@@ -5,8 +5,6 @@ ms.topic: how-to
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.date: 07/11/2024
 ms.author: lajanuar
 ---
@@ -15,12 +13,12 @@ ms.author: lajanuar
 # Create SAS tokens for storage containers
 
 ::: moniker range="<=doc-intel-4.0.0"
- [!INCLUDE [applies to v4.0 v3.1 v3.0 v2.1](includes/applies-to-v40-v31-v30-v21.md)]
+ [!INCLUDE [applies to v4.0 v3.1 v3.0 v2.1](../includes/applies-to-v40-v31-v30-v21.md)]
 ::: moniker-end
 
- 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.
+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.
 
-:::image type="content" source="media/sas-tokens/sas-url-token.png" alt-text="Screenshot of storage URI with SAS token appended.":::
+  :::image type="content" source="../media/sas-tokens/sas-url-token.png" alt-text="Screenshot of storage URI with SAS token appended.":::
 
 At a high level, here's how SAS tokens work:
 
@@ -68,20 +66,20 @@ To get started, you need:
 1. Sign in to the [Azure portal](https://portal.azure.com).
     * Select **Your storage account** → **Data storage** → **Containers**.
 
-   :::image type="content" source="media/sas-tokens/data-storage-menu.png" alt-text="Screenshot that shows the Data storage menu in the Azure portal.":::
+   :::image type="content" source="../media/sas-tokens/data-storage-menu.png" alt-text="Screenshot that shows the Data storage menu in the Azure portal.":::
 
 1. Select a container from the list.
 
 1. Select **Upload** from the menu at the top of the page.
 
-    :::image type="content" source="media/sas-tokens/container-upload-button.png" alt-text="Screenshot that shows the container Upload button in the Azure portal.":::
+    :::image type="content" source="../media/sas-tokens/container-upload-button.png" alt-text="Screenshot that shows the container Upload button in the Azure portal.":::
 
 1. The **Upload blob** window appears. Select your files to upload.
 
-    :::image type="content" source="media/sas-tokens/upload-blob-window.png" alt-text="Screenshot that shows the Upload blob window in the Azure portal.":::
+    :::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 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
 
@@ -99,13 +97,13 @@ The Azure portal is a web-based console that enables you to manage your Azure su
 
    * Make sure the **Read**, **Write**, **Delete**, and **List** permissions are selected.
 
-    :::image type="content" source="media/sas-tokens/sas-permissions.png" alt-text="Screenshot that shows the SAS permission fields in the Azure portal.":::
+    :::image type="content" source="../media/sas-tokens/sas-permissions.png" alt-text="Screenshot that shows the SAS permission fields in the Azure portal.":::
 
     >[!IMPORTANT]
     >
     > * If you receive a message similar to the following one, you'll also need to assign access to the blob data in your storage account:
     >
-    >     :::image type="content" source="media/sas-tokens/need-permissions.png" alt-text="Screenshot that shows the lack of permissions warning.":::
+    >     :::image type="content" source="../media/sas-tokens/need-permissions.png" alt-text="Screenshot that shows the lack of permissions warning.":::
     >
     > * [Azure role-based access control](/azure/role-based-access-control/overview) (Azure RBAC) is the authorization system used to manage access to Azure resources. Azure RBAC helps you manage access and permissions for your Azure resources.
     > * [Assign an Azure role for access to blob data](/azure/role-based-access-control/role-assignments-portal?tabs=current) to assign a role that allows for read, write, and delete permissions for your Azure storage container. *See* [Storage Blob Data Contributor](/azure/role-based-access-control/built-in-roles#storage-blob-data-contributor).
@@ -179,4 +177,4 @@ That's it! You learned how to create SAS tokens to authorize how clients access
 ## Next step
 
 > [!div class="nextstepaction"]
-> [Build a training data set](how-to-guides/build-a-custom-model.md?view=doc-intel-2.1.0&preserve-view=true)
+> [Build a training data set](../how-to-guides/build-a-custom-model.md?view=doc-intel-2.1.0&preserve-view=true)

@@ -7,17 +7,13 @@ manager: venkyv
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
 ms.date: 11/15/2023
-ms.author: egeaney
-ms.custom:
-  - applied-ai-non-critical-form
-  - ignite-2023
 monikerRange: '<=doc-intel-4.0.0'
 ---
 
 
 # Document Intelligence encryption of data at rest
 
-[!INCLUDE [applies to v4.0, v3.1, v3.0, and v2.1](includes/applies-to-v40-v31-v30-v21.md)]
+[!INCLUDE [applies to v4.0, v3.1, v3.0, and v2.1](../includes/applies-to-v40-v31-v30-v21.md)]
 
 > [!IMPORTANT]
 >
@@ -27,12 +23,12 @@ monikerRange: '<=doc-intel-4.0.0'
 
 Azure AI Document Intelligence automatically encrypts your data when persisting it to the cloud. Document Intelligence encryption protects your data to help you to meet your organizational security and compliance commitments.
 
-[!INCLUDE [cognitive-services-about-encryption](../../ai-services/includes/cognitive-services-about-encryption.md)]
+[!INCLUDE [cognitive-services-about-encryption](../../../ai-services/includes/cognitive-services-about-encryption.md)]
 
 > [!IMPORTANT]
 > Customer-managed keys are only available resources created after 11 May, 2020. To use CMK with Document Intelligence, you will need to create a new Document Intelligence resource. Once the resource is created, you can use Azure Key Vault to set up your managed identity.
 
-[!INCLUDE [cognitive-services-cmk](../../ai-services/includes/configure-customer-managed-keys.md)]
+[!INCLUDE [cognitive-services-cmk](../../../ai-services/includes/configure-customer-managed-keys.md)]
 
 ## Next steps
 

@@ -5,8 +5,6 @@ description: Learn how to configure secure communications between Document Intel
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: how-to
 ms.date: 05/23/2024
 ms.author: vikurpad
@@ -16,27 +14,27 @@ monikerRange: '<=doc-intel-4.0.0'
 
 # Configure secure access with managed identities and virtual networks
 
-[!INCLUDE [applies to v4.0, v3.1, v3.0, and v2.1](includes/applies-to-v40-v31-v30-v21.md)]
+[!INCLUDE [applies to v4.0, v3.1, v3.0, and v2.1](../includes/applies-to-v40-v31-v30-v21.md)]
 
 This how-to guide walks you through the process of enabling secure connections for your Document Intelligence resource. You can secure the following connections:
 
 * Communication between a client application within a Virtual Network (`VNET`) and your Document Intelligence Resource.
 
-* Communication between Document Intelligence Studio and your Document Intelligence resource.
+* Communication between Document Intelligence Studio and your Document Intelligence resource.F
 
 * Communication between your Document Intelligence resource and a storage account (needed when training a custom model).
 
  You're setting up your environment to secure the resources:
 
-  :::image type="content" source="media/managed-identities/secure-config-di.png" alt-text="Screenshot of secure configuration with managed identity and virtual networks.":::
+  :::image type="content" source="../media/managed-identities/secure-config-di.png" alt-text="Screenshot of secure configuration with managed identity and virtual networks.":::
 
 ## Prerequisites
 
 To get started, you need:
 
 * An active [**Azure account**](https://azure.microsoft.com/free/cognitive-services/)—if you don't have one, you can [**create a free account**](https://azure.microsoft.com/free/).
 
-* A [**Document Intelligence**](https://portal.azure.com/#create/Microsoft.CognitiveServicesFormRecognizer) or [**Azure AI services**](https://portal.azure.com/#create/Microsoft.CognitiveServicesAIServices) resource in the Azure portal. For detailed steps, _see_ [Create an Azure AI services resource](../../ai-services/multi-service-resource.md?pivots=azportal).
+* A [**Document Intelligence**](https://portal.azure.com/#create/Microsoft.CognitiveServicesFormRecognizer) or [**Azure AI services**](https://portal.azure.com/#create/Microsoft.CognitiveServicesAIServices) resource in the Azure portal. For detailed steps, _see_ [Create an Azure AI services resource](../../../ai-services/multi-service-resource.md?pivots=azportal).
 
 * An [**Azure blob storage account**](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) in the same region as your Document Intelligence resource. Create containers to store and organize your blob data within your storage account.
 
@@ -64,7 +62,7 @@ Configure each of the resources to ensure that the resources can communicate wit
 
 You now have a working implementation of all the components needed to build a Document Intelligence solution with the default security model:
 
-  :::image type="content" source="media/managed-identities/default-config-di.png" alt-text="Screenshot of default security configuration.":::
+  :::image type="content" source="../media/managed-identities/default-config-di.png" alt-text="Screenshot of default security configuration.":::
 
 Next, complete the following steps:
 
@@ -84,7 +82,7 @@ Next, complete the following steps:
 
 Navigate to the Document Intelligence resource in the Azure portal and select the **Identity** tab. Toggle the **System assigned** managed identity to **On** and save the changes:
 
-  :::image type="content" source="media/managed-identities/v2-fr-mi.png" alt-text="Screenshot of configure managed identity.":::
+  :::image type="content" source="../media/managed-identities/v2-fr-mi.png" alt-text="Screenshot of configure managed identity.":::
 
 ## Secure the Storage account
 
@@ -96,7 +94,7 @@ Start configuring secure communications by navigating to the **Networking** tab
 
 1. **Save** your changes.
 
-  :::image type="content" source="media/managed-identities/v2-stg-firewall.png" alt-text="Screenshot of configure storage firewall.":::
+  :::image type="content" source="../media/managed-identities/v2-stg-firewall.png" alt-text="Screenshot of configure storage firewall.":::
 
 > [!NOTE]
 >
@@ -112,11 +110,11 @@ To ensure that the Document Intelligence resource can access the training datase
 
 1. Select the **Add role assignment** button.
 
-    :::image type="content" source="media/managed-identities/v2-stg-role-assign-role.png" alt-text="Screenshot of add role assignment window.":::
+    :::image type="content" source="../media/managed-identities/v2-stg-role-assign-role.png" alt-text="Screenshot of add role assignment window.":::
 
 1. On the **Role** tab, search for and select the **Storage Blob Data Contributor** permission and select **Next**.
 
-    :::image type="content" source="media/managed-identities/v2-stg-role-assignment.png" alt-text="Screenshot of choose a role tab.":::
+    :::image type="content" source="../media/managed-identities/v2-stg-role-assignment.png" alt-text="Screenshot of choose a role tab.":::
 
 1. On the **Members** tab, select the **Managed identity** option and choose **+ Select members**
 
@@ -128,7 +126,7 @@ To ensure that the Document Intelligence resource can access the training datase
 
    * **Select**. Choose the Document Intelligence resource you enabled with a managed identity.
 
-    :::image type="content" source="media/managed-identities/v2-stg-role-assign-resource.png" alt-text="Screenshot of managed identities dialog window.":::
+    :::image type="content" source="../media/managed-identities/v2-stg-role-assign-resource.png" alt-text="Screenshot of managed identities dialog window.":::
 
 1. **Close** the dialog window.
 
