Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into placeholder-acct-key

Author: pauljewellmsft

.openpublishing.redirection.json

@@ -4087,10 +4087,12 @@
             "source_path_from_root": "/articles/event-grid/move-domains-across-regions.md",
             "redirect_url": "/azure",
             "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/data-factory/continuous-integration-delivery-automate-github-actions.md",
+            "redirect_url": "/azure",
+            "redirect_document_id": false
         }
-
-
-
-        
+       
     ]
 }

.openpublishing.redirection.virtual-desktop.json

@@ -419,6 +419,11 @@
             "source_path_from_root": "/articles/virtual-desktop/troubleshoot-getting-started.md",
             "redirect_url": "/azure/virtual-desktop/troubleshoot-quickstart",
             "redirect_document_id": true
+        },
+        {
+            "source_path_from_root": "/articles/virtual-desktop/fslogix-containers-azure-files.md",
+            "redirect_url": "/azure/virtual-desktop/fslogix-profile-containers",
+            "redirect_document_id": true
         }
     ]
 }

articles/active-directory-b2c/TOC.yml

@@ -824,8 +824,6 @@
     href: user-flow-versions-legacy.md
 - name: Resources
   items:
-  - name: Azure Roadmap
-    href: https://azure.microsoft.com/updates/?status=nowavailable,inpreview,indevelopment&category=identity,security&query=b2c
   - name: Frequently asked questions
     href: ./faq.yml
     displayName: FAQ

articles/ai-services/computer-vision/includes/identity-curl-quickstart.md

@@ -24,8 +24,7 @@ Get started with facial recognition using the Face REST API. The Face service pr
     * You'll need the key and endpoint from the resource you create to connect your application to the Face API. You'll paste your key and endpoint into the code below later in the quickstart.
     * You can use the free pricing tier (`F0`) to try the service, and upgrade later to a paid tier for production.
 * [PowerShell version 6.0+](/powershell/scripting/install/installing-powershell-core-on-windows), or a similar command-line application.
-* [cURL](https://curl.haxx.se/) installed.
-
+* [cURL](https://curl.se/) installed.
 
 
 
@@ -34,58 +33,134 @@ Get started with facial recognition using the Face REST API. The Face service pr
 > [!NOTE]
 > If you haven't received access to the Face service using the [intake form](https://aka.ms/facerecognition), some of these functions won't work.
 
-1. First, call the Detect API on the source face. This is the face that we'll try to identify from the larger group. Copy the following command to a text editor, insert your own key, and then copy it into a shell window and run it.
+1. First, call the Detect API on the source face. This is the face that we'll try to identify from the larger group. Copy the following command to a text editor, insert your own key and endpoint, and then copy it into a shell window and run it.
+
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_detect":::
+
+    #### [Linux](#tab/linux)
 
     :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_detect":::
 
+    ---
+
     Save the returned face ID string to a temporary location. You'll use it again at the end.
 
-1. Next you'll need to create a **LargePersonGroup**. This object will store the aggregated face data of several persons. Run the following command, inserting your own key. Optionally, change the group's name and metadata in the request body.
+1. Next you'll need to create a **LargePersonGroup** and give it an arbitrary ID that matches regex pattern `^[a-z0-9-_]+$`. This object will store the aggregated face data of several persons. Run the following command, inserting your own key. Optionally, change the group's name and metadata in the request body.
+
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_create_persongroup":::
+
+    #### [Linux](#tab/linux)
 
     :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_create_persongroup":::
 
-    Save the returned ID of the created group to a temporary location.
+    ---
+
+    Save the specified ID of the created group to a temporary location.
 
 1. Next, you'll create **Person** objects that belong to the group. Run the following command, inserting your own key and the ID of the **LargePersonGroup** from the previous step. This command creates a **Person** named "Family1-Dad".
 
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_create_person":::
+
+    #### [Linux](#tab/linux)
+
     :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_create_person":::
 
+    ---
+
     After you run this command, run it again with different input data to create more **Person** objects: "Family1-Mom", "Family1-Son", "Family1-Daughter", "Family2-Lady", and "Family2-Man".
 
     Save the IDs of each **Person** created; it's important to keep track of which person name has which ID.
 
 1. Next you'll need to detect new faces and associate them with the **Person** objects that exist. The following command detects a face from the image *Family1-Dad1.jpg* and adds it to the corresponding person. You need to specify the `personId` as the ID that was returned when you created the "Family1-Dad" **Person** object. The image name corresponds to the name of the created **Person**. Also enter the **LargePersonGroup** ID and your key in the appropriate fields.
 
