[migrate-content] Fix links

Author: Learn-Build-Service

.openpublishing.redirection.json

@@ -45,11 +45,6 @@
       "redirect_url": "/previous-versions/azure/public-multi-access-edge-compute-mec/tutorial-create-vm-using-python-sdk",
       "redirect_document_id": false
     },
-    {
-      "source_path": "articles/ai-studio/how-to/commitment-tier.md",
-      "redirect_url": "/azure/ai-services/commitment-tier.md",
-      "redirect_document_id": false
-    },
     {
       "source_path": "articles/storage/files/files-samples-dotnet-v11.md",
       "redirect_url": "/previous-versions/azure/storage/files/files-samples-dotnet-v11",

articles/ai-services/.openpublishing.redirection.ai-services.json

@@ -79,13 +79,13 @@ In this example, the named value in API Management is *openai-api-key*.
 ## Authenticate with managed identity
 
 An alternative way to authenticate to an Azure OpenAI API by using a managed identity in Microsoft Entra ID. For background, see
-[How to configure Azure OpenAI Service with managed identity](../ai-services/openai/how-to/managed-identity.md).
+[How to configure Azure OpenAI Service with managed identity](/azure/ai-services/openai/how-to/managed-identity).
 
 Following are steps to configure your API Management instance to use a managed identity to authenticate requests to an Azure OpenAI API. 
 
 1. [Enable](api-management-howto-use-managed-service-identity.md) a system-assigned or user-assigned managed identity for your API Management instance. The following example assumes that you've enabled the instance's system-assigned managed identity.
 
-1. Assign the managed identity the **Cognitive Services OpenAI User** role, scoped to the appropriate resource. For example, assign the system-assigned managed identity the **Cognitive Services OpenAI User** role on the Azure OpenAI resource. For detailed steps, see [Role-based access control for Azure OpenAI service](../ai-services/openai/how-to/role-based-access-control.md).
+1. Assign the managed identity the **Cognitive Services OpenAI User** role, scoped to the appropriate resource. For example, assign the system-assigned managed identity the **Cognitive Services OpenAI User** role on the Azure OpenAI resource. For detailed steps, see [Role-based access control for Azure OpenAI service](/azure/ai-services/openai/how-to/role-based-access-control).
 
 1. Add the following policy snippet in the `inbound` policy section to authenticate requests to the Azure OpenAI API using the managed identity. 
 
@@ -160,5 +160,5 @@ Following are high level steps to restrict API access to users or apps that are
 ## Related content
 
 * Learn more about [Microsoft Entra ID and OAuth2.0](../active-directory/develop/authentication-vs-authorization.md).  
-* [Authenticate requests to Azure AI services](../ai-services/authentication.md)
+* [Authenticate requests to Azure AI services](/azure/ai-services/authentication)
 * [Protect Azure OpenAI keys with API Management](/semantic-kernel/deploy/use-ai-apis-with-api-management?toc=%2Fazure%2Fapi-management%2Ftoc.json&bc=/azure/api-management/breadcrumb/toc.json)

articles/api-management/api-management-authenticate-authorize-azure-openai.md

@@ -22,7 +22,7 @@ This article shows two options to import an [Azure OpenAI Service](/azure/ai-ser
 ## Prerequisites
 
 - An existing API Management instance. [Create one if you haven't already](get-started-create-service-instance.md).
-- An Azure OpenAI resource with a model deployed. For more information about model deployment, see the [resource deployment guide](../ai-services/openai/how-to/create-resource.md).
+- An Azure OpenAI resource with a model deployed. For more information about model deployment, see the [resource deployment guide](/azure/ai-services/openai/how-to/create-resource).
 
     Make a note of the ID (name) of the deployment. You'll need it when you test the imported API in API Management.
 - Permissions to grant access to the Azure OpenAI resource from the API Management instance.

articles/api-management/azure-openai-api-from-specification.md

@@ -18,7 +18,7 @@ ms.collection: ce-skilling-ai-copilot
 Enable semantic caching of responses to Azure OpenAI API requests to reduce bandwidth and processing requirements imposed on the backend APIs and lower latency perceived by API consumers. With semantic caching, you can return cached responses for identical prompts and also for prompts that are similar in meaning, even if the text isn't the same. For background, see [Tutorial: Use Azure Cache for Redis as a semantic cache](../azure-cache-for-redis/cache-tutorial-semantic-cache.md).
 
 > [!NOTE]
-> The configuration steps in this article enable semantic caching for Azure OpenAI APIs. These steps can be generalized to enable semantic caching for corresponding large language model (LLM) APIs available through the [Azure AI Model Inference API](../ai-studio/reference/reference-model-inference-api.md). 
+> The configuration steps in this article enable semantic caching for Azure OpenAI APIs. These steps can be generalized to enable semantic caching for corresponding large language model (LLM) APIs available through the [Azure AI Model Inference API](/azure/ai-studio/reference/reference-model-inference-api). 
 
