Merge branch 'main' into release-umc-rebranding

Author: v-alje

.openpublishing.publish.config.json

@@ -416,6 +416,12 @@
       "branch": "main",
       "branch_mapping": {}
     },
+    {
+      "path_to_root": "azureml-examples-archive",
+      "url": "https://github.com/azure/azureml-examples",
+      "branch": "v1-archive",
+      "branch_mapping": {}
+    },
     {
       "path_to_root": "azureml-examples-batch-pup",
       "url": "https://github.com/azure/azureml-examples",

articles/active-directory/authentication/howto-mfa-mfasettings.md

@@ -200,7 +200,7 @@ The following table lists more numbers for different countries.
 | Sri Lanka            | +94 117750440   |
 | Sweden               | +46 701924176   |
 | Taiwan               | +886 277515260  |
-| Turkey               | +90 8505404893  |
+| Türkiye              | +90 8505404893  |
 | Ukraine              | +380 443332393  |
 | United Arab Emirates | +971 44015046   |
 | Vietnam              | +84 2039990161  |

articles/active-directory/external-identities/customers/how-to-customize-languages-customers.md

@@ -77,7 +77,7 @@ The following languages are supported in the customer tenant:
    - Spanish (Spain)
    - Swedish (Sweden)
    - Thai (Thailand)
-   - Turkish (Turkey)
+   - Turkish (Türkiye)
    - Ukrainian (Ukraine)
 
 6. Customize the elements on the **Basics**, **Layout**, **Header**, **Footer**, **Sign-in form**, and **Text** tabs. For detailed instructions, see [Customize the branding and end-user experience](how-to-customize-branding-customers.md).

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

@@ -575,75 +575,75 @@ http {
  ```yml
 version: '3.3'
 services:
- nginx:
-  image: nginx:alpine
-  container_name: reverseproxy
-  volumes:
-    - ${NGINX_CONF_FILE}:/etc/nginx/nginx.conf
-  ports:
-    - "5000:5000"
- layout:
-  container_name: azure-cognitive-service-layout
-  image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/layout-3.0:latest
-  environment:
-    eula: accept
-    apikey: ${FORM_RECOGNIZER_KEY}
-    billing: ${FORM_RECOGNIZER_ENDPOINT_URI}
-    Logging:Console:LogLevel:Default: Information
-    SharedRootFolder: /shared
-    Mounts:Shared: /shared
-    Mounts:Output: /logs
-  volumes:
-    - type: bind
-      source: ${SHARED_MOUNT_PATH}
-      target: /shared
-    - type: bind
-      source: ${OUTPUT_MOUNT_PATH}
-      target: /logs
-  expose:
-    - "5000"
+  nginx:
+    image: nginx:alpine
+    container_name: reverseproxy
+    volumes:
+      - ${NGINX_CONF_FILE}:/etc/nginx/nginx.conf
+    ports:
+      - "5000:5000"
+  layout:
+    container_name: azure-cognitive-service-layout
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/layout-3.0:latest
+    environment:
+      eula: accept
+      apikey: ${FORM_RECOGNIZER_KEY}
+      billing: ${FORM_RECOGNIZER_ENDPOINT_URI}
+      Logging:Console:LogLevel:Default: Information
+      SharedRootFolder: /shared
+      Mounts:Shared: /shared
+      Mounts:Output: /logs
+    volumes:
+      - type: bind
+        source: ${SHARED_MOUNT_PATH}
+        target: /shared
+      - type: bind
+        source: ${OUTPUT_MOUNT_PATH}
+        target: /logs
+    expose:
+      - "5000"
 
- custom-template:
-  container_name: azure-cognitive-service-custom-template
-  image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/custom-template-3.0:latest
-  restart: always
-  depends_on:
-    - layout
-  environment:
-    AzureCognitiveServiceLayoutHost: http://azure-cognitive-service-layout:5000
-    eula: accept
-    apikey: ${FORM_RECOGNIZER_KEY}
-    billing: ${FORM_RECOGNIZER_ENDPOINT_URI}
-    Logging:Console:LogLevel:Default: Information
-    SharedRootFolder: /shared
-    Mounts:Shared: /shared
-    Mounts:Output: /logs
-  volumes:
-    - type: bind
-      source: ${SHARED_MOUNT_PATH}
-      target: /shared
-    - type: bind
-      source: ${OUTPUT_MOUNT_PATH}
-      target: /logs
-  expose:
-    - "5000"
+  custom-template:
+    container_name: azure-cognitive-service-custom-template
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/custom-template-3.0:latest
+    restart: always
+    depends_on:
+      - layout
+    environment:
+      AzureCognitiveServiceLayoutHost: http://azure-cognitive-service-layout:5000
+      eula: accept
+      apikey: ${FORM_RECOGNIZER_KEY}
+      billing: ${FORM_RECOGNIZER_ENDPOINT_URI}
+      Logging:Console:LogLevel:Default: Information
+      SharedRootFolder: /shared
+      Mounts:Shared: /shared
+      Mounts:Output: /logs
+    volumes:
+      - type: bind
+        source: ${SHARED_MOUNT_PATH}
+        target: /shared
+      - type: bind
+        source: ${OUTPUT_MOUNT_PATH}
+        target: /logs
+    expose:
+      - "5000"
 
- studio:
-  container_name: form-recognizer-studio
-  image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/studio:3.0
-  environment:
-    ONPREM_LOCALFILE_BASEPATH: /onprem_folder
-    STORAGE_DATABASE_CONNECTION_STRING: /onprem_db/Application.db
-  volumes:
-    - type: bind
-      source: ${FILE_MOUNT_PATH} # path to your local folder
-      target: /onprem_folder
-    - type: bind
-      source: ${DB_MOUNT_PATH} # path to your local folder
-      target: /onprem_db
-  ports:
-    - "5001:5001"
-  user: "1000:1000" # echo $(id -u):$(id -g)
+  studio:
+    container_name: form-recognizer-studio
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/studio:3.0
+    environment:
+      ONPREM_LOCALFILE_BASEPATH: /onprem_folder
+      STORAGE_DATABASE_CONNECTION_STRING: /onprem_db/Application.db
+    volumes:
+      - type: bind
+        source: ${FILE_MOUNT_PATH} # path to your local folder
+        target: /onprem_folder
+      - type: bind
+        source: ${DB_MOUNT_PATH} # path to your local folder
+        target: /onprem_db
+    ports:
+      - "5001:5001"
+    user: "1000:1000" # echo $(id -u):$(id -g)
 
  ```
 