@@ -167,7 +165,7 @@ Next, configure the virtual network to ensure only resources within the virtual
 >
 >If you try accessing any of the Document Intelligence Studio features, you'll see an access denied message. To enable access from the Studio on your machine, select the **Add your client IP address** checkbox and **Save** to restore access.
 
-  :::image type="content" source="media/managed-identities/v2-fr-network.png" alt-text="Screenshot showing how to disable public access to Document Intelligence.":::
+  :::image type="content" source="../media/managed-identities/v2-fr-network.png" alt-text="Screenshot showing how to disable public access to Document Intelligence.":::
 
 ### Configure your private endpoint
 
@@ -185,7 +183,7 @@ Next, configure the virtual network to ensure only resources within the virtual
 
     * Select **Next: Resource**.
 
-    :::image type="content" source="media/managed-identities/v2-fr-private-end-basics.png" alt-text="Screenshot showing how to set-up a private endpoint":::
+    :::image type="content" source="../media/managed-identities/v2-fr-private-end-basics.png" alt-text="Screenshot showing how to set-up a private endpoint.":::
 
 ### Configure your virtual network
 
@@ -199,7 +197,7 @@ Next, configure the virtual network to ensure only resources within the virtual
 
 1. Accept the default value **Yes** to **integrate with private DNS zone**.
 
-    :::image type="content" source="media/managed-identities/v2-fr-private-end-vnet.png" alt-text="Screenshot showing how to configure private endpoint":::
+    :::image type="content" source="../media/managed-identities/v2-fr-private-end-vnet.png" alt-text="Screenshot showing how to configure private endpoint.":::
 
 1. Accept the remaining defaults and select **Next: Tags**.
 
@@ -221,13 +219,13 @@ Navigate to your **storage account** on the Azure portal.
 
 1. Select **Next: Resource**.
 
-    :::image type="content" source="media/managed-identities/v2-stg-private-end-basics.png" alt-text="Screenshot showing how to create a private endpoint":::
+    :::image type="content" source="../media/managed-identities/v2-stg-private-end-basics.png" alt-text="Screenshot showing how to create a private endpoint.":::
 
 1. On the resource tab, select **blob** from the **Target sub-resource** list.
 
 1. select **Next: Virtual Network**.
 
-   :::image type="content" source="media/managed-identities/v2-stg-private-end-resource.png" alt-text="Screenshot showing how to configure a private endpoint for a blob.":::
+   :::image type="content" source="../media/managed-identities/v2-stg-private-end-resource.png" alt-text="Screenshot showing how to configure a private endpoint for a blob.":::
 
 1. Select the **Virtual network** and **Subnet**. Make sure **Enable network policies for all private endpoints in this subnet** is selected and the **Dynamically allocate IP address** is enabled.
 
@@ -262,28 +260,28 @@ That's it! You can now configure secure access for your Document Intelligence re
 
 * **Failed to access Blob container**:
 
-   :::image type="content" source="media/managed-identities/cors-error.png" alt-text="Screenshot of error message when CORS config is required":::
+   :::image type="content" source="../media/managed-identities/cors-error.png" alt-text="Screenshot of error message when CORS config is required.":::
 
   **Resolution**:
-    1. [Configure CORS](quickstarts/try-document-intelligence-studio.md#configure-cors).
+    1. [Configure CORS](../quickstarts/try-document-intelligence-studio.md#configure-cors).
  
     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.
 
 * **AuthorizationFailure**:
 
-  :::image type="content" source="media/managed-identities/auth-failure.png" alt-text="Screenshot of authorization failure error.":::
+  :::image type="content" source="../media/managed-identities/auth-failure.png" alt-text="Screenshot of authorization failure error.":::
 
   **Resolution**: 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.
 
 * **ContentSourceNotAccessible**:
 
-   :::image type="content" source="media/managed-identities/content-source-error.png" alt-text="Screenshot of content source not accessible error.":::
+   :::image type="content" source="../media/managed-identities/content-source-error.png" alt-text="Screenshot of content source not accessible error.":::
 
     **Resolution**: Make sure you grant your Document Intelligence managed identity the role of **Storage Blob Data Contributor** and enabled **Trusted services** access or **Resource instance** rules on the networking tab.
 
 * **AccessDenied**:
 
-  :::image type="content" source="media/managed-identities/access-denied.png" alt-text="Screenshot of an access denied error.":::
+  :::image type="content" source="../media/managed-identities/access-denied.png" alt-text="Screenshot of an access denied error.":::
 
   **Resolution**: 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.
 

@@ -1,12 +1,10 @@
 ---
-title: Create and use managed identities with Document Intelligence 
+title: Create and use managed identities with Document Intelligence
 titleSuffix: Azure AI services
 description: Understand how to create and  use managed identity with Document Intelligence.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: how-to
 ms.date: 05/23/2024
 ms.author: lajanuar
@@ -16,19 +14,19 @@ monikerRange: '<=doc-intel-4.0.0'
 
 # Managed identities for Document Intelligence
 
-[!INCLUDE [applies to v4.0, v3.1, v3.0, v2.1](includes/applies-to-v40-v31-v30-v21.md)]
+[!INCLUDE [applies to v4.0, v3.1, v3.0, v2.1](../includes/applies-to-v40-v31-v30-v21.md)]
 
 Managed identities for Azure resources are service principals that create a Microsoft Entra identity and specific permissions for Azure managed resources:
 
-:::image type="content" source="media/managed-identities/rbac-flow.png" alt-text="Screenshot of managed identity flow (RBAC).":::
+:::image type="content" source="../media/managed-identities/rbac-flow.png" alt-text="Screenshot of managed identity flow (RBAC).":::
 
 * Managed identities grant access to any resource that supports Microsoft Entra authentication, including your own applications. Unlike security keys and authentication tokens, managed identities eliminate the need for developers to manage credentials.
 
 * You can grant access to an Azure resource and assign an Azure role to a managed identity using [Azure role-based access control (Azure RBAC)](/azure/role-based-access-control/overview). There's no added cost to use managed identities in Azure.
 
 > [!IMPORTANT]
 >
-> * Managed identities eliminate the need for you to manage credentials, including Shared Access Signature (SAS) tokens. 
+> * Managed identities eliminate the need for you to manage credentials, including Shared Access Signature (SAS) tokens.
 >
 > * Managed identities are a safer way to grant access to data without having credentials in your code.
 
@@ -48,21 +46,21 @@ To get started, you need:
 
 * An active [**Azure account**](https://azure.microsoft.com/free/cognitive-services/)—if you don't have one, you can [**create a free account**](https://azure.microsoft.com/free/).
 
-* A [**Document Intelligence**](https://portal.azure.com/#create/Microsoft.CognitiveServicesTextTranslation) or [**Azure AI services**](https://portal.azure.com/#create/Microsoft.CognitiveServicesAIServices) resource in the Azure portal. For detailed steps, _see_ [Create an Azure AI services resource](../../ai-services/multi-service-resource.md?pivots=azportal).
+* A [**Document Intelligence**](https://portal.azure.com/#create/Microsoft.CognitiveServicesTextTranslation) or [**Azure AI services**](https://portal.azure.com/#create/Microsoft.CognitiveServicesAIServices) resource in the Azure portal. For detailed steps, _see_ [Create an Azure AI services resource](../../../ai-services/multi-service-resource.md?pivots=azportal).
 
 * An [**Azure blob storage account**](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) in the same region as your Document Intelligence resource. You also need to create containers to store and organize your blob data within your storage account.
 
   * If your storage account is behind a firewall, **you must enable the following configuration**: </br></br>
 
   * On your storage account page, select **Security + networking** → **Networking** from the left menu.
-    :::image type="content" source="media/managed-identities/security-and-networking-node.png" alt-text="Screenshot of security + networking tab.":::
+    :::image type="content" source="../media/managed-identities/security-and-networking-node.png" alt-text="Screenshot of security + networking tab.":::
 
   * In the main window, select **Allow access from selected networks**.
-  :::image type="content" source="media/managed-identities/firewalls-and-virtual-networks.png" alt-text="Screenshot of Selected networks radio button selected.":::
+  :::image type="content" source="../media/managed-identities/firewalls-and-virtual-networks.png" alt-text="Screenshot of Selected networks radio button selected.":::
 
   * On the selected networks page, navigate to the **Exceptions** category and make certain that the  [**`Allow Azure services on the trusted services list to access this storage account`**](/azure/storage/common/storage-network-security?tabs=azure-portal#manage-exceptions) checkbox is enabled.
 
-    :::image type="content" source="media/managed-identities/allow-trusted-services-checkbox-portal-view.png" alt-text="Screenshot of allow trusted services checkbox, portal view":::
+    :::image type="content" source="../media/managed-identities/allow-trusted-services-checkbox-portal-view.png" alt-text="Screenshot of allow trusted services checkbox, portal view.":::
 * A brief understanding of [**Azure role-based access control (Azure RBAC)**](/azure/role-based-access-control/role-assignments-portal) using the Azure portal.
 
 ## Managed identity assignments
@@ -87,7 +85,7 @@ In the following steps, we enable a system-assigned managed identity and grant D
 
 1. In the left rail, Select **Identity** from the **Resource Management** list:
 
-    :::image type="content" source="media/managed-identities/resource-management-identity-tab.png" alt-text="Screenshot of resource management identity tab in the Azure portal.":::
+    :::image type="content" source="../media/managed-identities/resource-management-identity-tab.png" alt-text="Screenshot of resource management identity tab in the Azure portal.":::
 
 1. In the main window, toggle the **System assigned Status** tab to **On**.
 
@@ -97,11 +95,11 @@ You need to grant Document Intelligence access to your storage account before it
 
 1. Under **Permissions** select **Azure role assignments**:
 
-    :::image type="content" source="media/managed-identities/enable-system-assigned-managed-identity-portal.png" alt-text="Screenshot of enable system-assigned managed identity in Azure portal.":::
+    :::image type="content" source="../media/managed-identities/enable-system-assigned-managed-identity-portal.png" alt-text="Screenshot of enable system-assigned managed identity in Azure portal.":::
 
 1. On the Azure role assignments page that opens, choose your subscription from the drop-down menu then select **&plus; Add role assignment**.
 
-    :::image type="content" source="media/managed-identities/azure-role-assignments-page-portal.png" alt-text="Screenshot of Azure role assignments page in the Azure portal.":::
+    :::image type="content" source="../media/managed-identities/azure-role-assignments-page-portal.png" alt-text="Screenshot of Azure role assignments page in the Azure portal.":::
 
     > [!NOTE]
     >
@@ -116,24 +114,25 @@ You need to grant Document Intelligence access to your storage account before it
     |**Resource**| **_The name of your storage resource_**|
     |**Role** | **_Storage Blob Data Reader_**—allows for read access to Azure Storage blob containers and data.|
 
-     :::image type="content" source="media/managed-identities/add-role-assignment-window.png" alt-text="Screenshot of add role assignments page in the Azure portal.":::
+     :::image type="content" source="../media/managed-identities/add-role-assignment-window.png" alt-text="Screenshot of add role assignments page in the Azure portal.":::
 
 1. After you receive the _Added Role assignment_ confirmation message, refresh the page to see the added role assignment.
 
