Merge pull request #248608 from laujan/147645-javascript-v3-1-sdk-update

v-regandowner · web-flow · commit e9af4333da28 · 2023-08-17T19:59:09.000-04:00
javascript v3.1 sdk update
diff --git a/articles/ai-services/document-intelligence/containers/install-run.md b/articles/ai-services/document-intelligence/containers/install-run.md
@@ -26,7 +26,7 @@ monikerRange: '<=doc-intel-3.1.0'
 [!INCLUDE [applies to v2.1](../includes/applies-to-v2-1.md)]
 ::: moniker-end
 
-Azure AI Document Intelligence is an Azure AI service that lets you build automated data processing software using machine-learning technology. Document Intelligence enables you to identify and extract text, key/value pairs, selection marks, table data, and more from your form documents. The results are delivered as structured data that includes the relationships in the original file.
+Azure AI Document Intelligence is an Azure AI service that lets you build automated data processing software using machine-learning technology. Document Intelligence enables you to identify and extract text, key/value pairs, selection marks, table data, and more from your documents. The results are delivered as structured data that includes the relationships in the original file.
 
 ::: moniker range=">=doc-intel-3.0.0"
 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.
diff --git a/articles/ai-services/document-intelligence/create-sas-tokens.md b/articles/ai-services/document-intelligence/create-sas-tokens.md
@@ -81,7 +81,7 @@ To get started, you need:
     :::image type="content" source="media/sas-tokens/upload-blob-window.png" alt-text="Screenshot that shows the Upload blob window in the Azure portal.":::
 
    > [!NOTE]
-   > By default, the REST API uses form documents located at the root of your container. You can also use data organized in subfolders if specified in the API call. For more information, see [Organize your data in subfolders](how-to-guides/build-a-custom-model.md?view=doc-intel-2.1.0&preserve-view=true#organize-your-data-in-subfolders-optional).
+   > By default, the REST API uses documents located at the root of your container. You can also use data organized in subfolders if specified in the API call. For more information, see [Organize your data in subfolders](how-to-guides/build-a-custom-model.md?view=doc-intel-2.1.0&preserve-view=true#organize-your-data-in-subfolders-optional).
 
 ## Use the Azure portal
 
diff --git a/articles/ai-services/document-intelligence/faq.yml b/articles/ai-services/document-intelligence/faq.yml
@@ -7,7 +7,7 @@ metadata:
   ms.service: applied-ai-services
   ms.subservice: forms-recognizer
   ms.topic: faq
-  ms.date: 08/02/2023
+  ms.date: 08/17/2023
   ms.author: lajanuar
   monikerRange: '<=doc-intel-3.1.0'
 
@@ -24,20 +24,28 @@ sections:
           What is Azure AI Document Intelligence and what happened to Form Recognizer?
         answer: |
 
-          - Form Recognizer is now Azure AI Document Intelligence!
-
           - Azure AI Document Intelligence is a cloud-based Azure AI service that uses machine-learning models to extract key-value pairs, text, and tables from your documents. The returned result is a structured JSON output.
 
           - Document Intelligence use cases include automated data processing, enhanced data-driven strategies, and enriched document search capabilities.
 
-          - As of July 2023, Azure AI services encompass all of what were previously known as Azure Cognitive Services and Azure Applied AI Services. There are no changes to pricing. The names "Cognitive Services" and "Azure Applied AI" continue to be used in Azure billing, cost analysis, price list, and price APIs. There are no breaking changes to application programming interfaces (APIs) or SDKs.
+          - As of July 2023, Form Recognizer is now Azure AI Document Intelligence!
+
+          -  Azure AI services encompass all of what were previously known as Azure Cognitive Services and Azure Applied AI Services.
+
+           - There are no changes to pricing. The names "Cognitive Services" and "Azure Applied AI" continue to be used in Azure billing, cost analysis, price list, and price APIs.
+
+           - There are no breaking changes to application programming interfaces (APIs) or SDKs.
+
+           - Some platforms are still awaiting the renaming update. All mention of Form Recognizer or Document Intelligence in our documentation refers to the same Azure service.
+
+
 
