update

Author: mumian

.openpublishing.redirection.json

@@ -1,5 +1,15 @@
 {
     "redirections": [
+    {
+      "source_path": "articles/network-watcher/network-watcher-security-group-view-powershell.md",
+      "redirect_url": "/previous-versions/azure/network-watcher/network-watcher-security-group-view-powershell",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/network-watcher/network-watcher-security-group-view-cli.md",
+      "redirect_url": "/previous-versions/azure/network-watcher/network-watcher-security-group-view-cli",
+      "redirect_document_id": false
+    },
     {
       "source_path": "articles/storage/blobs/blob-v11-samples-dotnet.md",
       "redirect_url": "/previous-versions/azure/storage/blobs/blob-v11-samples-dotnet",
@@ -4024,6 +4034,11 @@
             "source_path_from_root":"/articles/aks/generation-2-vm-windows.md",
             "redirect_url":"/azure/aks/generation-2-vm",
             "redirect_document_id":false
+        },
+        {
+            "source_path_from_root":"/articles/cosmos-db/high-availability.md",
+            "redirect_url":"/azure/reliability/reliability-cosmos-db-nosql.md",
+            "redirect_document_id":false
         }
     ]
 }

articles/active-directory-b2c/custom-policies-series-sign-up-or-sign-in-federation.md

@@ -225,7 +225,7 @@ Notice the claims transformations we defined in [step 3.2](#step-32---define-cla
 
 Just like in sign-in with a local account, you need to configure the [Microsoft Entra Technical Profiles](active-directory-technical-profile.md), which you use to connect to Microsoft Entra ID storage, to store or read a user social account. 
 
-1. In the `ContosoCustomPolicy.XML` file, locate the `AAD-UserRead` technical profile and then add a new technical profile by using the following code: 
+1. In the `ContosoCustomPolicy.XML` file, locate the `AAD-UserRead` technical profile and then add a new technical profile below it by using the following code: 
 
     ```xml
         <TechnicalProfile Id="AAD-UserWriteUsingAlternativeSecurityId">
@@ -517,6 +517,7 @@ Use the following steps to add a combined local and social account:
     ```xml
         <OutputClaim ClaimTypeReferenceId="authenticationSource" DefaultValue="localIdpAuthentication" AlwaysUseDefaultValue="true" />
     ```
+   Make sure you also add the `authenticationSource` claim in the output claims collection of the `UserSignInCollector` self-asserted technical profile.
 
 1. In the `UserJourneys` section, add a new user journey, `LocalAndSocialSignInAndSignUp` by using the following code: 
 

articles/ai-services/openai/concepts/models.md

@@ -14,11 +14,11 @@ recommendations: false
 
 # Azure OpenAI Service models
 
-Azure OpenAI Service is powered by a diverse set of models with different capabilities and price points. Model availability varies by region.  For GPT-3 and other models retiring in July 2024, see [Azure OpenAI Service legacy models](./legacy-models.md).
+Azure OpenAI Service is powered by a diverse set of models with different capabilities and price points. Model availability varies by region. For GPT-3 and other models retiring in July 2024, see [Azure OpenAI Service legacy models](./legacy-models.md).
 
 | Models | Description |
 |--|--|
-| [GPT-4 Turbo 🆕](#gpt-4-turbo) | The latest most capable Azure OpenAI models with multimodal versions which can accept both text and images as input. |
+| [GPT-4 Turbo **NEW**](#gpt-4-turbo) | The latest most capable Azure OpenAI models with multimodal versions, which can accept both text and images as input. |
 | [GPT-4](#gpt-4) | A set of models that improve on GPT-3.5 and can understand and generate natural language and code. |
 | [GPT-3.5](#gpt-35) | A set of models that improve on GPT-3 and can understand and generate natural language and code. |
 | [Embeddings](#embeddings-models) | A set of models that can convert text into numerical vector form to facilitate text similarity. |
@@ -48,29 +48,25 @@ You can see the token context length supported by each model in the [model summa
 
 See [model versions](../concepts/model-versions.md) to learn about how Azure OpenAI Service handles model version upgrades, and [working with models](../how-to/working-with-models.md) to learn how to view and configure the model version settings of your GPT-4 deployments.
 
