Merge pull request #285171 from MicrosoftDocs/main

8/21/2024 AM Publish

Author: Taojunshen

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

@@ -117,7 +117,7 @@
           },
           {
             "source_path_from_root": "/articles/ai-services/openai/how-to/monitoring.md",
-            "redirect_url": "/azure/ai-services/openai/monitor-openai",
+            "redirect_url": "/azure/ai-services/openai/how-to/monitor-openai",
             "redirect_document_id": false
           },
           {

articles/ai-services/openai/concepts/model-retirements.md

@@ -4,7 +4,7 @@ titleSuffix: Azure OpenAI
 description: Learn about the model deprecations and retirements in Azure OpenAI.
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 08/14/2024
+ms.date: 08/21/2024
 ms.custom: 
 manager: nitinme
 author: mrbullwinkle
@@ -108,6 +108,8 @@ These models are currently available for use in Azure OpenAI Service.
 
  **<sup>1</sup>** We will notify all customers with these preview deployments at least 30 days before the start of the upgrades. We will publish an upgrade schedule detailing the order of regions and model versions that we will follow during the upgrades, and link to that schedule from here.
 
+> [!IMPORTANT]
+> Vision enhancements preview features including Optical Character Recognition (OCR), object grounding, video prompts will be retired and no longer available once `gpt-4` Version: `vision-preview` is upgraded to `turbo-2024-04-09`. If you are currently relying on any of these preview features, this automatic model upgrade will be a breaking change.
 
 ## Deprecated models
 
@@ -156,7 +158,7 @@ If you're an existing customer looking for information about these models, see [
 * Updated `gpt-4` preview model upgrade date to November 15, 2024 or later for the following versions:
   * 1106-preview
   * 0125-preview
-  * vision-preview
+  * vision-preview (Vision enhancements feature will no longer be supported once this model is retired/upgraded.)
 
 ### July 18, 2024
 

articles/ai-services/openai/how-to/monitor-openai.md

@@ -19,7 +19,7 @@ Azure OpenAI provides out-of-box dashboards for each of your Azure OpenAI resour
 
 :::image type="content" source="../media/monitoring/dashboard.png" alt-text="Screenshot that shows out-of-box dashboards for an Azure OpenAI resource in the Azure portal." lightbox="../media/monitoring/dashboard.png" border="false":::
 
-The dashboards are grouped into four categories: **HTTP Requests**, **Tokens-Based Usage**, **PTU Utilization**, and **Fine-tuning**
+The dashboards are grouped into four categories: **HTTP Requests**, **Tokens-Based Usage**, **PTU Utilization**, and **Fine-tuning**.
 
 ## Data collection and routing in Azure Monitor
 

articles/ai-services/openai/quotas-limits.md

@@ -10,7 +10,7 @@ ms.custom:
   - ignite-2023
   - references_regions
 ms.topic: conceptual
-ms.date: 08/16/2024
+ms.date: 08/21/2024
 ms.author: mbullwin
 ---
 
@@ -93,7 +93,7 @@ Global Standard deployments use Azure's global infrastructure, dynamically routi
 The Usage Limit determines the level of usage above which customers might see larger variability in response latency. A customer’s usage is defined per model and is the total tokens consumed across all deployments in all subscriptions in all regions for a given tenant.
 
 > [!NOTE]
-> Usage tiers only apply to standard and global standard deployment types. Usage tiers do not apply to global batch deployments.
+> Usage tiers only apply to standard and global standard deployment types. Usage tiers do not apply to global batch and provisioned throughput deployments.
 
 #### GPT-4o global standard & standard
 

articles/ai-services/personalizer/what-is-personalizer.md

@@ -1,8 +1,8 @@
 ---
 title: What is Personalizer?
 description: Personalizer is a cloud-based service that allows you to choose the best experience to show to your users, learning from their real-time behavior.
-author: jcodella
-ms.author: jacodel
+author: tyclintw
+ms.author: tyclintw
 ms.manager: nitinme
 ms.service: azure-ai-personalizer
 ms.topic: overview

articles/ai-studio/tutorials/copilot-sdk-build-rag.md

@@ -42,7 +42,7 @@ In this part one, you learn how to:
     > [!IMPORTANT]
     > This tutorial builds on the code and environment you set up in the quickstart.
 