       - question: |
           How is Document Intelligence related to Document Generative AI?
         answer: |
 
           A Document Generative AI solution can enable you to chat with your documents, generate captivating content from them and access the power of Azure OpenAI models on your data. With Azure AI Document Intelligence and Azure OpenAI combined, you can build an enterprise application to seamlessly interact with your documents using natural languages, easily find answers and gain valuable insights, effortlessly generate new and engaging content from your existing documents. Check for more details in the [technical community blog](https://techcommunity.microsoft.com/t5/azure-ai-services-blog/document-generative-ai-the-power-of-azure-ai-document/ba-p/3875015).
-      
+
       - question: |
           Which Document Intelligence use cases require special consideration?
         answer: |
diff --git a/articles/ai-services/document-intelligence/how-to-guides/build-a-custom-model.md b/articles/ai-services/document-intelligence/how-to-guides/build-a-custom-model.md
@@ -175,7 +175,7 @@ Follow these tips to further optimize your data set for training.
 
 ## Upload your training data
 
-When you've put together the set of form documents for training, you need to upload it to an Azure blob storage container. If you don't know how to create an Azure storage account with a container, follow the [Azure Storage quickstart for Azure portal](../../../storage/blobs/storage-quickstart-blobs-portal.md). Use the standard performance tier.
+When you've put together the set of documents for training, you need to upload it to an Azure blob storage container. If you don't know how to create an Azure storage account with a container, follow the [Azure Storage quickstart for Azure portal](../../../storage/blobs/storage-quickstart-blobs-portal.md). Use the standard performance tier.
 
 If you want to use manually labeled data, upload the *.labels.json* and *.ocr.json* files that correspond to your training documents. You can use the [Sample Labeling tool](../label-tool.md) (or your own UI) to generate these files.
 
diff --git a/articles/ai-services/document-intelligence/how-to-guides/includes/v2-1/csharp-sdk.md b/articles/ai-services/document-intelligence/how-to-guides/includes/v2-1/csharp-sdk.md
@@ -290,7 +290,7 @@ The following code processes the ID document at the given URI and prints the maj
 
 ## Train a custom model
 
-This section demonstrates how to train a model with your own data. A trained model can output structured data that includes the key/value relationships in the original form document. After you train the model, you can test, retrain, and eventually use it to reliably extract data from more forms according to your needs.
+This section demonstrates how to train a model with your own data. A trained model can output structured data that includes the key/value relationships in the original document. After you train the model, you can test, retrain, and eventually use it to reliably extract data from more forms according to your needs.
 
 > [!NOTE]
 > You can also train models with a graphical user interface such as the [Document Intelligence Sample Labeling tool](../../../label-tool.md).
diff --git a/articles/ai-services/document-intelligence/how-to-guides/includes/v2-1/java-sdk.md b/articles/ai-services/document-intelligence/how-to-guides/includes/v2-1/java-sdk.md
@@ -258,7 +258,7 @@ The following code processes the ID document at the given URI and prints the maj
 
 ## Train a custom model
 
-This section demonstrates how to train a model with your own data. A trained model can output structured data that includes the key/value relationships in the original form document. After you train the model, you can test, retrain, and eventually use it to reliably extract data from more forms according to your needs.
+This section demonstrates how to train a model with your own data. A trained model can output structured data that includes the key/value relationships in the original document. After you train the model, you can test, retrain, and eventually use it to reliably extract data from more forms according to your needs.
 
 > [!NOTE]
 > You can also train models with a graphical user interface such as the [Document Intelligence Sample Labeling tool](../../../label-tool.md).
diff --git a/articles/ai-services/document-intelligence/how-to-guides/includes/v2-1/javascript-sdk.md b/articles/ai-services/document-intelligence/how-to-guides/includes/v2-1/javascript-sdk.md
@@ -192,7 +192,7 @@ To analyze ID documents from a URL, use the `beginRecognizeIdDocumentsFromUrl` m
 
 ## Train a custom model
 
-This section demonstrates how to train a model with your own data. A trained model can output structured data that includes the key/value relationships in the original form document. After you train the model, you can test, retrain, and eventually use it to reliably extract data from more forms according to your needs.
+This section demonstrates how to train a model with your own data. A trained model can output structured data that includes the key/value relationships in the original document. After you train the model, you can test, retrain, and eventually use it to reliably extract data from more forms according to your needs.
 