-|  Model ID  | Max Request (tokens) | Training Data (up to)  |
-|  --- |  :--- | :---: |
-| `gpt-4` (0314) | 8,192 | Sep 2021         |
-| `gpt-4-32k`(0314)  | 32,768               | Sep 2021         |
-| `gpt-4` (0613)     | 8,192                | Sep 2021         |
-| `gpt-4-32k` (0613) | 32,768               | Sep 2021         |
-| `gpt-4` (1106-Preview)**<sup>1</sup>**<br>**GPT-4 Turbo Preview** | Input: 128,000  <br> Output: 4,096           | Apr 2023         |
-| `gpt-4` (0125-Preview)**<sup>1</sup>**<br>**GPT-4 Turbo Preview** | Input: 128,000  <br> Output: 4,096           | Dec 2023         |
-| `gpt-4` (vision-preview)**<sup>2</sup>**<br>**GPT-4 Turbo with Vision Preview**  | Input: 128,000  <br> Output: 4,096              | Apr 2023       |
-| `gpt-4` (turbo-2024-04-09) 🆕 <br>**GPT-4 Turbo with Vision GA** | Input: 128,000  <br> Output: 4,096  | Dec 2023 |
-
-**<sup>1</sup>** GPT-4 Turbo Preview = `gpt-4` (0125-Preview) or `gpt-4` (1106-Preview). To deploy this model, under **Deployments** select model **gpt-4**. Under version select (0125-Preview) or (1106-Preview).
-
-**<sup>2</sup>** GPT-4 Turbo with Vision Preview = `gpt-4` (vision-preview). To deploy this model, under **Deployments** select model **gpt-4**. For **Model version** select **vision-preview**.
+|  Model ID  | Description | Max Request (tokens) | Training Data (up to)  |
+|  --- |  :--- |:--- |:---: |
+| `gpt-4` (turbo-2024-04-09) <br>**GPT-4 Turbo with Vision** | **Latest GA model** <br> - Replacement for all GPT-4 preview models (`vision-preview`, `1106-Preview`, `0125-Preview`). <br> - [**Feature availability**](#gpt-4-turbo) is currently different depending on method of input, and deployment type. <br> - Does **not support** enhancements. | Input: 128,000  <br> Output: 4,096  | Dec 2023 |
+| `gpt-4` (0125-Preview)*<br>**GPT-4 Turbo Preview** | **Preview Model** <br> -Replaces 1106-Preview <br>- Better code generation performance <br> - Reduces cases where the model doesn't complete a task <br> - JSON Mode <br> - parallel function calling <br> - reproducible output (preview) | Input: 128,000  <br> Output: 4,096           | Dec 2023         |
+| `gpt-4` (vision-preview)<br>**GPT-4 Turbo with Vision Preview**  | **Preview model** <br> - Accepts text and image input. <br> - Supports enhancements <br> - JSON Mode <br> - parallel function calling <br> - reproducible output (preview) | Input: 128,000  <br> Output: 4,096              | Apr 2023       |
+| `gpt-4` (1106-Preview)<br>**GPT-4 Turbo Preview** | **Preview Model** <br> - JSON Mode <br> - parallel function calling <br> - reproducible output (preview) | Input: 128,000  <br> Output: 4,096 | Apr 2023         |
+| `gpt-4-32k` (0613) | **Older GA model** <br> - Basic function calling with tools | 32,768               | Sep 2021         |
+| `gpt-4` (0613)     | **Older GA model** <br> - Basic function calling with tools | 8,192                | Sep 2021         |
+| `gpt-4-32k`(0314)  | **Older GA model** <br> - Deprecated function calling | 32,768               | Sep 2021         |
+| `gpt-4` (0314) | **Older GA model** <br> - Deprecated function calling | 8,192 | Sep 2021         |
 
 > [!CAUTION]
-> We don't recommend using preview models in production. We will upgrade all deployments of preview models to future preview versions and a stable version. Models designated preview do not follow the standard Azure OpenAI model lifecycle.
+> We don't recommend using preview models in production. We will upgrade all deployments of preview models to either future preview versions or to the latest stable/GA version. Models designated preview do not follow the standard Azure OpenAI model lifecycle.
 
