Merge branch 'main' of https://github.com/microsoftdocs/azure-docs-pr into dhsm-emails

Author: msmbaldwin

articles/ai-services/openai/assistants-reference-messages.md

@@ -36,7 +36,7 @@ Create a message.
 
 |Name | Type | Required | Description |
 |---  |---   |---       |---          |
-| `role` | string | Required | The role of the entity that is creating the message. Can be `user` or `assistant`. `assistant` indicates the message is sent by an actual user and should be used in most cases to represent user-generated messages. `assistant` indicates the message is generated by the assistant. Use this value to insert messages from the assistant into the conversation. |
+| `role` | string | Required | The role of the entity that is creating the message. Can be `user` or `assistant`. `user` indicates the message is sent by an actual user and should be used in most cases to represent user-generated messages. `assistant` indicates the message is generated by the assistant. Use this value to insert messages from the assistant into the conversation. |
 | `content` | string | Required | The content of the message. |
 | `file_ids` | array | Optional | A list of File IDs that the message should use. There can be a maximum of 10 files attached to a message. Useful for tools like retrieval and code_interpreter that can access and use files. |
 | `metadata` | map | Optional | Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format. Keys can be a maximum of 64 characters long and values can be a maximum of 512 characters long. |
@@ -371,7 +371,7 @@ Represents a message within a thread.
 | `object` | string  |The object type, which is always thread.message.|
 | `created_at` | integer  |The Unix timestamp (in seconds) for when the message was created.|
 | `thread_id` | string  |The thread ID that this message belongs to.|
-| `role` | string  |The entity that produced the message. One of user or assistant.|
+| `role` | string  |The entity that produced the message. One of `user` or `assistant`.|
 | `content` | array  |The content of the message in array of text and/or images.|
 | `assistant_id` | string or null  |If applicable, the ID of the assistant that authored this message.|
 | `run_id` | string or null  |If applicable, the ID of the run associated with the authoring of this message.|

articles/ai-studio/quickstarts/get-started-code.md

@@ -13,7 +13,6 @@ author: eric-urban
 ---
 
 # Build a custom chat app in Python using the prompt flow SDK
-
 [!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
 
 In this quickstart, we walk you through setting up your local development environment with the prompt flow SDK. We write a prompt, run it as part of your app code, trace the LLM calls being made, and run a basic evaluation on the outputs of the LLM.
@@ -139,7 +138,7 @@ Activating the Python environment means that when you run ```python``` or ```pip
 
 ## Install the prompt flow SDK
 
-In this section, we use prompt flow to build our application. [Prompt flow](https://microsoft.github.io/promptflow/) is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring.
+In this section, we use prompt flow to build our application. [Prompt flow](https://microsoft.github.io/promptflow) is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring.
 