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_add_face":::
+
+    #### [Linux](#tab/linux)
+
     :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_add_face":::
 
+    ---
+
     Then, run the above command again with a different source image and target **Person**. The images available are: *Family1-Dad1.jpg*, *Family1-Dad2.jpg* *Family1-Mom1.jpg*, *Family1-Mom2.jpg*, *Family1-Son1.jpg*, *Family1-Son2.jpg*, *Family1-Daughter1.jpg*, *Family1-Daughter2.jpg*, *Family2-Lady1.jpg*, *Family2-Lady2.jpg*, *Family2-Man1.jpg*, and *Family2-Man2.jpg*. Be sure that the **Person** whose ID you specify in the API call matches the name of the image file in the request body.
 
     At the end of this step, you should have multiple **Person** objects that each have one or more corresponding faces, detected directly from the provided images.
 
 1. Next, train the **LargePersonGroup** with the current face data. The training operation teaches the model how to associate facial features, sometimes aggregated from multiple source images, to each single person. Insert the **LargePersonGroup** ID and your key before running the command.
 
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_train":::
+
+    #### [Linux](#tab/linux)
+
     :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_train":::
+
+    ---
 
+1. Check whether the training status is succeeded. If not, wait for a while and query again.
+
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_check_status":::
+
+    #### [Linux](#tab/linux)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_check_status":::
+
+    ---
+
 1. Now you're ready to call the Identify API, using the source face ID from the first step and the **LargePersonGroup** ID. Insert these values into the appropriate fields in the request body, and insert your key.
 
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_identify":::
+
+    #### [Linux](#tab/linux)
+
     :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_identify":::
 
+    ---
+
     The response should give you a **Person** ID indicating the person identified with the source face. It should be the ID that corresponds to the "Family1-Dad" person, because the source face is of that person.
 
 1. To do face verification, you'll use the **Person** ID returned in the previous step, the **LargePersonGroup** ID, and also the source face ID. Insert these values into the fields in the request body, and insert your key.
 
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="verify":::
+
+    #### [Linux](#tab/linux)
+
     :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="verify":::
 
+    ---
+
     The response should give you a boolean verification result along with a confidence value.
 
 
 
 ## Clean up resources
 
-To delete the **LargePersonGroup** you created in this exercise, run the LargePersonGroup - Delete call.
+To delete the **LargePersonGroup** you created in this exercise, run the [LargePersonGroup - Delete](/rest/api/face/person-group-operations/delete-large-person-group) call.
+
+#### [Windows](#tab/windows)
+
+:::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_delete":::
+
+#### [Linux](#tab/linux)
 
 :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_delete":::
 
+---
+
 If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.
 
 * [Portal](../../multi-service-resource.md?pivots=azportal#clean-up-resources)

articles/ai-services/document-intelligence/containers/image-tags.md

@@ -61,7 +61,7 @@ The following containers support DocumentIntelligence v3.0 models and features:
 
 Document Intelligence container images can be found within the [**Microsoft Artifact Registry** (also know as Microsoft Container Registry(MCR))](https://mcr.microsoft.com/catalog?search=document%20intelligence), the primary registry for all Microsoft published container images.
 
-The following containers support DocumentIntelligence v3.0 models and features:
+The following containers support DocumentIntelligence v3.1 models and features:
 
 | Container name |image |
 |---|---|

articles/ai-services/document-intelligence/containers/install-run.md

@@ -19,7 +19,7 @@ ms.author: lajanuar
 <!-- markdownlint-disable MD051 -->
 
 :::moniker range="doc-intel-2.1.0 || doc-intel-4.0.0"
-Support for containers is currently available with Document Intelligence version `2022-08-31 (GA)` for all models and `2023-07-31 (GA)` for Read and Layout only:
+Support for containers is currently available with Document Intelligence version `2022-08-31 (GA)` for all models and `2023-07-31 (GA)` for Read, Layout, ID Document, Receipt and Invoice models:
 
 * [REST API `2022-08-31 (GA)`](/rest/api/aiservices/document-models/analyze-document?view=rest-aiservices-v3.0%20(2022-08-31)&preserve-view=true&tabs=HTTP)
 * [REST API `2023-07-31 (GA)`](/rest/api/aiservices/document-models/analyze-document?view=rest-aiservices-v3.1%20(2023-07-31)&tabs=HTTP&preserve-view=true)
@@ -38,9 +38,9 @@ Azure AI Document Intelligence is an Azure AI service that lets you build automa
 
 In this article you learn how to download, install, and run Document Intelligence containers. Containers enable you to run the Document Intelligence service in your own environment. Containers are great for specific security and data governance requirements.
 
-* **Read**, and **Layout**  models are supported by Document Intelligence v3.1 containers.
+* **Read**, **Layout**,  **ID Document**,  **Receipt**, and **Invoice**  models are supported by Document Intelligence v3.1 containers.
 