-    :::image type="content" source="media/managed-identities/add-role-assignment-confirmation.png" alt-text="Screenshot of Added role assignment confirmation pop-up message.":::
+    :::image type="content" source="../media/managed-identities/add-role-assignment-confirmation.png" alt-text="Screenshot of Added role assignment confirmation pop-up message.":::
 
 1. If you don't see the change right away, wait and try refreshing the page once more. When you assign or remove role assignments, it can take up to 30 minutes for changes to take effect.
 
-    :::image type="content" source="media/managed-identities/assigned-roles-window.png" alt-text="Screenshot of Azure role assignments window.":::
+    :::image type="content" source="../media/managed-identities/assigned-roles-window.png" alt-text="Screenshot of Azure role assignments window.":::
 
  That's it! You completed the steps to enable a system-assigned managed identity. With managed identity and Azure RBAC, you granted Document Intelligence specific access rights to your storage resource without having to manage credentials such as SAS tokens.
 
 ### Other role assignments for Document Intelligence Studio
 
 If you're going to use Document Intelligence Studio and your storage account is configured with network restriction such as firewall or virtual network, another role, **Storage Blob Data Contributor**, needs to be assigned to your Document Intelligence service. Document Intelligence Studio requires this role to write blobs to your storage account when you perform Auto label, Human in the loop, or Project sharing/upgrade operations.
 
-  :::image type="content" source="media/managed-identities/blob-data-contributor-role.png" alt-text="Screenshot of assigning storage blob data contributor role.":::
+  :::image type="content" source="../media/managed-identities/blob-data-contributor-role.png" alt-text="Screenshot of assigning storage blob data contributor role.":::
 
 ## Next steps
+
 > [!div class="nextstepaction"]
 > [Configure secure access with managed identities and private endpoints](managed-identities-secured-access.md)

