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Copy file name to clipboardExpand all lines: articles/ai-services/document-intelligence/authentication/create-sas-tokens.md
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author: laujan
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manager: nitinme
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ms.service: azure-ai-document-intelligence
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ms.date: 07/11/2024
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ms.date: 11/19/2024
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ms.author: lajanuar
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---
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[!INCLUDE [applies to v4.0 v3.1 v3.0 v2.1](../includes/applies-to-v40-v31-v30-v21.md)]
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::: moniker-end
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In this article, learn how to create user delegation, shared access signature (SAS) tokens, using the Azure portal or Azure Storage Explorer. User delegation SAS tokens are secured with Microsoft Entra credentials. SAS tokens provide secure, delegated access to resources in your Azure storage account.
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In this article, learn how to create user delegation, shared access signature (SAS) tokens, using either the Azure portal or Azure Storage Explorer. User delegation SAS tokens are secured with Microsoft Entra credentials. SAS tokens provide secure, delegated access to resources in your Azure storage account.
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:::image type="content" source="../media/sas-tokens/sas-url-token.png" alt-text="Screenshot of storage URI with SAS token appended.":::
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> [!NOTE]
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> 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).
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## Use the Azure portal
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## Generating SAS tokens
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Once the prerequisites are met and you upload your documents, you can now generate SAS tokens. There are two paths you can take from here; one using the Azure portal and the other using the Azure storage explorer. Select between the two following tabs for more information.
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### [**Azure Portal**](#tab/azure-portal)
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The Azure portal is a web-based console that enables you to manage your Azure subscription and resources using a graphical user interface (GUI).
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1. To [construct a SAS URL](#use-your-sas-url-to-grant-access), append the SAS token (URI) to the URL for a storage service.
Azure Storage Explorer is a free standalone app that enables you to easily manage your Azure cloud storage resources from your desktop.
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### Get started
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####Get started
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* You need the [**Azure Storage Explorer**](/azure/vs-azure-tools-storage-manage-with-storage-explorer) app installed in your Windows, macOS, or Linux development environment.
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* After the Azure Storage Explorer app is installed, [connect it the storage account](/azure/vs-azure-tools-storage-manage-with-storage-explorer?tabs=windows#connect-to-a-storage-account-or-service) you're using for Document Intelligence.
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### Create your SAS tokens
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####Create your SAS tokens
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1. Open the Azure Storage Explorer app on your local machine and navigate to your connected **Storage Accounts**.
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1. Expand the Storage Accounts node and select **Blob Containers**.
Copy file name to clipboardExpand all lines: articles/ai-services/document-intelligence/authentication/managed-identities-secured-access.md
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ms.service: azure-ai-document-intelligence
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ms.topic: how-to
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ms.date: 05/23/2024
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ms.date: 11/19/2024
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ms.author: vikurpad
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monikerRange: '<=doc-intel-4.0.0'
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* Select **Next: Resource**.
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:::image type="content" source="../media/managed-identities/v2-fr-private-end-basics.png" alt-text="Screenshot showing how to set-up a private endpoint.":::
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:::image type="content" source="../media/managed-identities/v2-fr-private-end-basics.png" alt-text="Screenshot showing how to setup a private endpoint.":::
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### Configure your virtual network
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:::image type="content" source="../media/managed-identities/cors-error.png" alt-text="Screenshot of error message when CORS config is required.":::
1. Make sure the client computer can access Document Intelligence resource and storage account, either they are in the same `VNET`, or client IP address is allowed in **Networking > Firewalls and virtual networks** setting page of both Document Intelligence resource and storage account.
Copy file name to clipboardExpand all lines: articles/ai-services/document-intelligence/concept/accuracy-confidence.md
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ms.service: azure-ai-document-intelligence
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ms.topic: conceptual
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ms.date: 11/19/2024
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> [!NOTE]
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> * Field level confidence is getting update to take into account word confidence score starting with **2024-07-31-preview** API version for **custom models**.
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> * Confidence scores for tables, table rows and table cells are available starting with the **2024-07-31-preview** API version for **custom models**.
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> * Field level confidence includes word confidence scores with **2024-11-30 (GA)** API version for **custom models**.
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> * Confidence scores for tables, table rows, and table cells are available starting with the **2024-11-30 (GA)** API version for **custom models**.
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Document Intelligence analysis results return an estimated confidence for predicted words, key-value pairs, selection marks, regions, and signatures. Currently, not all document fields return a confidence score.
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## Table, row, and cell confidence
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With the addition of table, row and cell confidence with the ```2024-02-29-preview``` API and onward, here are some common questions that should help with interpreting the table, row, and cell scores:
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Here are some common questions that should help with interpreting the table, row, and cell scores:
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**Q:** Is it possible to see a high confidence score for cells, but a low confidence score for the row?<br>
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*[`languages`](#language-detection)
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For `2024-07-31-preview` release and later, the Read model supports searchable PDF output:
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*[`Searchable PDF](#searchable-pdf)
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*[`Searchable PDF` support](#searchable-pdf)
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:::moniker-end
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*[`queryFields`](#query-fields)
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:::moniker range="doc-intel-4.0.0"
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*[`keyValuePairs`](#key-value-pairs)
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> [!NOTE]
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> * Not all add-on capabilities are supported by all models. For more information, *see*[model data extraction](../model-overview.md#model-analysis-features).
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> * Add-on capabilities are currently not supported for Microsoft Office file types.
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Document Intelligence supports optional features that can be enabled and disabled depending on the document extraction scenario. The following add-on capabilities are available for `2023-10-31-preview`, and later releases:
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*[`keyValuePairs`](#key-value-pairs)
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*[`queryFields`](#query-fields)
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> [!NOTE]
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> The query fields implementation in the 2023-10-30-preview API is different from the last preview release. The new implementation is less expensive and works well with structured documents.