-* **Read**, **Layout**, **General Document**, **ID Document**,  **Receipt**, **Invoice**, **Business Card**, and **Custom** models are supported by Document Intelligence v3.0 containers.
+* **Read**, **Layout**, **General Document**, **Business Card**, and **Custom** models are supported by Document Intelligence v3.0 containers.
 
 * **Business Card** model is currently only supported in the [v2.1 containers](install-run.md?view=doc-intel-2.1.0&preserve-view=true).
 

articles/ai-services/document-intelligence/studio-overview.md

@@ -53,6 +53,9 @@ Your organization can opt to disable local authentication and enforce Microsoft
 
 * **Designating role assignments**. Document Intelligence Studio basic access requires the [`Cognitive Services User`](../../role-based-access-control/built-in-roles/ai-machine-learning.md#cognitive-services-user) role. For more information, *see* [Document Intelligence role assignments](quickstarts/try-document-intelligence-studio.md#azure-role-assignments) and [Document Intelligence Studio Permission](faq.yml#what-permissions-do-i-need-to-access-document-intelligence-studio-).
 
+> [!IMPORTANT]
+> Make sure you have the Cognitive Services User role, and not the Cognitive Services Contributor role when setting up Entra authentication. In Azure concept, Contributor role can only perform actions to control and manage the resource itself, including listing the access keys. Any user accounts with "Contributor" role that is able to access the Document Intelligence service is calling with access keys. However, when setting up access with Entra ID, key-access will be disabled and Cognitive Service User role will be required for an account to use the resources.
+
 ## Authentication
 
 Navigate to the [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/). If it's your first time logging in, a popup window appears prompting you to configure your service resource. In accordance with your organization's policy, you have one or two options:

articles/ai-services/openai/concepts/content-filter.md

@@ -989,7 +989,7 @@ data: [DONE]
 ```
 
 > [!IMPORTANT]
-> When content filtering is triggered for a prompt and a `"status": 400` is received as part of the response there will be a charge for this request as the prompt was evaluated by the service. [Charges will also occur](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) when a `"status":200` is received with `"finish_reason": "content_filter"`. In this case the prompt did not have any issues, but the completion generated by the model was detected to violate the content filtering rules which results in the completion being filtered.
+> When content filtering is triggered for a prompt and a `"status": 400` is received as part of the response there will be a charge for this request as the prompt was evaluated by the service. Due to the asynchronous nature of the content filtering system, a charge for both the prompt and completion tokens will occur. [Charges will also occur](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) when a `"status":200` is received with `"finish_reason": "content_filter"`. In this case the prompt did not have any issues, but the completion generated by the model was detected to violate the content filtering rules which results in the completion being filtered.
 
 ## Best practices
 

articles/ai-services/openai/faq.yml

@@ -7,7 +7,7 @@ metadata:
   manager: nitinme
   ms.service: azure-ai-openai
   ms.topic: faq
-  ms.date: 04/24/2024
+  ms.date: 06/12/2024
   ms.author: mbullwin
   author: mrbullwinkle
 title: Azure OpenAI Service frequently asked questions
@@ -228,6 +228,11 @@ sections:
           What are the known limitations of GPT-4 Turbo with Vision?
         answer: |
           See the [limitations](./concepts/gpt-with-vision.md#limitations) section of the GPT-4 Turbo with Vision concepts guide.
+      - question: |
+          I keep getting truncated responses when I use GPT-4 Turbo vision models. Why is this happening?
+        answer:
+          By default GPT-4 `vision-preview` and GPT-4 `turbo-2024-04-09` have a `max_tokens` value of 16. Depending on your request this value is often too low and can lead to truncated responses. To resolve this issue, pass a larger `max_tokens` value as part of your chat completions API requests. GPT-4o defaults to 4096 max_tokens.
+
   - name: Assistants
     questions:
       - question: |

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

@@ -10,7 +10,7 @@ ms.custom:
   - ignite-2023
   - references_regions
 ms.topic: conceptual
-ms.date: 06/05/2024
+ms.date: 06/12/2024
 ms.author: mbullwin
 ---
 
@@ -46,6 +46,7 @@ The following sections provide you with a quick guide to the default quotas and
 | Max file size for Assistants & fine-tuning | 512 MB |
 | Assistants token limit | 2,000,000 token limit |
 | GPT-4o max images per request (# of images in the messages array/conversation history) | 10 |
+| GPT-4 `vision-preview` & GPT-4 `turbo-2024-04-09` default max tokens | 16 <br><br> Increase the `max_tokens` parameter value to avoid truncated responses. GPT-4o max tokens defaults to 4096. |
 
 ## Regional quota limits