 > [!NOTE]
 > You can also train models with a graphical user interface (GUI) such as the [Document Intelligence Sample Labeling tool](../../../label-tool.md).
diff --git a/articles/ai-services/document-intelligence/how-to-guides/includes/v2-1/python-sdk.md b/articles/ai-services/document-intelligence/how-to-guides/includes/v2-1/python-sdk.md
@@ -92,7 +92,7 @@ You need to add references to the URLs for your training and testing data.
 * Use the sample form and receipt images included in the samples (also available on [GitHub](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/formrecognizer/azure-ai-formrecognizer/samples/sample_forms) or you can use the above steps to get the SAS URL of an individual document in blob storage.
 
 > [!NOTE]
-> The code snippets in this project use remote forms accessed by URLs. If you want to process local form documents instead, see the related methods in the [reference documentation](/python/api/azure-ai-formrecognizer) and [samples](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/formrecognizer/azure-ai-formrecognizer/samples).
+> The code snippets in this project use remote forms accessed by URLs. If you want to process local documents instead, see the related methods in the [reference documentation](/python/api/azure-ai-formrecognizer) and [samples](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/formrecognizer/azure-ai-formrecognizer/samples).
 
 ## Analyze layout
 
@@ -194,7 +194,7 @@ To analyze ID documents from a URL, use the `begin_recognize_id_documents_from_u
 
 ## Train a custom model
 
-This section demonstrates how to train a model with your own data. A trained model can output structured data that includes the key/value relationships in the original form document. After you train the model, you can test, retrain, and eventually use it to reliably extract data from more forms according to your needs.
+This section demonstrates how to train a model with your own data. A trained model can output structured data that includes the key/value relationships in the original document. After you train the model, you can test, retrain, and eventually use it to reliably extract data from more forms according to your needs.
 
 > [!NOTE]
 > You can also train models with a graphical user interface such as the [Document Intelligence Sample Labeling tool](../../../label-tool.md).
diff --git a/articles/ai-services/document-intelligence/how-to-guides/includes/v3-0/python-sdk.md b/articles/ai-services/document-intelligence/how-to-guides/includes/v3-0/python-sdk.md
@@ -112,7 +112,7 @@ def format_polygon(polygon):
 
 
 def analyze_read():
-    # sample form document
+    # sample document
     formUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/read.png"
 
     document_analysis_client = DocumentAnalysisClient(
@@ -191,7 +191,7 @@ def format_polygon(polygon):
 
 
 def analyze_layout():
-    # sample form document
+    # sample document
     formUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/layout.png"
 
     document_analysis_client = DocumentAnalysisClient(
@@ -441,7 +441,7 @@ def format_address_value(address_value):
 
 
 def analyze_tax_us_w2():
-    # sample form document
+    # sample document
     formUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/w2.png"
 
     document_analysis_client = DocumentAnalysisClient(
diff --git a/articles/ai-services/document-intelligence/overview.md b/articles/ai-services/document-intelligence/overview.md
@@ -23,7 +23,11 @@ monikerRange: '<=doc-intel-3.1.0'
 > [!NOTE]
 > Form Recognizer is now **Azure AI Document Intelligence**!
 >
-> As of July 2023, Azure AI services encompass all of what were previously known as Cognitive Services and Azure Applied AI Services. There are no changes to pricing. The names *Cognitive Services* and *Azure Applied AI* continue to be used in Azure billing, cost analysis, price list, and price APIs. There are no breaking changes to application programming interfaces (APIs) or SDKs.
+> * As of July 2023, Azure AI services encompass all of what were previously known as Cognitive Services and Azure Applied AI Services.
+> * There are no changes to pricing.
+> * The names *Cognitive Services* and *Azure Applied AI* continue to be used in Azure billing, cost analysis, price list, and price APIs.
+> * There are no breaking changes to application programming interfaces (APIs) or SDKs.
+> * Some platforms are still awaiting the renaming update. All mention of Form Recognizer or Document Intelligence in our documentation refers to the same Azure service.
 
