openai folder moved to ai-foundry

Author: eric-urban

articles/ai-foundry/.openpublishing.redirection.ai-studio.json

@@ -467,7 +467,7 @@
           },
           {
             "source_path_from_root": "/articles/ai-foundry/foundry-models/supported-languages-openai.md",
-            "redirect_url": "/azure/ai-services/openai/supported-languages",
+            "redirect_url": "/azure/ai-foundry/openai/supported-languages",
             "redirect_document_id": false
           },
           {
@@ -945,7 +945,7 @@
         },
         {
             "source_path_from_root": "/articles/ai-studio/quickstarts/assistants.md",
-            "redirect_url": "/azure/ai-services/openai/assistants-quickstart",
+            "redirect_url": "/azure/ai-foundry/openai/assistants-quickstart",
             "redirect_document_id": true
         },
         {
@@ -1110,7 +1110,7 @@
           },
           {
             "source_path_from_root": "/articles/ai-studio/quickstarts/multimodal-vision.md",
-            "redirect_url": "/azure/ai-services/openai/gpt-v-quickstart",
+            "redirect_url": "/azure/ai-foundry/openai/gpt-v-quickstart",
             "redirect_document_id": false
           },
           {

articles/ai-foundry/concepts/ai-resources.md

@@ -90,6 +90,6 @@ If not provided by you, the following dependent resources are automatically crea
 ## Next steps
 
 - [Create a [!INCLUDE [hub-project-name](../includes/hub-project-name.md)]](../how-to/create-projects.md?pivots=hub-project)
-- [Quickstart: Analyze images and video in the chat playground](/azure/ai-services/openai/gpt-v-quickstart)
+- [Quickstart: Analyze images and video in the chat playground](/azure/ai-foundry/openai/gpt-v-quickstart)
 - [Learn more about Azure AI Foundry](../what-is-azure-ai-foundry.md)
 - [Learn more about projects](../how-to/create-projects.md?pivots=hub-project)

articles/ai-foundry/concepts/foundry-models-overview.md

@@ -236,7 +236,7 @@ To set the public network access flag for the Azure AI Foundry hub:
 
 * If you have an Azure AI Foundry hub with MaaS deployments created before July 11, 2024, and you enable a private endpoint on this hub, the existing serverless API deployments won't follow the hub's networking configuration. For serverless API deployments in the hub to follow the hub's networking configuration, you need to create the deployments again.
 
-* Currently, [Azure OpenAI On Your Data](/azure/ai-services/openai/concepts/use-your-data) support isn't available for serverless API deployments in private hubs, because private hubs have the public network access flag disabled.
+* Currently, [Azure OpenAI On Your Data](/azure/ai-foundry/openai/concepts/use-your-data) support isn't available for serverless API deployments in private hubs, because private hubs have the public network access flag disabled.
 
 * Any network configuration change (for example, enabling or disabling the public network access flag) might take up to five minutes to propagate.
 

articles/ai-foundry/concepts/model-benchmarks.md

@@ -107,7 +107,7 @@ Performance metrics are calculated as an aggregate over 14 days, based on 24 tra
 
 | Parameter                             | Value                                                                                              | Applicable For                                                                                                                                                                                                                              |
 |---------------------------------------|----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Region                                | East US/East US2                                                                                   | [serverless API deployments](../how-to/model-catalog-overview.md#serverless-api-deployment-pay-per-token-offer-billing) and [Azure OpenAI](/azure/ai-services/openai/overview)                                                                                          |
+| Region                                | East US/East US2                                                                                   | [serverless API deployments](../how-to/model-catalog-overview.md#serverless-api-deployment-pay-per-token-offer-billing) and [Azure OpenAI](/azure/ai-foundry/openai/overview)                                                                                          |
 | Tokens per minute (TPM) rate limit    | 30k (180 RPM based on Azure OpenAI) for non-reasoning and 100k for reasoning models <br> N/A (serverless API deployments) | For Azure OpenAI models, selection is available for users with rate limit ranges based on deployment type (serverless API, global, global standard, and so on.) <br> For serverless API deployments, this setting is abstracted.                             |
 | Number of requests                    | Two requests in a trail for every hour (24 trails per day)                                         | serverless API deployments, Azure OpenAI                                                                                                                                                                                                               |
 | Number of trails/runs                 | 14 days with 24 trails per day for 336 runs                                                        | serverless API deployments, Azure OpenAI                                                                                                                                                                                                               |