@@ -1,435 +0,0 @@
----
-title: Read model OCR data extraction - Document Intelligence 
-titleSuffix: Azure AI services
-description: Extract print and handwritten text from scanned and digital documents with Document Intelligence's Read OCR model.
-author: laujan
-manager: nitinme
-ms.service: azure-ai-document-intelligence
-ms.topic: conceptual
-ms.date: 08/07/2024
-ms.author: lajanuar
----
-
-
-# Document Intelligence read model
-
-::: moniker range="doc-intel-4.0.0"
-[!INCLUDE [preview-version-notice](includes/preview-notice.md)]
-
-**This content applies to:**![checkmark](media/yes-icon.png) **v4.0 (preview)** | **Previous versions:** ![blue-checkmark](media/blue-yes-icon.png) [**v3.1 (GA)**](?view=doc-intel-3.1.0&preserve-view=tru) ![blue-checkmark](media/blue-yes-icon.png) [**v3.0 (GA)**](?view=doc-intel-3.0.0&preserve-view=tru)
-::: moniker-end
-
-::: moniker range="doc-intel-3.1.0"
-**This content applies to:** ![checkmark](media/yes-icon.png) **v3.1 (GA)** | **Latest version:** ![purple-checkmark](media/purple-yes-icon.png) [**v4.0 (preview)**](?view=doc-intel-4.0.0&preserve-view=true) | **Previous versions:** ![blue-checkmark](media/blue-yes-icon.png) [**v3.0**](?view=doc-intel-3.0.0&preserve-view=true)
-::: moniker-end
-
-::: moniker range="doc-intel-3.0.0"
-**This content applies to:** ![checkmark](media/yes-icon.png) **v3.0 (GA)** | **Latest versions:** ![purple-checkmark](media/purple-yes-icon.png) [**v4.0 (preview)**](?view=doc-intel-4.0.0&preserve-view=true) ![purple-checkmark](media/purple-yes-icon.png) [**v3.1**](?view=doc-intel-3.1.0&preserve-view=true)
-::: moniker-end
-
-> [!NOTE]
->
-> For extracting text from external images like labels, street signs, and posters, use the [Azure AI Image Analysis v4.0 Read](../../ai-services/Computer-vision/concept-ocr.md) feature optimized for general, non-document images with a performance-enhanced synchronous API that makes it easier to embed OCR in your user experience scenarios.
->
-
-Document Intelligence Read Optical Character Recognition (OCR) model runs at a higher resolution than Azure AI Vision Read and extracts print and handwritten text from PDF documents and scanned images. It also includes support for extracting text from Microsoft Word, Excel, PowerPoint, and HTML documents. It detects paragraphs, text lines, words, locations, and languages. The Read model is the underlying OCR engine for other Document Intelligence prebuilt models like Layout, General Document, Invoice, Receipt, Identity (ID) document, Health insurance card, W2 in addition to custom models.
-
-## What is OCR for documents?
-
-Optical Character Recognition (OCR) for documents is optimized for large text-heavy documents in multiple file formats and global languages. It includes features like higher-resolution scanning of document images for better handling of smaller and dense text; paragraph detection; and fillable form management. OCR capabilities also include advanced scenarios like single character boxes and accurate extraction of key fields commonly found in invoices, receipts, and other prebuilt scenarios.
-
-## Development options
-
-::: moniker range="doc-intel-4.0.0"
-
-Document Intelligence v4.0 (2024-07-31-preview) supports the following tools, applications, and libraries:
-
-| Feature | Resources | Model ID |
-|----------|-------------|-----------|
-|**Read OCR model**|&bullet; [**Document Intelligence Studio**](https://documentintelligence.ai.azure.com)</br>&bullet;  [**REST API**](/rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-07-31-preview)&preserve-view=true)</br>&bullet;  [**C# SDK**](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-4.0.0&preserve-view=true)</br>&bullet;  [**Python SDK**](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-4.0.0&preserve-view=true)</br>&bullet;  [**Java SDK**](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-4.0.0&preserve-view=true)</br>&bullet;  [**JavaScript SDK**](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-4.0.0&preserve-view=true)|**prebuilt-read**|
-::: moniker-end
-
-::: moniker range="doc-intel-3.1.0"
-
-Document Intelligence v3.1 supports the following tools, applications, and libraries:
-
-| Feature | Resources | Model ID |
-|----------|-------------|-----------|
-|**Read OCR model**|&bullet; [**Document Intelligence Studio**](https://documentintelligence.ai.azure.com)</br>&bullet;  [**REST API**](/rest/api/aiservices/document-models/analyze-document?view=rest-aiservices-2023-07-31&preserve-view=true&tabs=HTTP)</br>&bullet;  [**C# SDK**](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.1.0&preserve-view=true)</br>&bullet;  [**Python SDK**](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.1.0&preserve-view=true)</br>&bullet;  [**Java SDK**](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.1.0&preserve-view=true)</br>&bullet;  [**JavaScript SDK**](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.1.0&preserve-view=true)|**prebuilt-read**|
-::: moniker-end
-
-::: moniker range="doc-intel-3.0.0"
-
-Document Intelligence v3.0 supports the following tools, applications, and libraries:
-
-| Feature | Resources | Model ID |
-|----------|-------------|-----------|
-|**Read OCR model**|&bullet; [**Document Intelligence Studio**](https://documentintelligence.ai.azure.com)</br>&bullet;  [**REST API**](/rest/api/aiservices/document-models/analyze-document?view=rest-aiservices-v3.0%20(2022-08-31)&preserve-view=true&tabs=HTTP)</br>&bullet;  [**C# SDK**](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true)</br>&bullet;  [**Python SDK**](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true)</br>&bullet;  [**Java SDK**](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true)</br>&bullet;  [**JavaScript SDK**](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true)|**prebuilt-read**|
-::: moniker-end
-
-## Input requirements
-
-[!INCLUDE [input requirements](./includes/input-requirements.md)]
-
-## Get started with Read model
-
-Try extracting text from forms and documents using the Document Intelligence Studio. You need the following assets:
-
-* An Azure subscription—you can [create one for free](https://azure.microsoft.com/free/cognitive-services/).
-
-* A [Document Intelligence instance](https://portal.azure.com/#create/Microsoft.CognitiveServicesFormRecognizer) in the Azure portal. You can use the free pricing tier (`F0`) to try the service. After your resource deploys, select **Go to resource** to get your key and endpoint.
-
- :::image type="content" source="media/containers/keys-and-endpoint.png" alt-text="Screenshot of keys and endpoint location in the Azure portal.":::
-
-> [!NOTE]
-> Currently, Document Intelligence Studio doesn't support Microsoft Word, Excel, PowerPoint, and HTML file formats.
-
-***Sample document processed with [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio/read)***
-
-:::image type="content" source="media/studio/form-recognizer-studio-read-v3p2-updated.png" alt-text="Screenshot of Read processing in Document Intelligence Studio.":::
-
-1. On the [Document Intelligence Studio home page](https://documentintelligence.ai.azure.com/studio), select **Read**.
-
-1. You can analyze the sample document or upload your own files.
-
-1. Select the **Run analysis** button and, if necessary, configure the **Analyze options**:
-
-    :::image type="content" source="media/studio/run-analysis-analyze-options.png" alt-text="Screenshot of Run analysis and Analyze options buttons in the Document Intelligence Studio.":::
-
-   > [!div class="nextstepaction"]
-   > [Try Document Intelligence Studio](https://documentintelligence.ai.azure.com/studio/read).
-
-## Supported languages and locales
-
-See our [Language Support—document analysis models](language-support-ocr.md) page for a complete list of supported languages.
-
-## Data extraction
-
-> [!NOTE]
-> Microsoft Word and HTML file are supported in v3.1 and later versions. Compared with PDF and images, below features are not supported:
->
-> * There are no angle, width/height and unit with each page object.
-> * For each object detected, there is no bounding polygon or bounding region.
-> * Page range (`pages`) is not supported as a parameter.
-> * No `lines` object.
-
-## Searchable PDF
-
-The searchable PDF capability enables you to convert an analog PDF, such as scanned-image PDF files, to a PDF with embedded text. The embedded text enables deep text search within the PDF's extracted content by overlaying the detected text entities on top of the image files. 
-
-  > [!IMPORTANT]
-  >
-  > * 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.
-  > * Searchable PDF is included with the 2024-07-31-preview `prebuilt-read` model with no additional cost for generating a searchable PDF output.
->   * Searchable PDF currently only supports PDF files as input. Support for other file types, such as image files, will be available later.  
-
-### Use searchable PDF
-
-To use searchable PDF, make a `POST` request using the `Analyze` operation and specify the output format as `pdf`:
-
-```bash
-
-POST /documentModels/prebuilt-read:analyze?output=pdf
-{...}
-202
-```
-
-Poll for completion of the `Analyze` operation. Once the operation is complete, issue a `GET` request to retrieve the PDF format of the `Analyze` operation results.
-
-Upon successful completion, the PDF can be retrieved and downloaded as `application/pdf`. This operation allows direct downloading of the embedded text form of PDF instead of Base64-encoded JSON.
-
-```bash
-
-// Monitor the operation until completion.
-GET /documentModels/prebuilt-read/analyzeResults/{resultId}
-200
-{...}
-
-// Upon successful completion, retrieve the PDF as application/pdf.
-GET /documentModels/prebuilt-read/analyzeResults/{resultId}/pdf
-200 OK
-Content-Type: application/pdf
-```
-
-### Pages
-
-The pages collection is a list of pages within the document. Each page is represented sequentially within the document and includes the orientation angle indicating if the page is rotated and the width and height (dimensions in pixels). The page units in the model output are computed as shown:
-
-|**File format**   | **Computed page unit**   | **Total pages**  |
-| --- | --- | --- |
-|Images (JPEG/JPG, PNG, BMP, HEIF) | Each image = 1 page unit | Total images  |
-|PDF | Each page in the PDF = 1 page unit | Total pages in the PDF |
-|TIFF | Each image in the TIFF = 1 page unit | Total images in the TIFF |
-|Word (DOCX)  | Up to 3,000 characters = 1 page unit, embedded or linked images not supported | Total pages of up to 3,000 characters each |
-|Excel (XLSX)  | Each worksheet = 1 page unit, embedded or linked images not supported | Total worksheets |
-|PowerPoint (PPTX) |  Each slide = 1 page unit, embedded or linked images not supported | Total slides |
-|HTML | Up to 3,000 characters = 1 page unit, embedded or linked images not supported | Total pages of up to 3,000 characters each |
-
-::: moniker range="doc-intel-2.1.0 || doc-intel-3.0.0"
-
-```json
-"pages": [
-    {
-        "pageNumber": 1,
-        "angle": 0,
-        "width": 915,
-        "height": 1190,
-        "unit": "pixel",
-        "words": [],
-        "lines": [],
-        "spans": []
-    }
-]
-```
-
-::: moniker-end
-
-::: moniker range="doc-intel-3.1.0"
-
-#### [Sample code](#tab/sample-code)
-```Python
-# Analyze pages.
-for page in result.pages:
-    print(f"----Analyzing document from page #{page.page_number}----")
-    print(
-        f"Page has width: {page.width} and height: {page.height}, measured with unit: {page.unit}"
-    )
-```
-> [!div class="nextstepaction"]
-> [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/v3.1(2023-07-31-GA)/Python(v3.1)/Read_model/sample_analyze_read.py)
-
-#### [Output](#tab/output)
-```json
-"pages": [
-    {
-        "pageNumber": 1,
-        "angle": 0,
-        "width": 915,
-        "height": 1190,
-        "unit": "pixel",
-        "words": [],
-        "lines": [],
-        "spans": []
-    }
-]
-```
----
-
-::: moniker-end
-
-::: moniker range="doc-intel-4.0.0"
-
-#### [Sample code](#tab/sample-code)
-
-```Python
-# Analyze pages.
-for page in result.pages:
-    print(f"----Analyzing document from page #{page.page_number}----")
-    print(f"Page has width: {page.width} and height: {page.height}, measured with unit: {page.unit}")
-```
-> [!div class="nextstepaction"]
-> [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Python(v4.0)/Read_model/sample_analyze_read.py)
-
-#### [Output](#tab/output)
-
-```json
-"pages": [
-    {
-        "pageNumber": 1,
-        "angle": 0,
-        "width": 915,
-        "height": 1190,
-        "unit": "pixel",
-        "words": [],
-        "lines": [],
-        "spans": []
-    }
-]
-```
----
-::: moniker-end
-
-### Select pages for text extraction
-
-For large multi-page PDF documents, use the `pages` query parameter to indicate specific page numbers or page ranges for text extraction.
-
-### Paragraphs
-
-The Read OCR model in Document Intelligence extracts all identified blocks of text in the `paragraphs` collection as a top level object under `analyzeResults`. Each entry in this collection represents a text block and includes the extracted text as`content` and the bounding `polygon` coordinates. The `span` information points to the text fragment within the top-level `content` property that contains the full text from the document.
-
-```json
-"paragraphs": [
-    {
-        "spans": [],
-        "boundingRegions": [],
-        "content": "While healthcare is still in the early stages of its Al journey, we are seeing pharmaceutical and other life sciences organizations making major investments in Al and related technologies.\" TOM LAWRY | National Director for Al, Health and Life Sciences | Microsoft"
-    }
-]
-```
-
-### Text, lines, and words
-
-The Read OCR model extracts print and handwritten style text as `lines` and `words`. The model outputs bounding `polygon` coordinates and `confidence` for the extracted words. The `styles` collection includes any handwritten style for lines if detected along with the spans pointing to the associated text. This feature applies to [supported handwritten languages](language-support.md).
-
-For Microsoft Word, Excel, PowerPoint, and HTML, Document Intelligence Read model v3.1 and later versions extracts all embedded text as is. Texts are extrated as words and paragraphs. Embedded images aren't supported.
-
-::: moniker range="doc-intel-2.1.0 || doc-intel-3.0.0"
-
-```json
-
-"words": [
-    {
-        "content": "While",
-        "polygon": [],
-        "confidence": 0.997,
-        "span": {}
-    },
-],
-"lines": [
-    {
-        "content": "While healthcare is still in the early stages of its Al journey, we",
-        "polygon": [],
-        "spans": [],
-    }
-]
-```
-
-::: moniker-end
-
-::: moniker range="doc-intel-3.1.0"
-
-#### [Sample code](#tab/sample-code)
-
-```Python
-# Analyze lines.
-for line_idx, line in enumerate(page.lines):
-    words = line.get_words()
-    print(
-        f"...Line # {line_idx} has {len(words)} words and text '{line.content}' within bounding polygon '{format_polygon(line.polygon)}'"
-    )
-
-    # Analyze words.
-    for word in words:
-        print(
-            f"......Word '{word.content}' has a confidence of {word.confidence}"
-        )
-```
-
-> [!div class="nextstepaction"]
-> [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/v3.1(2023-07-31-GA)/Python(v3.1)/Read_model/sample_analyze_read.py)
-
-#### [Output](#tab/output)
-
-```json
-"words": [
-    {
-        "content": "While",
-        "polygon": [],
-        "confidence": 0.997,
-        "span": {}
-    },
-],
-"lines": [
-    {
-        "content": "While healthcare is still in the early stages of its Al journey, we",
-        "polygon": [],
-        "spans": [],
-    }
-]
-```
-
----
-::: moniker-end
-
-::: moniker range="doc-intel-4.0.0"
-
-#### [Sample code](#tab/sample-code)
-
-```Python
-# Analyze lines.
-if page.lines:
-    for line_idx, line in enumerate(page.lines):
-        words = get_words(page, line)
-        print(
-            f"...Line # {line_idx} has {len(words)} words and text '{line.content}' within bounding polygon '{line.polygon}'"
-        )
-
-        # Analyze words.
-        for word in words:
-            print(f"......Word '{word.content}' has a confidence of {word.confidence}")
-```
-> [!div class="nextstepaction"]
-> [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Python(v4.0)/Read_model/sample_analyze_read.py)
-
-#### [Output](#tab/output)
-
-```json
-"words": [
-    {
-        "content": "While",
-        "polygon": [],
-        "confidence": 0.997,
-        "span": {}
-    },
-],
-"lines": [
-    {
-        "content": "While healthcare is still in the early stages of its Al journey, we",
-        "polygon": [],
-        "spans": [],
-    }
-]
-```
----
-::: moniker-end
-
-### Handwritten style for text lines
-
-The response includes classifying whether each text line is of handwriting style or not, along with a confidence score. For more information, *see* [handwritten language support](language-support-ocr.md). The following example shows an example JSON snippet.
-
-```json
-"styles": [
-{
-    "confidence": 0.95,
-    "spans": [
-    {
-        "offset": 509,
-        "length": 24
-    }
-    "isHandwritten": true
-    ]
-}
-```
-
-If you enabled the [font/style addon capability](concept-add-on-capabilities.md#font-property-extraction), you also get the font/style result as part of the `styles` object.
-
-## Next steps
-
-Complete a Document Intelligence quickstart:
-
-> [!div class="checklist"]
->
-> * [**REST API**](how-to-guides/use-sdk-rest-api.md?view=doc-intel-4.0.0&preserve-view=true)
-> * [**C# SDK**](how-to-guides/use-sdk-rest-api.md?view=doc-intel-4.0.0&preserve-view=true?pivots=programming-language-csharp)
-> * [**Python SDK**](how-to-guides/use-sdk-rest-api.md?view=doc-intel-4.0.0&preserve-view=true?pivots=programming-language-python)
-> * [**Java SDK**](how-to-guides/use-sdk-rest-api.md?view=doc-intel-4.0.0&preserve-view=true?pivots=programming-language-java)
-> * [**JavaScript**](how-to-guides/use-sdk-rest-api.md?view=doc-intel-4.0.0&preserve-view=true?pivots=programming-language-javascript)</li></ul>
-
-Explore our REST API:
-
-> [!div class="nextstepaction"]
-> [Document Intelligence API v4.0](/rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-07-31-preview)&preserve-view=true)
-
-::: moniker range="doc-intel-4.0.0"
-Find more samples on GitHub:
-> [!div class="nextstepaction"]
-> [Read model.](https://github.com/Azure-Samples/document-intelligence-code-samples/tree/main/Python(v4.0)/Read_model)
-
-::: moniker-end
-
-::: moniker range="doc-intel-3.1.0"
-Find more samples on GitHub:
-> [!div class="nextstepaction"]
-> [Read model.](https://github.com/Azure-Samples/document-intelligence-code-samples/tree/v3.1(2023-07-31-GA)/Python(v3.1)/Read_model)
-
-::: moniker-end

@@ -6,13 +6,13 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 08/07/2024
+ms.date: 10/03/2024
 ms.author: lajanuar
 ---
 
-# Interpret and improve model accuracy and analysis confidence scores
+<!-- markdownlint-disable MD033 -->
 
-[!INCLUDE [applies to v4.0, v3.1, v3.0, and v2.1](includes/applies-to-v40-v31-v30-v21.md)]
+# Interpret and improve model accuracy and analysis confidence scores
 
 A confidence score indicates probability by measuring the degree of statistical certainty that the extracted result is detected correctly. The estimated accuracy is calculated by running a few different combinations of the training data to predict the labeled values. In this article, learn to interpret accuracy and confidence scores and best practices for using those scores to improve accuracy and confidence results.
 