 ::: moniker range=">=doc-intel-3.0.0"
  [!INCLUDE [applies to v3.1, v3.0, and v2.1](includes/applies-to-v3-1-v3-0-v2-1.md)]
diff --git a/articles/ai-services/document-intelligence/quickstarts/get-started-sdks-rest-api.md b/articles/ai-services/document-intelligence/quickstarts/get-started-sdks-rest-api.md
@@ -20,11 +20,20 @@ monikerRange: '<=doc-intel-3.1.0'
 [!INCLUDE [applies to v3.1 and v3.0](../includes/applies-to-v3-1-v3-0.md)]
 
 > [!IMPORTANT]
-> Azure Cognitive Services Form Recognizer is now Azure AI Document Intelligence. Some platforms are still awaiting the renaming update. All mention of Form Recognizer or Document Intelligence in our documentation refers to the same Azure service.
+>
+> * Azure Cognitive Services Form Recognizer is now Azure AI Document Intelligence.
+> * Some platforms are still awaiting the renaming update.
+> * All mention of Form Recognizer or Document Intelligence in our documentation refers to the same Azure service.
 
 ::: moniker range="doc-intel-3.1.0"
 
-Get started with Azure AI Document Intelligence latest GA version (v3.1). Azure AI Document Intelligence is a cloud-based Azure AI service that uses machine learning to extract key-value pairs, text, tables and key data from your documents. You can easily integrate Document Intelligence models into your workflows and applications by using an SDK in the programming language of your choice or calling the REST API.  For this quickstart, we recommend that you use the free service while you're learning the technology. Remember that the number of free pages is limited to 500 per month.
+* Get started with Azure AI Document Intelligence latest GA version (v3.1).
+
+* Azure AI Document Intelligence is a cloud-based Azure AI service that uses machine learning to extract key-value pairs, text, tables and key data from your documents.
+
+* You can easily integrate Document Intelligence models into your workflows and applications by using an SDK in the programming language of your choice or calling the REST API.
+
+* For this quickstart, we recommend that you use the free service while you're learning the technology. Remember that the number of free pages is limited to 500 per month.
 
 To learn more about Document Intelligence features and development options, visit our [Overview](../overview.md) page.
 
@@ -86,7 +95,7 @@ To learn more about Document Intelligence features and development options, visi
 
 That's it, congratulations!
 
-In this quickstart, you used a form Document Intelligence model to analyze various forms and documents. Next, explore the Document Intelligence Studio and reference documentation to learn about Document Intelligence API in depth.
+In this quickstart, you used a document Intelligence model to analyze various forms and documents. Next, explore the Document Intelligence Studio and reference documentation to learn about Document Intelligence API in depth.
 
 ## Next steps
 
diff --git a/articles/ai-services/document-intelligence/quickstarts/includes/v2-1/csharp.md b/articles/ai-services/document-intelligence/quickstarts/includes/v2-1/csharp.md
@@ -118,7 +118,7 @@ Extract text, selection marks, text styles, and table structures, along with the
 
 > [!div class="checklist"]
 >
-> * For this example, you'll need a **form document file at a URI**. You can use our [sample form document](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf) for this quickstart.
+> * For this example, you'll need a **document file at a URI**. You can use our [sample document](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf) for this quickstart.
 > * We've added the file URI value to the `formUri` variable.
 > * To extract the layout from a given file at a URI, use the `StartRecognizeContentFromUriAsync` method.
 
diff --git a/articles/ai-services/document-intelligence/quickstarts/includes/v2-1/java.md b/articles/ai-services/document-intelligence/quickstarts/includes/v2-1/java.md
@@ -119,7 +119,7 @@ Extract text, selection marks, text styles, and table structures, along with the
 