@@ -1051,7 +1051,7 @@ http {
 2. The following code sample is a self-contained `docker compose` example to run Document Intelligence Layout, Label Tool, Custom API, and Custom Supervised containers together. With `docker compose`, you use a YAML file to configure your application's services. Then, with `docker-compose up` command, you create and start all the services from your configuration.
 
  ```yml
- version: '3.3'
+version: '3.3'
 services:
  nginx:
   image: nginx:alpine

articles/ai-services/document-intelligence/how-to-guides/includes/v2-1/rest-api.md

@@ -34,7 +34,6 @@ ms.author: lajanuar
 * A URL for an **image of an invoice**. You can use a [sample document](https://raw.githubusercontent.com/Azure/azure-sdk-for-python/master/sdk/formrecognizer/azure-ai-formrecognizer/samples/sample_forms/forms/Invoice_1.pdf) for this quickstart.
 * A URL for an **image of an ID document**. You can use a [sample image](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/DriverLicense.png)
 
-
 ## Analyze layout
 
 You can use Document Intelligence to analyze and extract tables, selection marks, text, and structure in documents, without needing to train a model. For more information about layout extraction, see the [Layout conceptual guide](../../../concept-layout.md). Before you run the command, make these changes:
@@ -51,9 +50,9 @@ curl -v -i POST "https://{endpoint}/formrecognizer/v2.1/layout/analyze" -H "Cont
 
 #### Operation-Location
 
-You receive a `202 (Success)` response that includes an **Operation-Location** header. The value of this header contains a result ID that you can use to query the status of the asynchronous operation and get the results:
+You receive a `202 (Success)` response that includes a read-only **Operation-Location** header. The value of this header contains a `resultID` that can be queried to get the status of the asynchronous operation and retrieve the results using a GET request with your same resource subscription key:
 
-https://<span></span>cognitiveservice/formrecognizer/v2.1/layout/analyzeResults/**{resultId}**. 
+https://<span></span>cognitiveservice/formrecognizer/v2.1/layout/analyzeResults/**{resultId}**.
 
 In the following example, as part of the URL, the string after `analyzeResults/` is the result ID.
 
@@ -63,7 +62,7 @@ https://cognitiveservice/formrecognizer/v2/layout/analyzeResults/54f0b076-4e38-4
 
 ### Get layout results
 
-After you've called the **[Analyze Layout](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/AnalyzeLayoutAsync)** API, you call the **[Get Analyze Layout Result](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/GetAnalyzeLayoutResult)** API to get the status of the operation and the extracted data. Before you run the command, make these changes:
+After you've called the **[Analyze Layout](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/AnalyzeLayoutAsync)** API, poll the **[Get Analyze Layout Result](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/GetAnalyzeLayoutResult)** API to get the status of the operation and the extracted data. Before you run the command, make these changes:
 
 1. Replace `{endpoint}` with the endpoint that you obtained with your Document Intelligence subscription.
 1. Replace `{key}` with the key you copied from the previous step.
@@ -83,7 +82,7 @@ You receive a `200 (success)` response with JSON content.
 See the following invoice image and its corresponding JSON output.
 
 * The `"readResults"` node contains every line of text with its respective bounding box placement on the page.
-* The `selectionMarks` node shows every selection mark (checkbox, radio mark) and whether its status is "selected" or "unselected".
+* The `selectionMarks` node shows every selection mark (checkbox, radio mark) and whether its status is `selected` or `unselected`.
 * The `"pageResults"` section includes the tables extracted. For each table, the text, row, and column index, row and column spanning, bounding box, and more are extracted.
 
 :::image type="content" source="../../../media/contoso-invoice.png" alt-text="Screenshot of Contoso project statement document with a table.":::
@@ -213,7 +212,7 @@ This output has been shortened for simplicity. See the [full sample output on Gi
 
 ## Analyze receipts
 
-This section demonstrates how to analyze and extract common fields from US receipts, using a pre-trained receipt model. For more information about receipt analysis, see the [Receipts conceptual guide](../../../concept-receipt.md). To start analyzing a receipt, call the **[Analyze Receipt](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/AnalyzeReceiptAsync)** API using the cURL command. Before you run the command, make these changes:
+This section demonstrates how to analyze and extract common fields from US receipts, using a pretrained receipt model. For more information about receipt analysis, see the [Receipts conceptual guide](../../../concept-receipt.md). To start analyzing a receipt, call the **[Analyze Receipt](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/AnalyzeReceiptAsync)** API using the cURL command. Before you run the command, make these changes:
 
 1. Replace `{endpoint}` with the endpoint that you obtained with your Document Intelligence subscription.
 1. Replace `{your receipt URL}` with the URL address of a receipt image.
@@ -255,7 +254,7 @@ curl -X GET "https://{endpoint}/formrecognizer/v2.1/prebuilt/receipt/analyzeResu
 
 You receive a `200 (Success)` response with JSON output. The first field, `"status"`, indicates the status of the operation. If the operation isn't complete, the value of `"status"` is `"running"` or `"notStarted"`, and you should call the API again, either manually or through a script. We recommend an interval of one second or more between calls.
 
-The `"readResults"` node contains all of the recognized text (if you set the optional *includeTextDetails* parameter to `true`). Text is organized by page, then by line, then by individual words. The `"documentResults"` node contains the receipt-specific values that the model discovered. The  `"documentResults"` node is where you find useful key/value pairs like the tax, total, merchant address, and so on.
+The `"readResults"` node contains all of the recognized text (if you set the optional *includeTextDetails* parameter to `true`). The response organizes text by page, then by line, then by individual words. The `"documentResults"` node contains the receipt-specific values that the model discovered. The  `"documentResults"` node is where you find useful key/value pairs like the tax, total, merchant address, and so on.
 
 See the following receipt image and its corresponding JSON output.
 
@@ -592,7 +591,7 @@ This output has been shortened for readability. See the [full sample output on G
 
 ## Analyze business cards
 
-This section demonstrates how to analyze and extract common fields from English business cards, using a pre-trained model. For more information about business card analysis, see the [Business cards conceptual guide](../../../concept-business-card.md). To start analyzing a business card, you call the **[Analyze Business Card](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/AnalyzeBusinessCardAsync)** API using the cURL command. Before you run the command, make these changes:
+This section demonstrates how to analyze and extract common fields from English business cards, using a pretrained model. For more information about business card analysis, see the [Business cards conceptual guide](../../../concept-business-card.md). To start analyzing a business card, you call the **[Analyze Business Card](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/AnalyzeBusinessCardAsync)** API using the cURL command. Before you run the command, make these changes:
 
 1. Replace `{endpoint}` with the endpoint that you obtained with your Document Intelligence subscription.
 1. Replace `{your business card URL}` with the URL address of a receipt image.
@@ -633,7 +632,7 @@ curl -v -X GET https://{endpoint}/formrecognizer/v2.1/prebuilt/businessCard/anal
 
 You receive a `200 (Success)` response with JSON output.
 
-The `"readResults"` node contains all of the recognized text. Text is organized by page, then by line, then by individual words. The `"documentResults"` node contains the business-card-specific values that the model discovered. The `"documentResults"` node is where you find useful contact information like the company name, first name, last name, phone number, and so on.
+The `"readResults"` node contains all of the recognized text. The response organizes text by page, then by line, then by individual words. The `"documentResults"` node contains the business-card-specific values that the model discovered. The `"documentResults"` node is where you find useful contact information like the company name, first name, last name, phone number, and so on.
 
 ![A business card from Contoso company](../../../media/business-card-english.jpg)
 
@@ -1218,13 +1217,13 @@ https://westus.api.cognitive.microsoft.com/formrecognizer/v2.1/custom/models/77d
 
 ### Train a model with labels
 
-To train with labels, you need to have special label information files (`\<filename\>.pdf.labels.json`) in your blob storage container alongside the training documents. The [Document Intelligence Sample Labeling tool](../../../label-tool.md) provides a UI to help you create these label files. Once you've them, you can call the **[Train Custom Model](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/TrainCustomModelAsync)** API, with the `"useLabelFile"` parameter set to `true` in the JSON body.