@@ -30,7 +30,7 @@ Field confidence indicates an estimated probability between 0 and 1 that the pre
 **Document Intelligence Studio** </br>
 **Analyzed invoice prebuilt-invoice model**
 
-:::image type="content" source="media/accuracy-confidence/confidence-scores.png" alt-text="confidence scores from Document Intelligence Studio":::
+:::image type="content" source="../media/accuracy-confidence/confidence-scores.png" alt-text="confidence scores from Document Intelligence Studio":::
 
 ## Improve confidence scores
 
@@ -48,17 +48,16 @@ After an analysis operation, review the JSON output. Examine the `confidence` va
 
 ## Accuracy scores for custom models
 
-
 > [!NOTE]
+>
 > * **Custom neural and generative models** do not provide accuracy scores during training.
 
-The output of a `build` (v3.0 and onward) or `train` (v2.1) custom model operation includes the estimated accuracy score. This score represents the model's ability to accurately predict the labeled value on a visually similar document. Accuracy is measured within a percentage value range from 0% (low) to 100% (high). It's best to target a score of 80% or higher. For more sensitive cases, like financial or medical records, we recommend a score of close to 100%. You can also add a human review stage to validate for more critical automation workflows. 
+The output of a `build` (v3.0 and onward) or `train` (v2.1) custom model operation includes the estimated accuracy score. This score represents the model's ability to accurately predict the labeled value on a visually similar document. Accuracy is measured within a percentage value range from 0% (low) to 100% (high). It's best to target a score of 80% or higher. For more sensitive cases, like financial or medical records, we recommend a score of close to 100%. You can also add a human review stage to validate for more critical automation workflows.
 
 **Document Intelligence Studio** </br>
 **Trained custom model (invoice)**
 
-:::image type="content" source="media/accuracy-confidence/accuracy-studio-results.png" alt-text="Trained custom model accuracy scores":::
-
+:::image type="content" source="../media/accuracy-confidence/accuracy-studio-results.png" alt-text="Trained custom model accuracy scores":::
 
 ## Interpret accuracy and confidence scores for custom models
 
@@ -78,7 +77,6 @@ The following table demonstrates how to interpret both the accuracy and confiden
 | Low | High | &bullet; This result is most unlikely.<br>&bullet; For low accuracy scores, add more labeled data or split visually distinct documents into multiple models. |
 | Low | Low| &bullet; Add more labeled data.<br>&bullet; Split visually distinct documents into multiple models.|
 
-
 ## Ensure high model accuracy for custom models
 
 Variances in the visual structure of your documents affect the accuracy of your model. Reported accuracy scores can be inconsistent when the analyzed documents differ from documents used in training. Keep in mind that a document set can look similar when viewed by humans but appear dissimilar to an AI model. To follow, is a list of the best practices for training models with the highest accuracy. Following these guidelines should produce a model with higher accuracy and confidence scores during analysis and reduce the number of documents flagged for human review.
@@ -89,7 +87,7 @@ Variances in the visual structure of your documents affect the accuracy of your
 
 * Separate visually distinct document types to train different models for custom template and neural models.
   * As a general rule, if you remove all user entered values and the documents look similar, you need to add more training data to the existing model.
-  * If the documents are dissimilar, split your training data into different folders and train a model for each variation. You can then [compose](how-to-guides/compose-custom-models.md?view=doc-intel-2.1.0&preserve-view=true#create-a-composed-model) the different variations into a single model.
+  * If the documents are dissimilar, split your training data into different folders and train a model for each variation. You can then [compose](../how-to-guides/compose-custom-models.md?view=doc-intel-2.1.0&preserve-view=true#create-a-composed-model) the different variations into a single model.
 
 * Ensure that you don't have any extraneous labels.
 
@@ -128,9 +126,9 @@ With the addition of table, row and cell confidence with the ```2024-02-29-previ
 **A:** Look at all levels of table confidence starting in a top-to-bottom approach: begin by checking a table's confidence as a whole, then drill down to the row level and look at individual rows, finally look at cell-level confidences. Depending on the type of table, there are a couple of things of note:
 
 For **fixed tables**, cell-level confidence already captures quite a bit of information on the correctness of things. This means that simply going over each cell and looking at its confidence can be enough to help determine the quality of the prediction.
-For **dynamic tables**, the levels are meant to build on top of each other, so the top-to-bottom approach is more important. 
+For **dynamic tables**, the levels are meant to build on top of each other, so the top-to-bottom approach is more important.
 
 ## Next step
 
 > [!div class="nextstepaction"]
-> [Learn more about custom models](concept-custom.md)
+> [Learn more about custom models](../train/custom-model.md)

@@ -5,31 +5,31 @@ description: How to increase service limit capacity with add-on capabilities.
 author: jaep3347
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: conceptual
 ms.date: 05/23/2024
 ms.author: lajanuar
 monikerRange: '>=doc-intel-3.1.0'
 ---
 
 <!-- markdownlint-disable MD033 -->
+<!-- markdownlint-disable MD024 -->
+<!-- markdownlint-disable MD051 -->
 
 # Document Intelligence add-on capabilities
 
 ::: moniker range="doc-intel-4.0.0"
-[!INCLUDE [preview-version-notice](includes/preview-notice.md)]
+[!INCLUDE [preview-version-notice](../includes/preview-notice.md)]
 
-**This content applies to:** ![checkmark](media/yes-icon.png) **v4.0 (preview)** | **Previous versions:** ![blue-checkmark](media/blue-yes-icon.png) [**v3.1 (GA)**](?view=doc-intel-3.1.0&preserve-view=tru)
+**This content applies to:** ![checkmark](../media/yes-icon.png) **v4.0 (preview)** | **Previous versions:** ![blue-checkmark](../media/blue-yes-icon.png) [**v3.1 (GA)**](?view=doc-intel-3.1.0&preserve-view=tru)
 :::moniker-end
 
 :::moniker range="doc-intel-3.1.0"
-**This content applies to:** ![checkmark](media/yes-icon.png) **v3.1 (GA)** | **Latest version:** ![purple-checkmark](media/purple-yes-icon.png) [**v4.0 (preview)**](?view=doc-intel-4.0.0&preserve-view=true)
+**This content applies to:** ![checkmark](../media/yes-icon.png) **v3.1 (GA)** | **Latest version:** ![purple-checkmark](../media/purple-yes-icon.png) [**v4.0 (preview)**](?view=doc-intel-4.0.0&preserve-view=true)
 :::moniker-end
 
 :::moniker range="doc-intel-3.1.0"
 > [!NOTE]
-> Add-on capabilities are available within all models except for the [Business card model](concept-business-card.md).
+> Add-on capabilities are available within all models except for the [Business card model](../prebuilt/business-card.md).
 :::moniker-end
 
 :::moniker range=">=doc-intel-3.1.0"
@@ -48,18 +48,17 @@ Document Intelligence supports more sophisticated and modular analysis capabilit
 
 * [`languages`](#language-detection)
 
-Starting with `2024-07-31-preview` release, the Read model supports searchable PDF output:
+For `2024-07-31-preview` release and later, the Read model supports searchable PDF output:
 
 * [`Searchable PDF](#searchable-pdf)
 
-
 :::moniker-end
 
 :::moniker range="doc-intel-4.0.0"
 
 > [!NOTE]
 >
-> * Not all add-on capabilities are supported by all models. For more information, *see* [model data extraction](concept-model-overview.md#model-analysis-features).
+> * Not all add-on capabilities are supported by all models. For more information, *see* [model data extraction](../model-overview.md#model-analysis-features).
 >
 > * Add-on capabilities are currently not supported for Microsoft Office file types.
 
@@ -104,11 +103,13 @@ The task of recognizing small text from large-size documents, like engineering d
 ::: moniker range="doc-intel-4.0.0"
 
 ### [REST API](#tab/rest-api)
+
 ```bash
 {your-resource-endpoint}.cognitiveservices.azure.com/documentintelligence/documentModels/prebuilt-layout:analyze?api-version=2024-02-29-preview&features=ocrHighResolution
 ```
 
 ### [Sample code](#tab/sample-code)
+
 ```Python
 # Analyze a document at a URL:
 formUrl = "https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Data/add-on/add-on-highres.png?raw=true"
@@ -157,12 +158,14 @@ if result.tables:
             print(f"...Cell[{cell.row_index}][{cell.column_index}] has text '{cell.content}'")
             if cell.bounding_regions:
                 for region in cell.bounding_regions:
-                    print(f"...content on page {region.page_number} is within bounding polygon '{region.polygon}'")   
+                    print(f"...content on page {region.page_number} is within bounding polygon '{region.polygon}'")
 ```
+
 > [!div class="nextstepaction"]
 > [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Python(v4.0)/Add-on_capabilities/sample_analyze_addon_highres.py)
 
 ### [Output](#tab/output)
+
 ```json
 "styles": [true],
 "pages": [
@@ -221,15 +224,20 @@ if result.tables:
 ]
 
 ```
+
 ---
 ::: moniker-end
 
-::: moniker range="doc-intel-3.1.0"
+:::moniker range="doc-intel-3.1.0"
+
 ### [REST API](#tab/rest-api)
+
 ```bash
 {your-resource-endpoint}.cognitiveservices.azure.com/formrecognizer/documentModels/prebuilt-layout:analyze?api-version=2023-07-31&features=ocrHighResolution
 ```
+
 ### [Sample code](#tab/sample-code)
+
 ```Python
 # Analyze a document at a URL:
 url = "(https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Data/add-on/add-on-highres.png?raw=true"
@@ -286,10 +294,12 @@ for table_idx, table in enumerate(result.tables):
                 f"...content on page {region.page_number} is within bounding polygon '{format_polygon(region.polygon)}'"
             )
 ```
+
 > [!div class="nextstepaction"]
 > [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/v3.1(2023-07-31-GA)/Python(v3.1)/Add-on_capabilities/sample_analyze_addon_highres.py)
 
 ### [Output](#tab/output)
+
 ```json
 "styles": [true],
 "pages": [
@@ -348,6 +358,7 @@ for table_idx, table in enumerate(result.tables):
 ]
 
 ```
+
 ---
 ::: moniker-end
 
@@ -361,18 +372,21 @@ The `ocr.formula` capability extracts all identified formulas, such as mathemati
 ::: moniker range="doc-intel-4.0.0"
 
 ### [REST API](#tab/rest-api)
+
 ```bash
 {your-resource-endpoint}.cognitiveservices.azure.com/documentintelligence/documentModels/prebuilt-layout:analyze?api-version=2024-02-29-preview&features=formulas
 ```
+
 ### [Sample code](#tab/sample-code)
+
 ```Python
 # Analyze a document at a URL:
 formUrl = "https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Data/add-on/layout-formulas.png?raw=true"
 poller = document_intelligence_client.begin_analyze_document(
     "prebuilt-layout",
     AnalyzeDocumentRequest(url_source=formUrl),
     features=[DocumentAnalysisFeature.FORMULAS],  # Specify which add-on capabilities to enable
-)       
+)
 result: AnalyzeResult = poller.result()
 
 # [START analyze_formulas]
@@ -382,7 +396,7 @@ for page in result.pages:
         inline_formulas = [f for f in page.formulas if f.kind == "inline"]
         display_formulas = [f for f in page.formulas if f.kind == "display"]
 
-        # To learn the detailed concept of "polygon" in the following content, visit: https://aka.ms/bounding-region 
+        # To learn the detailed concept of "polygon" in the following content, visit: https://aka.ms/bounding-region
         print(f"Detected {len(inline_formulas)} inline formulas.")
         for formula_idx, formula in enumerate(inline_formulas):
             print(f"- Inline #{formula_idx}: {formula.value}")
@@ -395,10 +409,12 @@ for page in result.pages:
             print(f"  Confidence: {formula.confidence}")
             print(f"  Bounding regions: {formula.polygon}")
 ```
+
 > [!div class="nextstepaction"]
 > [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Python(v4.0)/Add-on_capabilities/sample_analyze_addon_formulas.py)
 