 > [!NOTE]
 > Version `0314` of `gpt-4` and `gpt-4-32k` will be retired no earlier than July 5, 2024.  Version `0613` of `gpt-4` and `gpt-4-32k` will be retired no earlier than September 30, 2024.  See [model updates](../how-to/working-with-models.md#model-updates) for model upgrade behavior.
 
 - GPT-4 version 0125-preview is an updated version of the GPT-4 Turbo preview previously released as version 1106-preview.  
-- GPT-4 version 0125-preview completes tasks such as code generation more completely compared to gpt-4-1106-preview.  Because of this, depending on the task, customers may find that GPT-4-0125-preview generates more output compared to the gpt-4-1106-preview.  We recommend customers compare the outputs of the new model.  GPT-4-0125-preview also addresses bugs in gpt-4-1106-preview with UTF-8 handling for non-English languages. GPT-4 version `turbo-2024-04-09` is the latest GA release and replaces `0125-Preview`, `1106-preview`, and `vision-preview`.
+- GPT-4 version 0125-preview completes tasks such as code generation more completely compared to gpt-4-1106-preview. Because of this, depending on the task, customers may find that GPT-4-0125-preview generates more output compared to the gpt-4-1106-preview.  We recommend customers compare the outputs of the new model.  GPT-4-0125-preview also addresses bugs in gpt-4-1106-preview with UTF-8 handling for non-English languages. GPT-4 version `turbo-2024-04-09` is the latest GA release and replaces `0125-Preview`, `1106-preview`, and `vision-preview`.
 
 > [!IMPORTANT]
 >

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

@@ -62,59 +62,15 @@ The [Integrated Vector Database in vCore-based Azure Cosmos DB for MongoDB](/azu
 
 For some data sources such as uploading files from your local machine (preview) or data contained in a blob storage account (preview), Azure AI Search is used. When you choose the following data sources, your data is ingested into an Azure AI Search index.
 
->[!TIP]
->If you use Azure Cosmos DB (except for its vCore-based API for MongoDB), you may be eligible for the [Azure AI Advantage offer](/azure/cosmos-db/ai-advantage), which provides the equivalent of up to $6,000 in Azure Cosmos DB throughput credits.
-
-|Data source  | Description  |
+|Data ingested through Azure AI Search  | Description  |
 |---------|---------|
 | [Azure AI Search](/azure/search/search-what-is-azure-search)  | Use an existing Azure AI Search index with Azure OpenAI On Your Data.      |
-| [Azure Cosmos DB](/azure/cosmos-db/introduction)  | Azure Cosmos DB's API for Postgres and vCore-based API for MongoDB offer natively integrated vector indexing; therefore, they don't require Azure AI Search. However, its other APIs do require Azure AI Search for vector indexing. Azure Cosmos DB for NoSQL's natively integrated vector database debuts in mid-2024.     |
 |Upload files (preview)      | Upload files from your local machine to be stored in an Azure Blob Storage database, and ingested into Azure AI Search.         |
 |URL/Web address (preview)        | Web content from the URLs is stored in Azure Blob Storage.         |
 |Azure Blob Storage (preview) | Upload files from Azure Blob Storage to be ingested into an Azure AI Search index.         |
 
 :::image type="content" source="../media/use-your-data/azure-databases-and-ai-search.png" lightbox="../media/use-your-data/azure-databases-and-ai-search.png" alt-text="Diagram of vector indexing services.":::
 