 > [!div class="checklist"]
 >
-> * For this example, you'll need a **form document file at a URI**. You can use our [sample form document](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf) for this quickstart.
+> * For this example, you'll need a **document file at a URI**. You can use our [sample document](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf) for this quickstart.
 > * To analyze a given file at a URI, you'll use the `beginRecognizeContentFromUrl` method.
 > * We've added the file URI value to the `formUrl` variable in the main method.
 
diff --git a/articles/ai-services/document-intelligence/quickstarts/includes/v2-1/javascript.md b/articles/ai-services/document-intelligence/quickstarts/includes/v2-1/javascript.md
@@ -94,7 +94,7 @@ In this quickstart, you use the following APIs to extract structured data from f
 
 > [!div class="checklist"]
 >
-> * For this example, you'll need a **form document file at a URI**. You can use our [sample form document](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf) for this quickstart.
+> * For this example, you'll need a **document file at a URI**. You can use our [sample document](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf) for this quickstart.
 > * We've added the file URI value to the `formUrl` variable near the top of the file.
 > * To analyze a given file at a URI, you'll use the `beginRecognizeContent` method.
 
diff --git a/articles/ai-services/document-intelligence/quickstarts/includes/v2-1/python.md b/articles/ai-services/document-intelligence/quickstarts/includes/v2-1/python.md
@@ -87,7 +87,7 @@ key = "YOUR_FORM_RECOGNIZER_KEY"
 
 > [!div class="checklist"]
 >
-> * For this example, you'll need a **form document file at a URI**. You can use our [sample form document](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf) for this quickstart.
+> * For this example, you'll need a **document file at a URI**. You can use our [sample document](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf) for this quickstart.
 > * We've added the file URI value to the `formUrl` variable near the top of the file.
 > * To analyze a given file at a URI, you'll use the `begin_recognize_content_from_url` method.
 
@@ -101,7 +101,7 @@ key = "YOUR_FORM_RECOGNIZER_KEY"
     return ", ".join(["[{}, {}]".format(p.x, p.y) for p in bounding_box])
 
  def recognize_content():
-    # sample form document
+    # sample document
     formUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf"
 
     form_recognizer_client = FormRecognizerClient(
diff --git a/articles/ai-services/document-intelligence/quickstarts/includes/v2-1/rest-api.md b/articles/ai-services/document-intelligence/quickstarts/includes/v2-1/rest-api.md
@@ -52,7 +52,7 @@ In this quickstart, you use the following APIs to extract structured data from f
 
 > [!div class="checklist"]
 >
-> * For this example, you'll need a **form document file at a URI**. You can use our [sample form document](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf) for this quickstart.
+> * For this example, you'll need a **document file at a URI**. You can use our [sample document](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf) for this quickstart.
 
 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.
diff --git a/articles/ai-services/document-intelligence/quickstarts/includes/v3-csharp-sdk.md b/articles/ai-services/document-intelligence/quickstarts/includes/v3-csharp-sdk.md
diff --git a/articles/ai-services/document-intelligence/quickstarts/includes/v3-java-sdk.md b/articles/ai-services/document-intelligence/quickstarts/includes/v3-java-sdk.md
diff --git a/articles/ai-services/document-intelligence/quickstarts/includes/v3-javascript-sdk.md b/articles/ai-services/document-intelligence/quickstarts/includes/v3-javascript-sdk.md
diff --git a/articles/ai-services/document-intelligence/quickstarts/includes/v3-python-sdk.md b/articles/ai-services/document-intelligence/quickstarts/includes/v3-python-sdk.md
diff --git a/articles/ai-services/document-intelligence/quickstarts/try-document-intelligence-studio.md b/articles/ai-services/document-intelligence/quickstarts/try-document-intelligence-studio.md
diff --git a/articles/ai-services/document-intelligence/quickstarts/try-sample-label-tool.md b/articles/ai-services/document-intelligence/quickstarts/try-sample-label-tool.md
diff --git a/articles/ai-services/document-intelligence/tutorial-azure-function.md b/articles/ai-services/document-intelligence/tutorial-azure-function.md
diff --git a/articles/ai-services/document-intelligence/whats-new.md b/articles/ai-services/document-intelligence/whats-new.md