-- You need a local copy of product data. The [Azure-Samples/rag-data-openai-python-promptflow repository on GitHub](https://github.com/Azure-Samples/rag-data-openai-python-promptflow/) contains sample retail product information that's relevant for this tutorial scenario. [Download the example Contoso Trek retail product data in a ZIP file](https://github.com/Azure-Samples/rag-data-openai-python-promptflow/raw/main/tutorial/data.zip) to your local machine.
+- You need a local copy of product data. The [Azure-Samples/rag-data-openai-python-promptflow repository on GitHub](https://github.com/Azure-Samples/rag-data-openai-python-promptflow/) contains sample retail product information that's relevant for this tutorial scenario. [Download the example Contoso Trek retail product data in a ZIP file](https://github.com/Azure-Samples/rag-data-openai-python-promptflow/tree/main/tutorial/data) to your local machine.
 
 ## Application code structure
 
@@ -224,7 +224,7 @@ You need to set environment variables for the Azure AI Search service and connec
 
 If you don't have an Azure AI Search index already created, we walk through how to create one. If you already have an index to use, you can skip to the [set the search environment variables](#set-search-environment-variables) section. The search index is created on the Azure AI Search service that was either created or referenced in the previous step.
 
-1. Use your own data or [download the example Contoso Trek retail product data in a ZIP file](https://github.com/Azure-Samples/rag-data-openai-python-promptflow/raw/main/tutorial/data.zip) to your local machine. Unzip the file into your **rag-tutorial** folder. This data is a collection of markdown files that represent product information. The data is structured in a way that is easy to ingest into a search index. You build a search index from this data.
+1. Use your own data or [download the example Contoso Trek retail product data in a ZIP file](https://github.com/Azure-Samples/rag-data-openai-python-promptflow/tree/main/tutorial/data) to your local machine. Unzip the file into your **rag-tutorial** folder. This data is a collection of markdown files that represent product information. The data is structured in a way that is easy to ingest into a search index. You build a search index from this data.
 
 1. The prompt flow RAG package allows you to ingest the markdown files, locally create a search index, and register it in the cloud project. Install the prompt flow RAG package:
 

articles/api-management/breaking-changes/stv1-platform-retirement-august-2024.md

@@ -1,19 +1,22 @@
 ---
-title: Azure API Management - stv1 platform retirement (August 2024) | Microsoft Docs
-description: Azure API Management will retire the stv1 compute platform effective 31 August 2024. Instances hosted on the stv1 platform must be migrated to the stv2 platform.
+title: Azure API Management - global Azure - stv1 platform retirement (August 2024)
+description: In the global Azure cloud, Azure API Management will retire stv1 compute platform effective 31 August 2024. Instances must be migrated to stv2 platform.
 services: api-management
 author: dlepow
 ms.service: azure-api-management
 ms.topic: reference
-ms.date: 12/19/2023
+ms.date: 08/08/2024
 ms.author: danlep
 ---
 
-# stv1 platform retirement (August 2024)
+# API Management stv1 platform retirement - Global Azure cloud (August 2024)
 
 [!INCLUDE [api-management-availability-premium-dev-standard-basic](../../../includes/api-management-availability-premium-dev-standard-basic.md)]
 
-As a cloud platform-as-a-service (PaaS), Azure API Management abstracts many details of the infrastructure used to host and run your service. **The infrastructure associated with the API Management `stv1` compute platform version will be retired effective 31 August 2024.** A more current compute platform version (`stv2`) is already available, and provides enhanced service capabilities. 
+As a cloud platform-as-a-service (PaaS), Azure API Management abstracts many details of the infrastructure used to host and run your service. **The infrastructure associated with the API Management `stv1` compute platform version will be retired effective 31 August 2024 in the global Microsoft Azure cloud.** A more current compute platform version (`stv2`) is already available, and provides enhanced service capabilities.
+
+> [!NOTE]
+> For API Management instances deployed in Microsoft Azure Government cloud or Microsoft Azure operated by 21Vianet cloud (Azure in China), the retirement date for the `stv1` platform is 28 February 2025. [Learn more](stv1-platform-retirement-sovereign-clouds-february-2025.md)
 
 The following table summarizes the compute platforms currently used for instances in the different API Management service tiers. 
 
@@ -38,7 +41,6 @@ Support for API Management instances hosted on the `stv1` platform will be retir
 > [!WARNING]
 > If your instance is currently hosted on the `stv1` platform, you must migrate to the `stv2` platform. Failure to migrate by the retirement date might result in loss of the environments running APIs and all configuration data.
 
-
 ## What do I need to do?
 