-# [Vector Database in Azure Cosmos DB for MongoDB](#tab/mongo-db)
-
-### Prerequisites
-* [vCore-based Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb/vcore/introduction) account
-* A deployed [embedding model](../concepts/understand-embeddings.md)
-
-### Limitations
-* Only vCore-based Azure Cosmos DB for MongoDB is supported.
-* The search type is limited to [Integrated Vector Database in Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb/vcore/vector-search) with an Azure OpenAI embedding model.
-* This implementation works best on unstructured and spatial data.
-
-  
-### Data preparation
-
-Use the script provided on [GitHub](https://github.com/microsoft/sample-app-aoai-chatGPT/tree/main/scripts#data-preparation) to prepare your data.
-
-<!--### Add your data source in Azure OpenAI Studio
-
-To add vCore-based Azure Cosmos DB for MongoDB as a data source, you'll need an existing Azure Cosmos DB for MongoDB index containing your data, and a deployed Azure OpenAI Ada embeddings model that will be used for vector search.
-
-1. In the [Azure OpenAI portal](https://oai.azure.com/portal) chat playground, select **Add your data**. In the panel that appears, select ** vCore-based Azure Cosmos DB for MongoDB** as the data source. 
-1. Select your Azure subscription and database account, then connect to your Azure Cosmos DB account by providing your Azure Cosmos DB account username and password.
-    
-    :::image type="content" source="../media/use-your-data/add-mongo-data-source.png" alt-text="A screenshot showing the screen for adding Mongo DB as a data source in Azure OpenAI Studio." lightbox="../media/use-your-data/add-mongo-data-source.png":::
-
-1. **Select Database**. In the dropdown menus, select the database name, database collection, and index name that you want to use as your data source. Select the embedding model deployment you would like to use for vector search on this data source, and acknowledge that you'll incur charges for using vector search. Then select **Next**.
-
-    :::image type="content" source="../media/use-your-data/select-mongo-database.png" alt-text="A screenshot showing the screen for adding Mongo DB settings in Azure OpenAI Studio." lightbox="../media/use-your-data/select-mongo-database.png":::
--->
-
-### Index field mapping
-
-When you add your vCore-based Azure Cosmos DB for MongoDB data source, you can specify data fields to properly map your data for retrieval.
-
-* Content data (required): One or more provided fields to be used to ground the model on your data. For multiple fields, separate the values with commas, with no spaces.
-* File name/title/URL: Used to display more information when a document is referenced in the chat.
-* Vector fields (required): Select the field in your database that contains the vectors.
-
-:::image type="content" source="../media/use-your-data/mongo-index-mapping.png" alt-text="A screenshot showing the index field mapping options for Mongo DB." lightbox="../media/use-your-data/mongo-index-mapping.png":::
-
 # [Azure AI Search](#tab/ai-search)
 
 You might want to consider using an Azure AI Search index when you either want to:
@@ -179,15 +135,41 @@ If you want to implement additional value-based criteria for query execution, yo
 
 [!INCLUDE [ai-search-ingestion](../includes/ai-search-ingestion.md)]
 
+
+# [Vector Database in Azure Cosmos DB for MongoDB](#tab/mongo-db)
+
+### Prerequisites
+* [vCore-based Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb/vcore/introduction) account
+* A deployed [embedding model](../concepts/understand-embeddings.md)
+
+### Limitations
+* Only vCore-based Azure Cosmos DB for MongoDB is supported.
+* The search type is limited to [Integrated Vector Database in Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb/vcore/vector-search) with an Azure OpenAI embedding model.
+* This implementation works best on unstructured and spatial data.
+
+  
+### Data preparation
+
+Use the script provided on [GitHub](https://github.com/microsoft/sample-app-aoai-chatGPT/tree/main/scripts#data-preparation) to prepare your data.
+
+### Index field mapping
+
+When you add your vCore-based Azure Cosmos DB for MongoDB data source, you can specify data fields to properly map your data for retrieval.
+
+* Content data (required): One or more provided fields to be used to ground the model on your data. For multiple fields, separate the values with commas, with no spaces.
+* File name/title/URL: Used to display more information when a document is referenced in the chat.
+* Vector fields (required): Select the field in your database that contains the vectors.
+
+:::image type="content" source="../media/use-your-data/mongo-index-mapping.png" alt-text="A screenshot showing the index field mapping options for Mongo DB." lightbox="../media/use-your-data/mongo-index-mapping.png":::
+
 # [Azure Blob Storage (preview)](#tab/blob-storage)
 
 You might want to use Azure Blob Storage as a data source if you want to connect to existing Azure Blob Storage and use files stored in your containers.
 