 ### [Output](#tab/output)
+
 ```json
 "content": ":formula:",
  "pages": [
@@ -423,15 +439,19 @@ for page in result.pages:
    }
  ]
 ```
+
 ::: moniker-end
 
-::: moniker range="doc-intel-3.1.0"
+:::moniker range="doc-intel-3.1.0"
 
 ### [REST API](#tab/rest-api)
+
 ```bash
 {your-resource-endpoint}.cognitiveservices.azure.com/formrecognizer/documentModels/prebuilt-layout:analyze?api-version=2023-07-31&features=formulas
 ```
+
 ### [Sample code](#tab/sample-code)
+
 ```Python
 # Analyze a document at a URL:
 url = "https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Data/add-on/layout-formulas.png?raw=true"
@@ -458,10 +478,12 @@ for page in result.pages:
         print(f"  Confidence: {formula.confidence}")
         print(f"  Bounding regions: {format_polygon(formula.polygon)}")
 ```
+
 > [!div class="nextstepaction"]
 > [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/v3.1(2023-07-31-GA)/Python(v3.1)/Add-on_capabilities/sample_analyze_addon_formulas.py)
 
 ### [Output](#tab/output)
+
 ```json
  "content": ":formula:",
    "pages": [
@@ -486,6 +508,7 @@ for page in result.pages:
      }
    ]
 ```
+
 ---
 ::: moniker-end
 
@@ -502,14 +525,15 @@ The `ocr.font` capability extracts all font properties of text extracted in the
 ```
 
 ### [Sample code](#tab/sample-code)
+
 ```Python
 # Analyze a document at a URL:
 formUrl = "https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Data/receipt/receipt-with-tips.png?raw=true"
 poller = document_intelligence_client.begin_analyze_document(
     "prebuilt-layout",
     AnalyzeDocumentRequest(url_source=formUrl),
     features=[DocumentAnalysisFeature.STYLE_FONT]    # Specify which add-on capabilities to enable.
-)       
+)
 result: AnalyzeResult = poller.result()
 
 # [START analyze_fonts]
@@ -566,10 +590,12 @@ for font_background_color, styles in font_background_colors.items():
     print(f"- Font background color: '{font_background_color}'")
     print(f"  Text: '{get_styled_text(styles, result.content)}'")
 ```
+
 > [!div class="nextstepaction"]
 > [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Python(v4.0)/Add-on_capabilities/sample_analyze_addon_fonts.py)
 
 ### [Output](#tab/output)
+
 ```json
 "content": "Foo bar",
 "styles": [
@@ -605,17 +631,20 @@ for font_background_color, styles in font_background_colors.items():
    }
  ]
 ```
+
 ---
 ::: moniker-end
 
-::: moniker range="doc-intel-3.1.0"
+:::moniker range="doc-intel-3.1.0"
 
 ### [REST API](#tab/rest-api)
+
 ```bash
   {your-resource-endpoint}.cognitiveservices.azure.com/formrecognizer/documentModels/prebuilt-layout:analyze?api-version=2023-07-31&features=styleFont
 ```
 
 ### [Sample code](#tab/sample-code)
+
 ```Python
 # Analyze a document at a URL:
 url = "https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Data/receipt/receipt-with-tips.png?raw=true"
@@ -677,10 +706,12 @@ for font_background_color, styles in font_background_colors.items():
     print(f"- Font background color: '{font_background_color}'")
     print(f"  Text: '{get_styled_text(styles, result.content)}'")
 ```
+
 > [!div class="nextstepaction"]
 > [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/v3.1(2023-07-31-GA)/Python(v3.1)/Add-on_capabilities/sample_analyze_addon_fonts.py)
 
 ### [Output](#tab/output)
+
 ```json
 "content": "Foo bar",
 "styles": [
@@ -716,6 +747,7 @@ for font_background_color, styles in font_background_colors.items():
    }
  ]
 ```
+
 ---
 ::: moniker-end
 
@@ -727,34 +759,38 @@ The `ocr.barcode` capability extracts all identified barcodes in the `barcodes`
 
 | **Barcode Type**   | **Example**   |
 | --- | --- |
-| `QR Code` |:::image type="content" source="media/barcodes/qr-code.png" alt-text="Screenshot of the QR Code.":::|
-| `Code 39` |:::image type="content" source="media/barcodes/code-39.png" alt-text="Screenshot of the Code 39.":::|
-| `Code 93` |:::image type="content" source="media/barcodes/code-93.gif" alt-text="Screenshot of the Code 93.":::|
-| `Code 128` |:::image type="content" source="media/barcodes/code-128.png" alt-text="Screenshot of the Code 128.":::|
-| `UPC (UPC-A & UPC-E)` |:::image type="content" source="media/barcodes/upc.png" alt-text="Screenshot of the UPC.":::|
-| `PDF417` |:::image type="content" source="media/barcodes/pdf-417.png" alt-text="Screenshot of the PDF417.":::|
-| `EAN-8` |:::image type="content" source="media/barcodes/european-article-number-8.gif" alt-text="Screenshot of the European-article-number barcode ean-8.":::|
-| `EAN-13` |:::image type="content" source="media/barcodes/european-article-number-13.gif" alt-text="Screenshot of the European-article-number barcode ean-13.":::|
-| `Codabar` |:::image type="content" source="media/barcodes/codabar.png" alt-text="Screenshot of the Codabar.":::|
-| `Databar` |:::image type="content" source="media/barcodes/databar.png" alt-text="Screenshot of the Data bar.":::|
-| `Databar` Expanded |:::image type="content" source="media/barcodes/databar-expanded.gif" alt-text="Screenshot of the Data bar Expanded.":::|
-| `ITF` |:::image type="content" source="media/barcodes/interleaved-two-five.png" alt-text="Screenshot of the interleaved-two-of-five barcode (ITF).":::|
-| `Data Matrix` |:::image type="content" source="media/barcodes/datamatrix.gif" alt-text="Screenshot of the Data Matrix.":::|
+| `QR Code` |:::image type="content" source="../media/barcodes/qr-code.png" alt-text="Screenshot of the QR Code.":::|
+| `Code 39` |:::image type="content" source="../media/barcodes/code-39.png" alt-text="Screenshot of the Code 39.":::|
+| `Code 93` |:::image type="content" source="../media/barcodes/code-93.gif" alt-text="Screenshot of the Code 93.":::|
+| `Code 128` |:::image type="content" source="../media/barcodes/code-128.png" alt-text="Screenshot of the Code 128.":::|
+| `UPC (UPC-A & UPC-E)` |:::image type="content" source="../media/barcodes/upc.png" alt-text="Screenshot of the UPC.":::|
+| `PDF417` |:::image type="content" source="../media/barcodes/pdf-417.png" alt-text="Screenshot of the PDF417.":::|
+| `EAN-8` |:::image type="content" source="../media/barcodes/european-article-number-8.gif" alt-text="Screenshot of the European-article-number barcode ean-8.":::|
+| `EAN-13` |:::image type="content" source="../media/barcodes/european-article-number-13.gif" alt-text="Screenshot of the European-article-number barcode ean-13.":::|
+| `Codabar` |:::image type="content" source="../media/barcodes/codabar.png" alt-text="Screenshot of the Codabar.":::|
+| `Databar` |:::image type="content" source="../media/barcodes/databar.png" alt-text="Screenshot of the Data bar.":::|
+| `Databar` Expanded |:::image type="content" source="../media/barcodes/databar-expanded.gif" alt-text="Screenshot of the Data bar Expanded.":::|
+| `ITF` |:::image type="content" source="../media/barcodes/interleaved-two-five.png" alt-text="Screenshot of the interleaved-two-of-five barcode (ITF).":::|
+| `Data Matrix` |:::image type="content" source="../media/barcodes/datamatrix.gif" alt-text="Screenshot of the Data Matrix.":::|
 
 ::: moniker range="doc-intel-4.0.0"
+
 ### [REST API](#tab/rest-api)
+
 ```bash
 {your-resource-endpoint}.cognitiveservices.azure.com/documentintelligence/documentModels/prebuilt-layout:analyze?api-version=2024-02-29-preview&features=barcodes
 ```
+
 ### [Sample code](#tab/sample-code)
+
 ```Python
 # Analyze a document at a URL:
 formUrl = "https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Data/add-on/add-on-barcodes.jpg?raw=true"
 poller = document_intelligence_client.begin_analyze_document(
     "prebuilt-read",
     AnalyzeDocumentRequest(url_source=formUrl),
     features=[DocumentAnalysisFeature.BARCODES]    # Specify which add-on capabilities to enable.
-)       
+)
 result: AnalyzeResult = poller.result()
 
 # [START analyze_barcodes]
@@ -769,10 +805,12 @@ for page in result.pages:
             print(f"  Confidence: {barcode.confidence}")
             print(f"  Bounding regions: {barcode.polygon}")
 ```
+
 > [!div class="nextstepaction"]
 > [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Python(v4.0)/Add-on_capabilities/sample_analyze_addon_barcodes.py)
 
 ### [Output](#tab/output)
+
 ```json
 ----Barcodes detected from page #1----
 Detected 2 barcodes:
@@ -785,16 +823,20 @@ Detected 2 barcodes:
   Confidence: 0.98
   Bounding regions: [50.5, 60.5, 70.5, 80.5]
 ```
+
 ---
 :::moniker-end
 
 :::moniker range="doc-intel-3.1.0"
 
 ### [REST API](#tab/rest-api)
+
 ```bash
 {your-resource-endpoint}.cognitiveservices.azure.com/formrecognizer/documentModels/prebuilt-layout:analyze?api-version=2023-07-31&features=barcodes
 ```
+
 ### [Sample code](#tab/sample-code)
+
 ```Python
 # Analyze a document at a URL:
 url = "https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Data/add-on/add-on-barcodes.jpg?raw=true"
@@ -814,10 +856,12 @@ for page in result.pages:
         print(f"  Confidence: {barcode.confidence}")
         print(f"  Bounding regions: {format_polygon(barcode.polygon)}")
 ```
+
 > [!div class="nextstepaction"]
 > [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/v3.1(2023-07-31-GA)/Python(v3.1)/Add-on_capabilities/sample_analyze_addon_barcodes.py)
 
 ### [Output](#tab/output)
+
 ```json
 ----Barcodes detected from page #1----
 Detected 2 barcodes:
@@ -830,6 +874,7 @@ Detected 2 barcodes:
   Confidence: 0.98
   Bounding regions: [50.5, 60.5, 70.5, 80.5]
 ```
+
 ---
 ::: moniker-end
 
@@ -840,18 +885,21 @@ Adding the `languages` feature to the `analyzeResult` request predicts the detec
 ::: moniker range="doc-intel-4.0.0"
 
 ### [REST API](#tab/rest-api)
+
 ```bash
 {your-resource-endpoint}.cognitiveservices.azure.com/documentintelligence/documentModels/prebuilt-layout:analyze?api-version=2024-02-29-preview&features=languages
 ```
+
 ### [Sample code](#tab/sample-code)
+
 ```Python
 # Analyze a document at a URL:
 formUrl = "https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Data/add-on/add-on-fonts_and_languages.png?raw=true"
 poller = document_intelligence_client.begin_analyze_document(
     "prebuilt-layout",
     AnalyzeDocumentRequest(url_source=formUrl),
     features=[DocumentAnalysisFeature.LANGUAGES]     # Specify which add-on capabilities to enable.
-)       
+)
 result: AnalyzeResult = poller.result()
 