 **Migrate all your existing instances hosted on the `stv1` compute platform to the `stv2` compute platform by 31 August 2024.**  

articles/api-management/breaking-changes/stv1-platform-retirement-sovereign-clouds-february-2025.md

@@ -0,0 +1,53 @@
+---
+title: Azure API Management - stv1 platform retirement - Azure Government, Azure in China (February 2025)
+description: In Azure Government and Azure operated by 21Vianet, API Management will retire stv1 platform effective 28 February 2025. Instances must be migrated to stv2 platform.
+services: api-management
+author: dlepow
+ms.service: azure-api-management
+ms.topic: reference
+ms.date: 08/09/2024
+ms.author: danlep
+---
+
+# API Management stv1 platform retirement - Azure Government and Azure operated by 21Vianet  (February 2025)
+
+[!INCLUDE [api-management-availability-premium-dev-standard-basic](../../../includes/api-management-availability-premium-dev-standard-basic.md)]
+
+As a cloud platform-as-a-service (PaaS), Azure API Management abstracts many details of the infrastructure used to host and run your service. **The infrastructure associated with the API Management `stv1` compute platform version will be retired effective 28 February 2025 in Microsoft Azure Government and in Microsoft Azure operated by 21 Vianet (Azure in China).** A more current compute platform version (`stv2`) is already available, and provides enhanced service capabilities.
+
+> [!NOTE]
+> For API Management instances deployed in global Microsoft Azure, the retirement date for the `stv1` platform is 31 August 2024. [Learn more](stv1-platform-retirement-august-2024.md)
+
+The following table summarizes the compute platforms currently used for instances in the different API Management service tiers. 
+
+| Version | Description | Architecture | Tiers |
+| -------| ----------| ----------- | ---- |
+| `stv2` | Single-tenant v2 | Azure-allocated compute infrastructure that supports availability zones, private endpoints | Developer, Basic, Standard, Premium |
+| `stv1` |  Single-tenant v1 | Azure-allocated compute infrastructure |  Developer, Basic, Standard, Premium | 
+| `mtv1` | Multi-tenant v1 |  Shared infrastructure that supports native autoscaling and scaling down to zero in times of no traffic |  Consumption |
+
+**For continued support and to take advantage of upcoming features, customers must [migrate](../migrate-stv1-to-stv2.md) their Azure API Management instances from the `stv1` compute platform to the `stv2` compute platform.** The `stv2` compute platform comes with additional features and improvements such as support for Azure Private Link and other networking features. 
+
+New instances created in service tiers other than the Consumption tier are mostly hosted on the `stv2` platform already. Existing instances on the `stv1` compute platform will continue to work normally until the retirement date, but those instances won’t receive the latest features available to the `stv2` platform.   
+
+## Is my service affected by this?
+
+If the value of the `platformVersion` property of your service is `stv1`, it's hosted on the `stv1` platform. See [How do I know which platform hosts my API Management instance?](../compute-infrastructure.md#how-do-i-know-which-platform-hosts-my-api-management-instance)
+
+## What is the deadline for the change?
+
+In Azure Government and Azure operated by 21Vianet, support for API Management instances hosted on the `stv1` platform will be retired by 28 February 2025.
+
+## What do I need to do?
+
+**Migrate all your existing instances hosted on the `stv1` compute platform to the `stv2` compute platform by 28 February 2025.**  
+
+If you have existing instances hosted on the `stv1` platform, follow our **[migration guide](../migrate-stv1-to-stv2.md)** to ensure a successful migration. 
+
+[!INCLUDE [api-management-migration-support](../../../includes/api-management-migration-support.md)]
+
+
+## Related content
+
+* [Migrate from stv1 platform to stv2](../migrate-stv1-to-stv2.md)
+* See all [upcoming breaking changes and feature retirements](overview.md).

articles/api-management/llm-semantic-cache-lookup-policy.md

@@ -71,7 +71,7 @@ Use the `llm-semantic-cache-lookup` policy to perform cache lookup of responses
 
 ### Example with corresponding llm-semantic-cache-store policy
 
-[!INCLUDE [api-management-semantic-cache-example](../../includes/api-management-semantic-cache-example.md)]
+[!INCLUDE [api-management-llm-semantic-cache-example](../../includes/api-management-llm-semantic-cache-example.md)]
 
 ## Related policies
 

articles/api-management/llm-semantic-cache-store-policy.md

@@ -54,7 +54,7 @@ The `llm-semantic-cache-store` policy caches responses to chat completion API an
 
 ### Example with corresponding llm-semantic-cache-lookup policy
 
-[!INCLUDE [api-management-semantic-cache-example](../../includes/api-management-semantic-cache-example.md)]
+[!INCLUDE [api-management-llm-semantic-cache-example](../../includes/api-management-llm-semantic-cache-example.md)]
 
 ## Related policies