 ## Prerequisites
 

articles/api-management/azure-openai-enable-semantic-caching.md

@@ -15,17 +15,17 @@ ms.author: danlep
 
 [!INCLUDE [api-management-availability-all-tiers](../../includes/api-management-availability-all-tiers.md)]
 
-This article introduces capabilities in Azure API Management to help you manage generative AI APIs, such as those provided by [Azure OpenAI Service](../ai-services/openai/overview.md). Azure API Management provides a range of policies, metrics, and other features to enhance security, performance, and reliability for the APIs serving your intelligent apps. Collectively, these features are called *generative AI (GenAI) gateway capabilities* for your generative AI APIs.
+This article introduces capabilities in Azure API Management to help you manage generative AI APIs, such as those provided by [Azure OpenAI Service](/azure/ai-services/openai/overview). Azure API Management provides a range of policies, metrics, and other features to enhance security, performance, and reliability for the APIs serving your intelligent apps. Collectively, these features are called *generative AI (GenAI) gateway capabilities* for your generative AI APIs.
 
 > [!NOTE]
-> * This article focuses on capabilities to manage APIs exposed by Azure OpenAI Service. Many of the GenAI gateway capabilities apply to other large language model (LLM) APIs, including those available through [Azure AI Model Inference API](../ai-studio/reference/reference-model-inference-api.md).
+> * This article focuses on capabilities to manage APIs exposed by Azure OpenAI Service. Many of the GenAI gateway capabilities apply to other large language model (LLM) APIs, including those available through [Azure AI Model Inference API](/azure/ai-studio/reference/reference-model-inference-api).
 > * Generative AI gateway capabilities are features of API Management's existing API gateway, not a separate API gateway. For more information on API Management, see [Azure API Management overview](api-management-key-concepts.md).
 
 ## Challenges in managing generative AI APIs
 
 One of the main resources you have in generative AI services is *tokens*. Azure OpenAI Service assigns quota for your model deployments expressed in tokens-per-minute (TPM) which is then distributed across your model consumers - for example, different applications, developer teams, departments within the company, etc.
 
-Azure makes it easy to connect a single app to Azure OpenAI Service: you can connect directly using an API key with a TPM limit configured directly on the model deployment level. However, when you start growing your application portfolio, you're presented with multiple apps calling single or even multiple Azure OpenAI Service endpoints deployed as pay-as-you-go or [Provisioned Throughput Units](../ai-services/openai/concepts/provisioned-throughput.md) (PTU) instances. That comes with certain challenges: 
+Azure makes it easy to connect a single app to Azure OpenAI Service: you can connect directly using an API key with a TPM limit configured directly on the model deployment level. However, when you start growing your application portfolio, you're presented with multiple apps calling single or even multiple Azure OpenAI Service endpoints deployed as pay-as-you-go or [Provisioned Throughput Units](/azure/ai-services/openai/concepts/provisioned-throughput) (PTU) instances. That comes with certain challenges: 
 
 * How is token usage tracked across multiple applications? Can cross-charges be calculated for multiple applications/teams that use Azure OpenAI Service models? 
 * How do you ensure that a single app doesn't consume the whole TPM quota, leaving other apps with no option to use Azure OpenAI Service models? 

articles/api-management/genai-gateway-capabilities.md

@@ -12,7 +12,7 @@ You can use Azure App Service to work with popular AI frameworks like LangChain
 
 #### Prerequisites
 
-- An [Azure OpenAI resource](../../../ai-services/openai/quickstart.md?pivots=programming-language-csharp&tabs=command-line%2Cpython#set-up) or an [OpenAI account](https://platform.openai.com/overview).
+- An [Azure OpenAI resource](/azure/ai-services/openai/quickstart?pivots=programming-language-csharp&tabs=command-line%2Cpython#set-up) or an [OpenAI account](https://platform.openai.com/overview).
 - A .NET 8 Blazor Web App.  Create the application with a template [here](https://dotnet.microsoft.com/learn/aspnet/blazor-tutorial/intro).
 
 ### Setup Blazor web app
@@ -64,7 +64,7 @@ After the Navigation is updated, we can start preparing to build the OpenAI clie
 
 In order to make calls to OpenAI with your client, you need to first grab the Keys and Endpoint values from Azure OpenAI, or OpenAI and add them as secrets for use in your application. Retrieve and save the values for later use.
 
-For Azure OpenAI, see [this documentation](../../../ai-services/openai/quickstart.md?pivots=programming-language-csharp&tabs=command-line%2Cpython#retrieve-key-and-endpoint) to retrieve the key and endpoint values. If you're planning to use [managed identity](../../overview-managed-identity.md) to secure your app you'll only need the `deploymentName` and `endpoint` values.  Otherwise, you need each of the following:
+For Azure OpenAI, see [this documentation](/azure/ai-services/openai/quickstart?pivots=programming-language-csharp&tabs=command-line%2Cpython#retrieve-key-and-endpoint) to retrieve the key and endpoint values. If you're planning to use [managed identity](../../overview-managed-identity.md) to secure your app you'll only need the `deploymentName` and `endpoint` values.  Otherwise, you need each of the following:
 
 - `deploymentName`
 - `endpoint`

articles/app-service/includes/deploy-intelligent-apps/deploy-intelligent-apps-linux-dotnet-pivot.md

@@ -12,7 +12,7 @@ You can use Azure App Service to create applications using Azure OpenAI and Open
 
 #### Prerequisites
 
-- An [Azure OpenAI resource](../../../ai-services/openai/quickstart.md?pivots=programming-language-csharp&tabs=command-line%2Cpython#set-up) or an [OpenAI account](https://platform.openai.com/overview).
+- An [Azure OpenAI resource](/azure/ai-services/openai/quickstart?pivots=programming-language-csharp&tabs=command-line%2Cpython#set-up) or an [OpenAI account](https://platform.openai.com/overview).
 - A Java spring boot application. Create the application using this [quickstart](../../quickstart-java.md?tabs=springboot&pivots=java-maven-javase).
 
