project in which you want to deploy your model. To deploy the Mistral model, your project must be in the *EastUS2* or *Sweden Central* region.
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1. In the deployment wizard, select the link to **Azure Marketplace Terms** to learn more about the terms of use.
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1. Select the **Pricing and terms** tab to learn about pricing for the selected model.
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1. Select the **Subscribe and Deploy** button. If this is your first time deploying the model in the project, you have to subscribe your project for the particular offering. This step requires that your account has the **Azure AI Developer role** permissions on the resource group, as listed in the prerequisites. Each project has its own subscription to the particular Azure Marketplace offering of the model, which allows you to control and monitor spending. Currently, you can have only one deployment for each model within a project.

‎articles/ai-studio/how-to/deploy-models-timegen-1.md

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- Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Studio. To perform the steps in this article, your user account must be assigned the __Azure AI Developer role__ on the resource group. For more information on permissions, visit [Role-based access control in Azure AI Studio](../concepts/rbac-ai-studio.md).
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### Pricing information
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#### Estimate the number of tokens needed
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Before you create a deployment, it's useful to estimate the number of tokens that you plan to use and be billed for.
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One token corresponds to one data point in your input dataset or output dataset.
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Suppose you have the following input time series dataset:
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| Unique_id | Timestamp | Target Variable | Exogenous Variable 1 | Exogenous Variable 2 |
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|:---------:|:-------------------:|:---------------:|:--------------------:|:--------------------:|
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| BE | 2016-10-22 00:00:00 | 70.00 | 49593.0 | 57253.0 |
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| BE | 2016-10-22 01:00:00 | 37.10 | 46073.0 | 51887.0 |
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To determine the number of tokens, multiply the number of rows (in this example, two) and the number of columns used for forecasting—not counting the unique_id and timestamp columns (in this example, three) to get a total of six tokens.
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| BE | 2016-10-22 02:00:00 | 46.57 |
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| BE | 2016-10-22 03:00:00 | 48.57 |
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#### Estimate the pricing
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There are four pricing meters, as described in the following table:
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| Pricing Meter | Description |
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|-----------------------------------------------|-----------------------------------------------------------------------------------------|
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| paygo-inference-input-tokens | Costs associated with the tokens used as input for inference when *finetune_steps* = 0 |
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| paygo-inference-output-tokens | Costs associated with the tokens used as output for inference when *finetune_steps* = 0 |
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| paygo-finetuned-model-inference-input-tokens | Costs associated with the tokens used as input for inference when *finetune_steps* > 0 |
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| paygo-finetuned-model-inference-output-tokens | Costs associated with the tokens used as output for inference when *finetune_steps* > 0 |
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These steps demonstrate the deployment of TimeGEN-1. To create a deployment:

‎articles/ai-studio/reference/reference-model-inference-api.md

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The Azure AI Model Inference API specifies a set of modalities and parameters that models can subscribe to. However, some models may have further capabilities that the ones the API indicates. On those cases, the API allows the developer to pass them as extra parameters in the payload.
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By setting a header `extra-parameters: allow`, the API will attempt to pass any unknown parameter directly to the underlying model. If the model can handle that parameter, the request completes.
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By setting a header `extra-parameters: pass-through`, the API will attempt to pass any unknown parameter directly to the underlying model. If the model can handle that parameter, the request completes.
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```JSON
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> The default value for `extra-parameters` is `error` which returns an error if an extra parameter is indicated in the payload. Alternatively, you can set `extra-parameters: ignore` to drop any unknown parameter in the request. Use this capability in case you happen to be sending requests with extra parameters that you know the model won't support but you want the request to completes anyway. A typical example of this is indicating `seed` parameter.
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The Azure AI Model Inference API is currently supported in certain models deployed as [Serverless API endpoints](../how-to/deploy-models-serverless.md) and Managed Online Endpoints. Deploy any of the [supported models](#availability) and use the exact same code to consume their predictions.
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# [Python](#tab/python)
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The client library `azure-ai-inference` does inference, including chat completions, for AI models deployed by Azure AI Studio and Azure Machine Learning Studio. It supports Serverless API endpoints and Managed Compute endpoints (formerly known as Managed Online Endpoints).
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Explore our [samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/ai/azure-ai-inference/samples) and read the [API reference documentation](https://aka.ms/azsdk/azure-ai-inference/python/reference) to get yourself started.
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# [JavaScript](#tab/javascript)
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The client library `@azure-rest/ai-inference` does inference, including chat completions, for AI models deployed by Azure AI Studio and Azure Machine Learning Studio. It supports Serverless API endpoints and Managed Compute endpoints (formerly known as Managed Online Endpoints).
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Explore our [samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-inference-rest/samples) and read the [API reference documentation](https://aka.ms/AAp1kxa) to get yourself started.
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# [REST](#tab/rest)
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Explore the reference section of the Azure AI model inference API to see parameters and options to consume models, including chat completions models, deployed by Azure AI Studio and Azure Machine Learning Studio. It supports Serverless API endpoints and Managed Compute endpoints (formerly known as Managed Online Endpoints).
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* [Get info](reference-model-inference-info.md): Returns the information about the model deployed under the endpoint.
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* [Text embeddings](reference-model-inference-embeddings.md): Creates an embedding vector representing the input text.
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* [Text completions](reference-model-inference-completions.md): Creates a completion for the provided prompt and parameters.
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* [Chat completions](reference-model-inference-chat-completions.md): Creates a model response for the given chat conversation.
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* [Image embeddings](reference-model-inference-images-embeddings.md): Creates an embedding vector representing the input text and image.
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---

‎articles/ai-studio/reference/reference-model-inference-chat-completions.md

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| Name | Required | Type | Description |
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| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `allow` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `drop` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
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| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `pass-through` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `ignore` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
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| azureml-model-deployment | | string | Name of the deployment you want to route the request to. Supported for endpoints that support multiple deployments. |
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## Request Body

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