 # [START analyze_languages]
@@ -865,10 +913,12 @@ if result.languages:
             f"  Text: '{','.join([result.content[span.offset : span.offset + span.length] for span in lang.spans])}'"
         )
 ```
+
 > [!div class="nextstepaction"]
 > [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Python(v4.0)/Add-on_capabilities/sample_analyze_addon_languages.py)
 
 ### [Output](#tab/output)
+
 ```json
 "languages": [
     {
@@ -883,16 +933,20 @@ if result.languages:
     },
 ]
 ```
+
 ---
 :::moniker-end
 
 :::moniker range="doc-intel-3.1.0"
 
 ### [REST API](#tab/rest-api)
+
 ```bash
 {your-resource-endpoint}.cognitiveservices.azure.com/formrecognizer/documentModels/prebuilt-layout:analyze?api-version=2023-07-31&features=languages
 ```
+
 ### [Sample code](#tab/sample-code)
+
 ```Python
 # Analyze a document at a URL:
 url = "https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Data/add-on/add-on-fonts_and_languages.png?raw=true"
@@ -909,10 +963,12 @@ for lang_idx, lang in enumerate(result.languages):
     print(f"  Confidence: {lang.confidence}")
     print(f"  Text: '{','.join([result.content[span.offset : span.offset + span.length] for span in lang.spans])}'")
 ```
+
 > [!div class="nextstepaction"]
 > [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/v3.1(2023-07-31-GA)/Python(v3.1)/Add-on_capabilities/sample_analyze_addon_languages.py)
 
 ### [Output](#tab/output)
+
 ```json
 "languages": [
     {
@@ -927,14 +983,15 @@ for lang_idx, lang in enumerate(result.languages):
     },
 ]
 ```
+
 ---
 ::: moniker-end
 
 ::: moniker range="doc-intel-4.0.0"
 
 ## Searchable PDF
 
-The searchable PDF capability enables you to convert an analog PDF, such as scanned-image PDF files, to a PDF with embedded text. The embedded text enables deep text search within the PDF's extracted content by overlaying the detected text entities on top of the image files. 
+The searchable PDF capability enables you to convert an analog PDF, such as scanned-image PDF files, to a PDF with embedded text. The embedded text enables deep text search within the PDF's extracted content by overlaying the detected text entities on top of the image files.
 
   > [!IMPORTANT]
   >
@@ -969,10 +1026,9 @@ GET /documentModels/prebuilt-read/analyzeResults/{resultId}/pdf
 Content-Type: application/pdf
 ```
 
-
 ## Key-value Pairs
 
-In earlier API versions, the prebuilt-document model extracted key-value pairs from forms and documents. With the addition of the `keyValuePairs` feature to prebuilt-layout, the layout model now produces the same results. 
+In earlier API versions, the `prebuilt-document` model extracted key-value pairs from forms and documents. With the addition of the `keyValuePairs` feature to prebuilt-layout, the layout model now produces the same results.
 
 Key-value pairs are specific spans within the document that identify a label or key and its associated response or value. In a structured form, these pairs could be the label and the value the user entered for that field. In an unstructured document, they could be the date a contract was executed on based on the text in a paragraph. The AI model is trained to extract identifiable keys and values based on a wide variety of document types, formats, and structures.
 
@@ -1008,11 +1064,11 @@ For query field extraction, specify the fields you want to extract and Document
 
 * 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:
 
-    :::image type="content" source="media/studio/query-fields.png" alt-text="Screenshot of the query fields button in Document Intelligence Studio.":::
+    :::image type="content" source="../media/studio/query-fields.png" alt-text="Screenshot of the query fields button in Document Intelligence Studio.":::
 
 * You can pass a list of field labels like `Party1`, `Party2`, `TermsOfUse`, `PaymentTerms`, `PaymentDate`, and `TermEndDate` as part of the `analyze document` request.
 
-   :::image type="content" source="media/studio/query-field-select.png" alt-text="Screenshot of query fields selection window in Document Intelligence Studio.":::
+   :::image type="content" source="../media/studio/query-field-select.png" alt-text="Screenshot of query fields selection window in Document Intelligence Studio.":::
 
 * Document Intelligence is able to analyze and extract the field data and return the values in a structured JSON output.
 
@@ -1025,12 +1081,13 @@ For query field extraction, specify the fields you want to extract and Document
 ```
 
 ### [Sample code](#tab/sample-code)
+
 ```Python
 # Analyze a document at a URL:
 formUrl = "https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Data/invoice/simple-invoice.png?raw=true"
 poller = document_intelligence_client.begin_analyze_document(
     "prebuilt-layout",
-    AnalyzeDocumentRequest(url_source=formUrl),   
+    AnalyzeDocumentRequest(url_source=formUrl),
     features=[DocumentAnalysisFeature.QUERY_FIELDS],    # Specify which add-on capabilities to enable.
     query_fields=["Address", "InvoiceNumber"],  # Set the features and provide a comma-separated list of field names.
 )
@@ -1043,24 +1100,26 @@ if result.documents:
         if doc.fields and doc.fields["InvoiceNumber"]:
             print(f"Invoice number: {doc.fields['InvoiceNumber'].value_string}")
 ```
+
 > [!div class="nextstepaction"]
 > [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Python(v4.0)/Add-on_capabilities/sample_analyze_addon_query_fields.py)
 
 ### [Output](#tab/output)
+
 ```json
 Address: 1 Redmond way Suite 6000 Redmond, WA Sunnayvale, 99243
 Invoice number: 34278587
 ```
+
 ---
 
 ::: moniker-end
 
-
 ## Next steps
 
 > [!div class="nextstepaction"]
 > Learn more:
-> [**Read model**](concept-read.md) [**Layout model**](concept-layout.md)
+> [**Read model**](../prebuilt/read.md) [**Layout model**](../prebuilt/layout.md)
 
 > [!div class="nextstepaction"]
 > SDK samples:
@@ -1072,7 +1131,7 @@ Invoice number: 34278587
 > [**Add-on capabilities**](https://github.com/Azure-Samples/document-intelligence-code-samples/tree/main/Python(v4.0)/Add-on_capabilities)
 ::: moniker-end
 
-::: moniker range="doc-intel-3.1.0"
+:::moniker range="doc-intel-3.1.0"
 > [!div class="nextstepaction"]
 > Find more samples:
 > [**Add-on capabilities**](https://github.com/Azure-Samples/document-intelligence-code-samples/tree/v3.1(2023-07-31-GA)/Python(v3.1)/Add-on_capabilities)

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 09/26/2024
+ms.date: 10/03/2024
 ms.author: vikurpad
 ms.custom:
   - references_regions
@@ -15,13 +15,11 @@ monikerRange: '>=doc-intel-3.0.0'
 
 # Analyze document API response
 
-**This content applies to:** ![checkmark](media/yes-icon.png) **v4.0 (preview)** ![checkmark](media/yes-icon.png) **v3.1 (GA)** ![checkmark](media/yes-icon.png) **v3.0 (GA)**
-
 In this article, let's examine the different objects returned as part of the `AnalyzeDocument` response and how to use the document analysis API response in your applications.
 
 ## Analyze document request
 
-The Document Intelligence APIs analyze images, PDFs, and other document files to extract and detect various content, layout, style, and semantic elements. The `Analyze` operation is an async API. Submitting a document returns an **Operation-Location** header that contains the URL to poll for completion. When an analysis request completes successfully, the response contains the elements described in the [model data extraction](concept-model-overview.md#model-data-extraction).
+The Document Intelligence APIs analyze images, PDFs, and other document files to extract and detect various content, layout, style, and semantic elements. The `Analyze` operation is an async API. Submitting a document returns an **Operation-Location** header that contains the URL to poll for completion. When an analysis request completes successfully, the response contains the elements described in the [model data extraction](../model-overview.md#model-data-extraction).
 
 ### Response elements
 
@@ -56,21 +54,21 @@ The `Analyze` response for each API returns different objects. API responses con
 | **keyValuePairs**| Key-value pairs recognized by a pretrained model. The key is a span of text from the document with the associated value. | General document and Invoice models |
 | **documents**| Fields recognized are returned in the `fields` dictionary within the list of documents| Prebuilt models, Custom models.|
 
-For more information on the objects returned by each API, see [model data extraction](concept-model-overview.md#model-data-extraction).
+For more information on the objects returned by each API, see [model data extraction](../model-overview.md#model-data-extraction).
 
 ## Element properties
 
 ### Spans
 
-Spans specify the logical position of each element in the overall reading order, with each span specifying a character offset and length into the top-level content string property. By default, character offsets and lengths are returned in units of user-perceived characters (also known as [`grapheme clusters`](/dotnet/standard/base-types/character-encoding-introduction) or text elements). To accommodate different development environments that use different character units, user can specify the `stringIndexIndex` query parameter to return span offsets and lengths in Unicode code points (Python 3) or UTF16 code units (Java, JavaScript, .NET) as well. For more information, *see* [multilingual/emoji support](../../ai-services/language-service/concepts/multilingual-emoji-support.md).
+Spans specify the logical position of each element in the overall reading order, with each span specifying a character offset and length into the top-level content string property. By default, character offsets and lengths are returned in units of user-perceived characters (also known as [`grapheme clusters`](/dotnet/standard/base-types/character-encoding-introduction) or text elements). To accommodate different development environments that use different character units, user can specify the `stringIndexIndex` query parameter to return span offsets and lengths in Unicode code points (Python 3) or UTF16 code units (Java, JavaScript, .NET) as well. For more information, *see* [multilingual/emoji support](../../../ai-services/language-service/concepts/multilingual-emoji-support.md).
 
-:::image type="content" source="media/span.png" alt-text="Screenshot of detected span example.":::
+:::image type="content" source="../media/span.png" alt-text="Screenshot of detected span example.":::
 
 ### Bounding Region
 
 Bounding regions describe the visual position of each element in the file. When elements aren't visually contiguous or cross pages (tables), the positions of most elements are described via an array of bounding regions. Each region specifies the page number (`1`-indexed) and bounding polygon. The bounding polygon is described as a sequence of points, clockwise from the left relative to the natural orientation of the element. For quadrilaterals, plot points are top-left, top-right, bottom-right, and bottom-left corners. Each point represents its x, y coordinate in the page unit specified by the unit property. In general, unit of measure for images is pixels while PDFs use inches.
 
-:::image type="content" source="media/bounding-regions.png" alt-text="Screenshot of detected bounding regions example.":::
+:::image type="content" source="../media/bounding-regions.png" alt-text="Screenshot of detected bounding regions example.":::
 
 > [!NOTE]
 > Currently, Document Intelligence only returns 4-vertex quadrilaterals as bounding polygons. Future versions may return different number of points to describe more complex shapes, such as curved lines or non-rectangular images. Bounding regions applied only to rendered files, if the file is not rendered, bounding regions are not returned. Currently files of docx/xlsx/pptx/html format are not rendered.
@@ -81,28 +79,28 @@ Bounding regions describe the visual position of each element in the file. When
 
 A word is a content element composed of a sequence of characters. With Document Intelligence, a word is defined as a sequence of adjacent characters, with whitespace separating words from one another. For languages that don't use space separators between words each character is returned as a separate word, even if it doesn't represent a semantic word unit.
 
-:::image type="content" source="media/word-boundaries.png" alt-text="Screenshot of detected words example.":::
+:::image type="content" source="../media/word-boundaries.png" alt-text="Screenshot of detected words example.":::
 
 #### Selection marks
 
 A selection mark is a content element that represents a visual glyph indicating the state of a selection. Checkbox is a common form of selection marks. However, they're also represented via radio buttons or a boxed cell in a visual form. The state of a selection mark can be selected or unselected, with different visual representation to indicate the state.
 
-:::image type="content" source="media/selection-marks.png" alt-text="Screenshot of detected selection marks example.":::
+:::image type="content" source="../media/selection-marks.png" alt-text="Screenshot of detected selection marks example.":::
 
 ### Layout elements
 
 #### Line
 
 A line is an ordered sequence of consecutive content elements separated by a visual space, or ones that are immediately adjacent for languages without space delimiters between words. Content elements in the same horizontal plane (row) but separated by more than a single visual space are most often split into multiple lines. While this feature sometimes splits semantically contiguous content into separate lines, it enables the representation of textual content split into multiple columns or cells. Lines in vertical writing are detected in the vertical direction.
 
-:::image type="content" source="media/lines.png" alt-text="Screenshot of detected lines example.":::
+:::image type="content" source="../media/lines.png" alt-text="Screenshot of detected lines example.":::
 
 #### Paragraph
 
 A paragraph is an ordered sequence of lines that form a logical unit. Typically, the lines share common alignment and spacing between lines. Paragraphs are often delimited via indentation, added spacing, or bullets/numbering. Content can only be assigned to a single paragraph.
 Select paragraphs can also be associated with a functional role in the document. Currently supported roles include page header, page footer, page number, title, section heading, and footnote.
 
-:::image type="content" source="media/paragraph.png" alt-text="Screenshot of detected paragraphs example.":::
+:::image type="content" source="../media/paragraph.png" alt-text="Screenshot of detected paragraphs example.":::
 
 #### Page
 
@@ -134,7 +132,7 @@ Based on its position and styling, a cell can be classified as general content,
 > [!NOTE]
 > Starting with *2024-07-31-preview*, the bounding regions for figures and tables cover only the core content and exclude associated caption and footnotes.
 
-:::image type="content" source="media/table.png" alt-text="Layout table":::
+:::image type="content" source="../media/table.png" alt-text="Layout table":::
 
 #### Figures
 
@@ -169,7 +167,7 @@ When *output=figures* is specified during the initial `Analyze` operation, the s
 
 #### Sections
 
-Hierarchical document structure analysis is pivotal in organizing, comprehending, and processing extensive documents. This approach is vital for semantically segmenting long documents to boost comprehension, facilitate navigation, and improve information retrieval. The advent of [Retrieval Augmented Generation (RAG)](concept-retrieval-augmented-generation.md) in document generative AI underscores the significance of hierarchical document structure analysis. The Layout model supports sections and subsections in the output, which identifies the relationship of sections and object within each section. The hierarchical structure is maintained in `elements` of each section.
+Hierarchical document structure analysis is pivotal in organizing, comprehending, and processing extensive documents. This approach is vital for semantically segmenting long documents to boost comprehension, facilitate navigation, and improve information retrieval. The advent of [Retrieval Augmented Generation (RAG)](../concept/retrieval-augmented-generation.md) in document generative AI underscores the significance of hierarchical document structure analysis. The Layout model supports sections and subsections in the output, which identifies the relationship of sections and object within each section. The hierarchical structure is maintained in `elements` of each section.
 