 ### Set up web app
@@ -33,7 +33,7 @@ For this Spring Boot application, we're building off the [quickstart](../../quic
 
 First, you need to grab the keys and endpoint values from Azure OpenAI, or OpenAI and add them as secrets for use in your application. Retrieve and save the values for later use to build the client.
 
-For Azure OpenAI, see [this documentation](../../../ai-services/openai/quickstart.md?pivots=programming-language-csharp&tabs=command-line%2Cpython#retrieve-key-and-endpoint) to retrieve the key and endpoint values. If you're planning to use [managed identity](../../overview-managed-identity.md) to secure your app you'll only need the `endpoint` value. Otherwise, you need each of the following:
+For Azure OpenAI, see [this documentation](/azure/ai-services/openai/quickstart?pivots=programming-language-csharp&tabs=command-line%2Cpython#retrieve-key-and-endpoint) to retrieve the key and endpoint values. If you're planning to use [managed identity](../../overview-managed-identity.md) to secure your app you'll only need the `endpoint` value. Otherwise, you need each of the following:
 
 - `endpoint`
 - `apiKey`

articles/app-service/includes/deploy-intelligent-apps/deploy-intelligent-apps-linux-java-pivot.md

@@ -12,7 +12,7 @@ You can use Azure App Service to work with popular AI frameworks like LangChain
 
 #### Prerequisites
 
-- An [Azure OpenAI resource](../../../ai-services/openai/quickstart.md?pivots=programming-language-csharp&tabs=command-line%2Cpython#set-up) or an [OpenAI account](https://platform.openai.com/overview).
+- An [Azure OpenAI resource](/azure/ai-services/openai/quickstart?pivots=programming-language-csharp&tabs=command-line%2Cpython#set-up) or an [OpenAI account](https://platform.openai.com/overview).
 - A Flask web application. Create the sample app using our [quickstart](../../quickstart-python.md?tabs=flask%2Cwindows%2Cazure-cli%2Cvscode-deploy%2Cdeploy-instructions-azportal%2Cterminal-bash%2Cdeploy-instructions-zip-azcli#1---sample-application).
 
 ### Setup flask web app
@@ -86,7 +86,7 @@ After the files are updated, we can start preparing our environment variables to
 
 In order to make calls to OpenAI with your client, you need to first grab the Keys and Endpoint values from Azure OpenAI, or OpenAI and add them as secrets for use in your application. Retrieve and save the values for later use.
 
-For Azure OpenAI, see [this documentation](../../../ai-services/openai/quickstart.md?pivots=programming-language-csharp&tabs=command-line%2Cpython#retrieve-key-and-endpoint) to retrieve the key and endpoint values. If you're planning to use [managed identity](../../overview-managed-identity.md) to secure your app you'll only need the `api_version` and `azure__endpoint` values.  Otherwise, you need each of the following:
+For Azure OpenAI, see [this documentation](/azure/ai-services/openai/quickstart?pivots=programming-language-csharp&tabs=command-line%2Cpython#retrieve-key-and-endpoint) to retrieve the key and endpoint values. If you're planning to use [managed identity](../../overview-managed-identity.md) to secure your app you'll only need the `api_version` and `azure__endpoint` values.  Otherwise, you need each of the following:
 
 - `api_key`
 - `api_version`

articles/app-service/includes/deploy-intelligent-apps/deploy-intelligent-apps-linux-python-pivot.md

@@ -11,7 +11,7 @@ ms.custom: devx-track-azurecli
 * [Sample application](https://github.com/Azure-Samples/app-service-language-detector)
 
 > [!TIP]
-> Azure AI services do [support authentication through managed identities](../../../ai-services/authentication.md#authorize-access-to-managed-identities), but this tutorial uses the [subscription key authentication](../../../ai-services/authentication.md#authenticate-with-a-single-service-resource-key) to demonstrate how you could connect to an Azure service that doesn't support managed identities from App Services.
+> Azure AI services do [support authentication through managed identities](/azure/ai-services/authentication#authorize-access-to-managed-identities), but this tutorial uses the [subscription key authentication](/azure/ai-services/authentication#authenticate-with-a-single-service-resource-key) to demonstrate how you could connect to an Azure service that doesn't support managed identities from App Services.
 
 ![Architecture diagram for tutorial scenario.](../../media/tutorial-connect-msi-key-vault/architecture.png)