articles/ai-foundry/concepts/rbac-azure-ai-foundry.md

@@ -590,7 +590,7 @@ When using Microsoft Entra ID authenticated connections in the chat playground,
 
 ## Scenario: Use an existing Azure OpenAI resource
 
-When you create a connection to an existing Azure OpenAI resource, you must also assign roles to your users so they can access the resource. You should assign either the **Cognitive Services OpenAI User** or **Cognitive Services OpenAI Contributor** role, depending on the tasks they need to perform. For information on these roles and the tasks they enable, see [Azure OpenAI roles](/azure/ai-services/openai/how-to/role-based-access-control#azure-openai-roles).
+When you create a connection to an existing Azure OpenAI resource, you must also assign roles to your users so they can access the resource. You should assign either the **Cognitive Services OpenAI User** or **Cognitive Services OpenAI Contributor** role, depending on the tasks they need to perform. For information on these roles and the tasks they enable, see [Azure OpenAI roles](/azure/ai-foundry/openai/how-to/role-based-access-control#azure-openai-roles).
 
 ## Scenario: Use Azure Container Registry
 
@@ -615,7 +615,7 @@ Azure Application Insights is an optional dependency for Azure AI Foundry hub. T
 
 ## Scenario: Provisioned throughput unit procurer
 
-The following example defines a custom role that can procure [provisioned throughput units (PTU)](/azure/ai-services/openai/concepts/provisioned-throughput).
+The following example defines a custom role that can procure [provisioned throughput units (PTU)](/azure/ai-foundry/openai/concepts/provisioned-throughput).
 
 ```json
 {
@@ -659,7 +659,7 @@ The following example defines a custom role that can procure [provisioned throug
 
 ## Scenario: Azure OpenAI Assistants API
 
-The following example defines a role for a developer using [Azure OpenAI Assistants](/azure/ai-services/openai/how-to/assistant).
+The following example defines a role for a developer using [Azure OpenAI Assistants](/azure/ai-foundry/openai/how-to/assistant).
 
 ```json
 {

articles/ai-foundry/faq.yml

@@ -14,7 +14,7 @@ metadata:
   author: sdgilley
 title: Azure AI Foundry frequently asked questions
 summary: |
-  FAQ for [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs). If you can't find answers to your questions in this document, and still need help check the [Azure AI services support options guide](../ai-services/cognitive-services-support-options.md?context=/azure/ai-services/openai/context/context). Azure OpenAI is part of Azure AI services.
+  FAQ for [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs). If you can't find answers to your questions in this document, and still need help check the [Azure AI services support options guide](../ai-services/cognitive-services-support-options.md?context=/azure/ai-foundry/openai/context/context). Azure OpenAI is part of Azure AI services.
 sections:
   - name: General questions
     questions:

articles/ai-foundry/foundry-models/concepts/deployment-types.md

@@ -78,7 +78,7 @@ Key use cases include:
 
 Data zone standard deployments are available in the same Azure AI Foundry resource as all other AI Foundry Models deployment types but allow you to leverage Azure global infrastructure to dynamically route traffic to the data center within the Microsoft defined data zone with the best availability for each request. Data zone standard provides higher default quotas than our Azure geography-based deployment types. 
 