 Use pip to install the prompt flow SDK into the virtual environment that you created.
 ```
@@ -161,24 +160,24 @@ Your AI services endpoint and deployment name are required to call the Azure Ope
 1. Create a ```.env``` file, and paste the following code:
     ```
     AZURE_OPENAI_ENDPOINT=endpoint_value
-    AZURE_OPENAI_DEPLOYMENT_NAME=deployment_name
-    AZURE_OPENAI_API_VERSION=2024-02-15-preview
+    AZURE_OPENAI_CHAT_DEPLOYMENT=chat_deployment_name
+    AZURE_OPENAI_API_VERSION=api_version
     ```
 
 1. Navigate to the [chat playground inside of your AI Studio project](./get-started-playground.md#chat-in-the-playground-without-your-data). First validate that chat is working with your model by sending a message to the LLM.
 1. Find the Azure OpenAI deployment name in the chat playground. Select the deployment in the dropdown and hover over the deployment name to view it. In this example, the deployment name is **gpt-35-turbo-16k**.
 
     :::image type="content" source="../media/quickstarts/promptflow-sdk/playground-deployment-view-code.png" alt-text="Screenshot of the AI Studio chat playground opened, highlighting the deployment name and the view code button." lightbox="../media/quickstarts/promptflow-sdk/playground-deployment-view-code.png":::
 
-1. In the ```.env``` file, replace ```deployment_name``` with the name of the deployment from the previous step. In this example, we're using the deployment name ```gpt-35-turbo-16k```. 
-1. Select the **<\> View Code** button and copy the endpoint value.
+1. In the ```.env``` file, replace ```chat_deployment_name``` with the name of the deployment from the previous step. In this example, we're using the deployment name ```gpt-35-turbo-16k```. 
+1. Select the **<\> View Code** button and copy the endpoint value and API version value.
 
     :::image type="content" source="../media/quickstarts/promptflow-sdk/playground-copy-endpoint.png" alt-text="Screenshot of the view code popup highlighting the button to copy the endpoint value." lightbox="../media/quickstarts/promptflow-sdk/playground-copy-endpoint.png":::
 
-1. In the ```.env``` file, replace ```endpoint_value``` with the endpoint value copied from the dialog in the previous step. 
+1. In the ```.env``` file, replace ```endpoint_value``` with the endpoint value and replace ```api_version``` with the API version copied from the dialog in the previous step (such as "2024-02-15-preview"). 
 
 > [!WARNING]
-> Key based authentication is supported but isn't recommended by Microsoft. If you want to use keys you can add your key to the ```.env```, but please ensure that your ```.env``` is in your ```.gitignore``` file so that you don't accidentally checked into your git repository.
+> Key based authentication is supported but isn't recommended by Microsoft. If you want to use keys you can add your key to the ```.env```, but please ensure that your ```.env``` is in your ```.gitignore``` file so that you don't accidentally check it into your git repository.
 
 ## Create a basic chat prompt and app
 
@@ -231,7 +230,7 @@ load_dotenv()
 from promptflow.core import Prompty, AzureOpenAIModelConfiguration
 
 model_config = AzureOpenAIModelConfiguration(
-    azure_deployment=os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME"),
+    azure_deployment=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT"),
     api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
     azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
 )
@@ -307,7 +306,7 @@ from promptflow.core import Prompty, AzureOpenAIModelConfiguration
 from promptflow.evals.evaluators import ChatEvaluator
 
 model_config = AzureOpenAIModelConfiguration(
-    azure_deployment=os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME"),
+    azure_deployment=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT"),
     api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
     azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
 )
@@ -352,4 +351,4 @@ For more information on how to use prompt flow evaluators, including how to make
 
 - [Quickstart: Create a project and use the chat playground in Azure AI Studio](./get-started-playground.md)
 - [Work with projects in VS Code](../how-to/develop/vscode.md)
-- [Overview of the Azure AI SDKs](../how-to/develop/sdk-overview.md)
+- [Overview of the Azure AI SDKs](../how-to/develop/sdk-overview.md)

articles/aks/outbound-rules-control-egress.md

@@ -60,7 +60,7 @@ The following network and FQDN/application rules are required for an AKS cluster
 |----------------------------------|-----------------|----------|
 | **`*.hcp.<location>.azmk8s.io`** | **`HTTPS:443`** | Required for Node <-> API server communication. Replace *\<location\>* with the region where your AKS cluster is deployed. This is required for clusters with *konnectivity-agent* enabled. Konnectivity also uses Application-Layer Protocol Negotiation (ALPN) to communicate between agent and server. Blocking or rewriting the ALPN extension will cause a failure. This isn't required for [private clusters][private-clusters]. |
 | **`mcr.microsoft.com`**          | **`HTTPS:443`** | Required to access images in Microsoft Container Registry (MCR). This registry contains first-party images/charts (for example, coreDNS, etc.). These images are required for the correct creation and functioning of the cluster, including scale and upgrade operations.  |
-| **`*.data.mcr.microsoft.com`**   | **`HTTPS:443`** | Required for MCR storage backed by the Azure content delivery network (CDN). |
+| **`*.data.mcr.microsoft.com`**, **`mcr-0001.mcr-msedge.net`**   | **`HTTPS:443`** | Required for MCR storage backed by the Azure content delivery network (CDN). |
 | **`management.azure.com`**       | **`HTTPS:443`** | Required for Kubernetes operations against the Azure API. |
 | **`login.microsoftonline.com`**  | **`HTTPS:443`** | Required for Microsoft Entra authentication. |
 | **`packages.microsoft.com`**     | **`HTTPS:443`** | This address is the Microsoft packages repository used for cached *apt-get* operations.  Example packages include Moby, PowerShell, and Azure CLI. |

articles/azure-resource-manager/bicep/user-defined-data-types.md

@@ -3,7 +3,7 @@ title: User-defined types in Bicep
 description: Describes how to define and use user-defined data types in Bicep.