 ## Schedule automatic index refreshes
 
 > [!NOTE] 
-> * Automatic index refreshing is supported for Azure Blob Storage only.
-> * If a document is deleted from input blob container, the corresponding chunk index records won't be removed by the scheduled refresh.
+> Automatic index refreshing is supported for Azure Blob Storage only.
 
 To keep your Azure AI Search index up-to-date with your latest data, you can schedule an automatic index refresh rather than manually updating it every time your data is updated. Automatic index refresh is only available when you choose **Azure Blob Storage** as the data source. To enable an automatic index refresh:
 

articles/ai-services/openai/how-to/fine-tuning.md

@@ -7,10 +7,10 @@ manager: nitinme
 ms.service: azure-ai-openai
 ms.custom: build-2023, build-2023-dataai, devx-track-python
 ms.topic: how-to
-ms.date: 02/22/2024
+ms.date: 05/03/2024
 author: mrbullwinkle
 ms.author: mbullwin
-zone_pivot_groups: openai-fine-tuning
+zone_pivot_groups: openai-fine-tuning-new
 ---
 
 # Customize a model with fine-tuning
@@ -26,10 +26,15 @@ In contrast to few-shot learning, fine tuning improves the model by training on
 
 We use LoRA, or low rank approximation, to fine-tune models in a way that reduces their complexity without significantly affecting their performance. This method works by approximating the original high-rank matrix with a lower rank one, thus only fine-tuning a smaller subset of "important" parameters during the supervised training phase, making the model more manageable and efficient. For users, this makes training faster and more affordable than other techniques.
 
-
 ::: zone pivot="programming-language-studio"
 
-[!INCLUDE [Studio fine-tuning](../includes/fine-tuning-studio.md)]
+[!INCLUDE [Azure OpenAI Studio fine-tuning](../includes/fine-tuning-studio.md)]
+
+::: zone-end
+
+::: zone pivot="programming-language-ai-studio"
+
+[!INCLUDE [AI Studio fine-tuning](../includes/fine-tuning-openai-in-ai-studio.md)]
 
 ::: zone-end
 
@@ -65,8 +70,8 @@ If your file upload fails, you can view the error message under “data files”
 
 - **Bad data:** A poorly curated or unrepresentative dataset will produce a low-quality model. Your model may learn inaccurate or biased patterns from your dataset. For example, if you are training a chatbot for customer service, but only provide training data for one scenario (e.g. item returns) it will not know how to respond to other scenarios. Or, if your training data is bad (contains incorrect responses), your model will learn to provide incorrect results.
 
-
 ## Next steps
 
 - Explore the fine-tuning capabilities in the [Azure OpenAI fine-tuning tutorial](../tutorials/fine-tune.md).
 - Review fine-tuning [model regional availability](../concepts/models.md#fine-tuning-models)
+- Learn more about [Azure OpenAI quotas](../quotas-limits.md)

articles/ai-services/openai/includes/create-resource-portal.md

@@ -89,7 +89,9 @@ As an option, you can add a private endpoint for access to your resource. Select
 
 1. Confirm your configuration settings, and select **Create**.
 
-The Azure portal displays a notification when the new resource is available.
+1. The Azure portal displays a notification when the new resource is available. Select **Go to resource**.
+
+   :::image type="content" source="../media/create-resource/create-resource-go-to-resource.png" alt-text="Screenshot showing the Go to resource button in the Azure portal.":::
 
 ## Deploy a model