-Customers with high consistent volume may experience greater latency variability. The threshold is set per model. See the [Quotas and limits](/azure/ai-services/openai/quotas-limits#usage-tiers) page to learn more. For workloads that require low latency variance at large volume, we recommend leveraging the provisioned deployment offerings. 
+Customers with high consistent volume may experience greater latency variability. The threshold is set per model. See the [Quotas and limits](/azure/ai-foundry/openai/quotas-limits#usage-tiers) page to learn more. For workloads that require low latency variance at large volume, we recommend leveraging the provisioned deployment offerings. 
 
 ## Data zone provisioned
 
@@ -110,7 +110,7 @@ Standard deployments are optimized for low to medium volume workloads with high
 
 **SKU name in code:** `ProvisionedManaged`
 
-Provisioned deployments allow you to specify the amount of throughput you require in a deployment. The service then allocates the necessary model processing capacity and ensures it's ready for you. Throughput is defined in terms of provisioned throughput units (PTU) which is a normalized way of representing the throughput for your deployment. Each model-version pair requires different amounts of PTU to deploy and provide different amounts of throughput per PTU. Learn more from our [Provisioned throughput concepts article](/azure/ai-services/openai/concepts/provisioned-throughput).
+Provisioned deployments allow you to specify the amount of throughput you require in a deployment. The service then allocates the necessary model processing capacity and ensures it's ready for you. Throughput is defined in terms of provisioned throughput units (PTU) which is a normalized way of representing the throughput for your deployment. Each model-version pair requires different amounts of PTU to deploy and provide different amounts of throughput per PTU. Learn more from our [Provisioned throughput concepts article](/azure/ai-foundry/openai/concepts/provisioned-throughput).
 
 
 ## Control deployment options

articles/ai-foundry/foundry-models/faq.yml

@@ -12,7 +12,7 @@ metadata:
   author: santiagxf
 title: Foundry Models frequently asked questions
 summary: |
-  If you can't find answers to your questions in this document, and still need help check the [Azure AI Foundry services (formerly known Azure AI Services) support options guide](../../ai-services/cognitive-services-support-options.md?context=/azure/ai-services/openai/context/context).
+  If you can't find answers to your questions in this document, and still need help check the [Azure AI Foundry services (formerly known Azure AI Services) support options guide](../../ai-services/cognitive-services-support-options.md?context=/azure/ai-foundry/openai/context/context).
 sections:
   - name: General
     questions:

articles/ai-foundry/foundry-models/how-to/quickstart-github-models.md

@@ -91,7 +91,7 @@ Azure AI Foundry Models supports additional features not available in GitHub Mod
 * Configure [content filtering](../../model-inference/how-to/configure-content-filters.md).
 * Configure rate limiting (for specific models).
 * Explore additional [deployment SKUs (for specific models)](../../model-inference/concepts/deployment-types.md).
-* Configure [private networking](../../../ai-services/cognitive-services-virtual-networks.md?context=/azure/ai-services/openai/context/context).
+* Configure [private networking](../../../ai-services/cognitive-services-virtual-networks.md?context=/azure/ai-foundry/openai/context/context).
 
 ## Got troubles?
 

articles/ai-foundry/how-to/connections-add.md

@@ -142,7 +142,7 @@ For more on how to set private endpoints to your connected resources, see the fo
 | Azure Storage         | [Use private endpoints](/azure/storage/common/storage-private-endpoints)                                       |
 | Azure Cosmos DB        | [Configure Azure Private Link for Azure Cosmos DB](/azure/cosmos-db/how-to-configure-private-endpoints?tabs=arm-bicep) |
 | Azure AI Search       | [Create a private endpoint for a secure connection](/azure/search/service-create-private-endpoint)             |
-| Azure OpenAI          | [Securing Azure OpenAI inside a virtual network with private endpoints](/azure/ai-services/openai/how-to/network) |
+| Azure OpenAI          | [Securing Azure OpenAI inside a virtual network with private endpoints](/azure/ai-foundry/openai/how-to/network) |
 | Application Insights  | [Use Azure Private Link to connect networks to Azure Monitor](/azure/azure-monitor/logs/private-link-security) |