 ms.topic: conceptual
 ms.custom: devx-track-bicep
-ms.date: 05/22/2024
+ms.date: 06/14/2024
 ---
 
 # User-defined data types in Bicep
@@ -258,7 +258,7 @@ output config object = serviceConfig
 
 The parameter value is validated based on the discriminated property value.  In the preceding example, if the *serviceConfig* parameter value is of type *foo*, it undergoes validation using the *FooConfig*type. Likewise, if the parameter value is of type *bar*, validation is performed using the *BarConfig* type, and this pattern continues for other types as well.
 
-## Import types between Bicep files (Preview)
+## Import types between Bicep files
 
 [Bicep CLI version 0.21.X or higher](./install.md) is required to use this compile-time import feature. The experimental flag `compileTimeImports` must be enabled from the [Bicep config file](./bicep-config.md#enable-experimental-features).
 

articles/azure-resource-manager/management/tag-support.md

@@ -418,7 +418,7 @@ To get the same data as a file of comma-separated values, download [tag-support.
 > | billingAccounts / appliedReservationOrders | No | No |
 > | billingAccounts / associatedTenants | No | No |
 > | billingAccounts / billingPermissions | No | No |
-> | billingAccounts / billingProfiles | No | No |
+> | billingAccounts / billingProfiles | Yes | Yes |
 > | billingAccounts / billingProfiles / billingPermissions | No | No |
 > | billingAccounts / billingProfiles / billingRoleAssignments | No | No |
 > | billingAccounts / billingProfiles / billingRoleDefinitions | No | No |
@@ -429,7 +429,7 @@ To get the same data as a file of comma-separated values, download [tag-support.
 > | billingAccounts / billingProfiles / invoices | No | No |
 > | billingAccounts / billingProfiles / invoices / pricesheet | No | No |
 > | billingAccounts / billingProfiles / invoices / transactions | No | No |
-> | billingAccounts / billingProfiles / invoiceSections | No | No |
+> | billingAccounts / billingProfiles / invoiceSections | Yes | Yes |
 > | billingAccounts / billingProfiles / invoiceSections / billingPermissions | No | No |
 > | billingAccounts / billingProfiles / invoiceSections / billingRoleAssignments | No | No |
 > | billingAccounts / billingProfiles / invoiceSections / billingRoleDefinitions | No | No |

articles/firewall/integrate-lb.md

@@ -5,15 +5,15 @@ services: firewall
 author: vhorne
 ms.service: firewall
 ms.topic: how-to
-ms.date: 10/27/2022
+ms.date: 06/14/2024
 ms.author: victorh
 ---
 
 # Integrate Azure Firewall with Azure Standard Load Balancer
 
 You can integrate an Azure Firewall into a virtual network with an Azure Standard Load Balancer (either public or internal). 
 
-The preferred design is to integrate an internal load balancer with your Azure firewall, as this is a much simpler design. You can use a public load balancer if you already have one deployed and you want to keep it in place. However, you need to be aware of an asymmetric routing issue that can break functionality with the public load balancer scenario.
+The preferred design is to integrate an internal load balancer with your Azure firewall, as this is a simpler design. You can use a public load balancer if you already have one deployed and you want to keep it in place. However, you need to be aware of an asymmetric routing issue that can break functionality with the public load balancer scenario.
 
 For more information about Azure Load Balancer, see [What is Azure Load Balancer?](../load-balancer/load-balancer-overview.md)
 
@@ -30,7 +30,7 @@ Asymmetric routing is where a packet takes one path to the destination and takes
 When you deploy an Azure Firewall into a subnet, one step is to create a default route for the subnet directing packets through the firewall's private IP address located on the AzureFirewallSubnet. For more information, see [Tutorial: Deploy and configure Azure Firewall using the Azure portal](tutorial-firewall-deploy-portal.md#create-a-default-route).
 
 When you introduce the firewall into your load balancer scenario, you want your Internet traffic to come in through your firewall's public IP address. From there, the firewall applies its firewall rules and NATs the packets to your load balancer's public IP address. This is where the problem occurs. Packets arrive on the firewall's public IP address, but return to the firewall via the private IP address (using the default route).
-To avoid this problem, create an additional host route for the firewall's public IP address. Packets going to the firewall's public IP address are routed via the Internet. This avoids taking the default route to the firewall's private IP address.
+To avoid this problem, create another host route for the firewall's public IP address. Packets going to the firewall's public IP address are routed via the Internet. This avoids taking the default route to the firewall's private IP address.
 
 :::image type="content" source="media/integrate-lb/Firewall-LB-asymmetric.png" alt-text="Diagram of asymmetric routing." lightbox="media/integrate-lb/Firewall-LB-asymmetric.png":::
 ### Route table example
@@ -58,7 +58,7 @@ So, you can deploy this scenario similar to the public load balancer scenario, b
 The virtual machines in the backend pool can have outbound Internet connectivity through the Azure Firewall. Configure a user defined route on the virtual machine's subnet with the firewall as the next hop.
 
 
-## Additional security
+## Extra security
 
 To further enhance the security of your load-balanced scenario, you can use network security groups (NSGs).
 

articles/machine-learning/how-to-managed-network.md

@@ -1111,6 +1111,7 @@ The Azure Machine Learning managed VNet feature is free. However, you're charged
 * Using FQDN outbound rules increases the cost of the managed VNet because FQDN rules use Azure Firewall. For more information, see [Pricing](#pricing).
 * FQDN outbound rules only support ports 80 and 443.
 * If your compute instance is in a managed network and is configured for no public IP, use the `az ml compute connect-ssh` command to connect to it using SSH.
+* When using Managed Vnet, you can't deploy compute resources inside your custom Vnet. Compute resources can only be created inside the managed Vnet.
 
 ### Migration of compute resources