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|Language detection|Free| ✔️| ✔️| n/a| n/a|
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|Key value pairs|Free| ✔️|n/a|n/a| n/a|
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|Query fields|Add-On*| ✔️|n/a|n/a| n/a|
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|Searhable pdf|Add-On**| ✔️|n/a|n/a| n/a|
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✱ Add-On - Query fields are priced differently than the other add-on features. See [pricing](https://azure.microsoft.com/pricing/details/ai-document-intelligence/) for details.
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✱ Add-On - Query fields are priced differently than the other add-on features. See [pricing](https://azure.microsoft.com/pricing/details/ai-document-intelligence/) for details. </br>
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** Add-On - Searchable pdf is available only with Read model as an add-on feature.
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## Supported file formats
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> [!IMPORTANT]
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> * Currently, the searchable PDF capability is only supported by Read OCR model `prebuilt-read`. When using this feature, please specify the `modelId` as `prebuilt-read`, as other model types will return error for this preview version.
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> * Searchable PDF is included with the 2024-07-31-preview`prebuilt-read` model with no usage cost for general PDF consumption.
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> * Currently, the searchable PDF capability is only supported by Read OCR model `prebuilt-read`. When using this feature, please specify the `modelId` as `prebuilt-read`.
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> * Searchable PDF is included with the 2024-11-30 (GA)`prebuilt-read` model with no usage cost for general PDF consumption.
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### Use searchable PDF
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> [!NOTE]
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> Document Intelligence Studio query field extraction is currently available with the Layout and Prebuilt models `2024-02-29-preview``2023-10-31-preview` API and later releases except for the `US tax` models (W2, 1098s, and 1099s models).
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> Document Intelligence Studio query field extraction is currently available with the Layout and Prebuilt models `2024-11-30 (GA) API with the exception of the `US tax` models (W2, 1098s, and 1099s models).
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### Query field extraction
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For query field extraction, specify the fields you want to extract and Document Intelligence analyzes the document accordingly. Here's an example:
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* If you're processing a contract in the [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio/document), use the `2024-02-29-preview` or `2023-10-31-preview` versions:
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* If you're processing a contract in the [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio/document), use the **2024-11-30 (GA)** version:
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:::image type="content" source="../media/studio/query-fields.png" alt-text="Screenshot of the query fields button in Document Intelligence Studio.":::
Copy file name to clipboardExpand all lines: articles/ai-services/document-intelligence/concept/analyze-document-response.md
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ms.date: 11/19/2024
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ms.custom:
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**Layout tables differ from document fields extracted from tabular data**. Layout tables are extracted from tabular visual content in the document without considering the semantics of the content. In fact, some layout tables are designed purely for visual layout and don't always contain structured data. The method to extract structured data from documents with diverse visual layout, like itemized details of a receipt, generally requires significant post processing. It's essential to map the row or column headers to structured fields with normalized field names. Depending on the document type, use prebuilt models or train a custom model to extract such structured content. The resulting information is exposed as document fields. Such trained models can also handle tabular data without headers and structured data in nontabular forms, for example the work experience section of a resume.
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> [!NOTE]
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> Starting with *2024-07-31-preview*, the bounding regions for figures and tables cover only the core content and exclude associated caption and footnotes.
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> The bounding regions for figures and tables cover only the core content and exclude associated caption and footnotes.
Azure AI Document Intelligence supports a wide variety of models that enable you to add intelligent document processing to your applications and optimize your workflows. Selecting the right model is essential to ensure the success of your enterprise. In this article, we explore the available Document Intelligence models and provide guidance for how to choose the best solution for your projects.
|**US Unified Tax**|You want to extract key information across all tax forms of W2, 1040, 1090, 1098 from a single file without running any custom classification of your own.|[**US Unified tax model**](../prebuilt/tax-document.md)|
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|**US Tax W-2 tax**|You want to extract key information such as salary, wages, and taxes withheld.|[**US tax W-2 model**](../prebuilt/tax-document.md)|
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|**US Tax W-4 tax**|You want to extract key information such as claim adjustments, personal information.|[**US tax W-4 model**](../prebuilt/tax-document.md)|
|**US Tax 1098**|You want to extract mortgage interest details such as principal, points, and tax.|[**US tax 1098 model**](../prebuilt/tax-document.md)|
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|**US Tax 1098-E**|You want to extract student loan interest details such as lender and interest amount.|[**US tax 1098-E model**](../prebuilt/tax-document.md)|
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|**US Tax 1098T**|You want to extract qualified tuition details such as scholarship adjustments, student status, and lender information.|[**US tax 1098-T model**](../prebuilt/tax-document.md)|
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|**Structured, consistent, documents with a static layout**. |Structured forms such as questionnaires or applications. |[**Custom template model**](./../train/custom-template.md)|
|**Unstructured documents, documents with varying templates**.|● Unstructured documents like contracts or letters</br> ● Varying document templates like loan statements from different mortgage companies|[**Custom generative model**](../train/custom-generative-extraction.md)|
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|**A collection of several models each trained on similar-type documents.**|● Supply purchase orders</br>● Equipment purchase orders</br>● Furniture purchase orders</br> **All composed into a single model**.|[**Composed custom model**](../train/composed-models.md)|
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## Custom classification model
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## Next steps
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*[Learn how to process your own forms and documents](../quickstarts/try-document-intelligence-studio.md) with the [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio)
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*[Learn how to process your own forms and documents](../studio-overview.md) with the [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio)
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