+To train with labels, you need to have special label information files (`\<filename\>.pdf.labels.json`) in your blob storage container alongside the training documents. The [Document Intelligence Sample Labeling tool](../../../label-tool.md) provides a UI to help you create these label files. Once you have them, you can call the **[Train Custom Model](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/TrainCustomModelAsync)** API, with the `"useLabelFile"` parameter set to `true` in the JSON body.
 
 Before you run the command, make these changes:
 
 1. Replace `{endpoint}` with the endpoint that you obtained with your Document Intelligence subscription.
 1. Replace `{key}` with the key you copied from the previous step.
-1. Replace `{SAS URL}` with the Azure Blob storage container's shared access signature (SAS) URL. 
+1. Replace `{SAS URL}` with the Azure Blob storage container's shared access signature (SAS) URL.
 
    * To retrieve the SAS URL for your custom model training data, go to your storage resource in the Azure portal and select the **Storage Explorer** tab. Navigate to your container, right-click, and select **Get shared access signature**. It's important to get the SAS for your container, not for the storage account itself. Make sure the **Read**, **Write**, **Delete** and **List** permissions are checked, and select **Create**. Then copy the value in the **URL** section to a temporary location. It should have the form: `https://<storage account>.blob.core.windows.net/<container name>?<SAS value>`.
 

articles/ai-services/document-intelligence/how-to-guides/includes/v3-0/rest-api.md

@@ -77,15 +77,15 @@ curl -i -X POST "%FR_ENDPOINT%formrecognizer/documentModels/{modelID}:analyze?ap
 
    **Enable add-on capabilities
 
-   To enable add-on capabilities, use the `features` query parameter in the POST request. There are four add-on capabilities available with the 2023-07-31 (GA) release: *ocr.highResolution*, *ocr.formula*, *ocr.font*, and *queryFields.premium*. To learn more about each of the capabilities, visit the [Add-On Capabilities concept page](../../../concept-accuracy-confidence.md). You can only call the highResolution, formula and font capabilities for the Read and Layout model, and the queryFields capability for the General Documents model. The following example shows how to call the highResolution, formula and font capabilities for the Layout model.
+   To enable add-on capabilities, use the `features` query parameter in the POST request. There are four add-on capabilities available with the `2023-07-31` (GA) release: *ocr.highResolution*, *ocr.formula*, *ocr.font*, and *queryFields.premium*. To learn more about each of the capabilities, visit the [Add-On Capabilities concept page](../../../concept-accuracy-confidence.md). You can only call the highResolution, formula and font capabilities for the Read and Layout model, and the queryFields capability for the General Documents model. The following example shows how to call the highResolution, formula and font capabilities for the Layout model.
 
    ```bash
    curl -i -X POST "%FR_ENDPOINT%formrecognizer/documentModels/prebuilt-layout:analyze?features=ocr.highResolution,ocr.formula,ocr.font?api-version=2023-07-31" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: %FR_KEY%" --data-ascii "{'urlSource': '{document-url}'}"
    ```
 
 ### POST response
 
-You receive a `202 (Success)` response that includes an **Operation-location** header. You use the value of this header to retrieve the response results.
+You receive a `202 (Success)` response that includes an **Operation-Location** header. You use the value of this header to retrieve the response results.
 
 :::image type="content" source="../../../media/how-to/rest-get-response.png" alt-text="{alt-text}":::
 
@@ -162,11 +162,11 @@ After you've called the [**Analyze document**](https://westus.dev.cognitive.micr
 
 Before you run the following command, make these changes:
 
-1. Replace `{POST response}` with the Operation-location header from the [POST response](#post-response).
+1. Replace `{POST response}` with the Operation-Location header from the [POST response](#post-response).
 
 1. Replace `FR_KEY` with the variable name for your environment variable if it differs.
 
-* Replace `{POST response}` with the Operation-location header from the [POST response](#post-response).
+* Replace `{POST response}` with the Operation-Location header from the [POST response](#post-response).
 
 * Replace `FR_KEY` with the variable for your environment variable if it differs from the name in the code.