 ```json
 {
@@ -192,15 +190,15 @@ Hierarchical document structure analysis is pivotal in organizing, comprehending
 A form field consists of a field label (key) and value. The field label is generally a descriptive text string describing the meaning of the field. It often appears to the left of the value, though it can also appear over or under the value. The field value contains the content value of a specific field instance. The value can consist of words, selection marks, and other content elements. It can also be empty for unfilled form fields. A special type of form field has a selection mark value with the field label to its right.
 Document field is a similar but distinct concept from general form fields. The field label (key) in a general form field must appear in the document. Thus, it can't generally capture information like the merchant name in a receipt. Document fields are labeled and don't extract a key. Document fields only map an extracted value to a labeled key. For more information, *see* [document fields](#form-field-key-value-pair).
 
-:::image type="content" source="media/key-value-pair.png" alt-text="Screenshot of detected key-value pairs example.":::
+:::image type="content" source="../media/key-value-pair.png" alt-text="Screenshot of detected key-value pairs example.":::
 
 ### Style elements
 
 #### Style
 
 A style element describes the font style to apply to text content. The content is specified via spans into the global content property. Currently, the only detected font style is whether the text is handwritten. As other styles are added, text can be described via multiple nonconflicting style objects. For compactness, all text sharing the particular font style (with the same confidence) are described via a single style object.
 
-:::image type="content" source="media/style.png" alt-text="Screenshot of detected style handwritten text example.":::
+:::image type="content" source="../media/style.png" alt-text="Screenshot of detected style handwritten text example.":::
 
 ```json
 
@@ -339,5 +337,4 @@ The semantic schema of a document type is described via the fields it contains.
 
 * Try processing your own forms and documents with [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio).
 
-* Complete a [Document Intelligence quickstart](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true) and get started creating a document processing app in the development language of your choice.
-
+* Complete a [Document Intelligence quickstart](../quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true) and get started creating a document processing app in the development language of your choice.

@@ -13,64 +13,54 @@ ms.author: lajanuar
 
 # Which model should I choose?
 
- ::: moniker range="doc-intel-4.0.0"
-[!INCLUDE [preview-version-notice](includes/preview-notice.md)]
-
-**This content applies to:**![checkmark](media/yes-icon.png) **v4.0 (preview)** | **Previous versions:** ![blue-checkmark](media/blue-yes-icon.png) [**v3.1 (GA)**](?view=doc-intel-3.1.0&preserve-view=tru) ![blue-checkmark](media/blue-yes-icon.png) [**v3.0 (GA)**](?view=doc-intel-3.0.0&preserve-view=tru)
-::: moniker-end
-
-::: moniker range="doc-intel-3.1.0"
-**This content applies to:** ![checkmark](media/yes-icon.png) **v3.1 (GA)** | **Latest version:** ![purple-checkmark](media/purple-yes-icon.png) [**v4.0 (preview)**](?view=doc-intel-4.0.0&preserve-view=true) | **Previous versions:** ![blue-checkmark](media/blue-yes-icon.png) [**v3.0**](?view=doc-intel-3.0.0&preserve-view=true)
-::: moniker-end
-
-::: moniker range="doc-intel-3.0.0"
-**This content applies to:** ![checkmark](media/yes-icon.png) **v3.0 (GA)** | **Latest versions:** ![purple-checkmark](media/purple-yes-icon.png) [**v4.0 (preview)**](?view=doc-intel-4.0.0&preserve-view=true) ![purple-checkmark](media/purple-yes-icon.png) [**v3.1**](?view=doc-intel-3.1.0&preserve-view=true)
+ ::: moniker range="<=doc-intel-3.1.0"
+[!INCLUDE [preview-version-notice](../includes/preview-notice.md)]
 ::: moniker-end
 
 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.
 
 > [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE5fX1b]
 
-The following decision charts highlight the features of each **Document Intelligence v3.0** supported model and help you choose the best model to meet the needs and requirements of your application.
+The following decision charts highlight the features of each supported model to help you choose the model that best meets the needs and requirements of your application.
 
 > [!IMPORTANT]
-> Be sure to check the [**language support**](language-support.md) page for supported language text and field extraction  by feature.
+> Be sure to check the [**language support**](../language-support/prebuilt.md) page for supported language text and field extraction  by feature.
 
 ## Pretrained document-analysis models
 
 | Document type | Example| Data to extract | Your best solution |
 | -----------------|-----------|--------|-------------------|
-|**A generic document**. | A contract or letter. |You want to primarily extract written or printed text lines, words, locations, and detected languages.|[**Read OCR model**](concept-read.md)|
-|**A document that includes structural information**. |A report or study.| In addition to written or printed text, you need to extract structural information like tables, selection marks, paragraphs, titles, headings, and subheadings.| [**Layout analysis model**](concept-layout.md)
-|**A structured or semi-structured document that includes content formatted as fields (keys) and values**.|A form or document that is a standardized format commonly used in your business or industry like a credit application or survey. | You want to extract fields and values including ones not covered by the scenario-specific prebuilt models **without having to train a custom model**.| [**Layout analysis model with the optional query string parameter `features=keyValuePairs` enabled **](concept-layout.md)|
+|**A generic document**. | A contract or letter. |You want to primarily extract written or printed text lines, words, locations, and detected languages.|[**Read OCR model**](../prebuilt/read.md)|
+|**A document that includes structural information**. |A report or study.| In addition to written or printed text, you need to extract structural information like tables, selection marks, paragraphs, titles, headings, and subheadings.| [**Layout analysis model**](../prebuilt/layout.md)|
+|**A structured or semi-structured document that includes content formatted as fields (keys) and values**.|A form or document that is a standardized format commonly used in your business or industry like a credit application or survey. | You want to extract fields and values including ones not covered by the scenario-specific prebuilt models **without having to train a custom model**.| [**Layout analysis model with the optional query string parameter `features=keyValuePairs` enabled **](../prebuilt/layout.md)|
 
 ## Pretrained scenario-specific models
 
 | Document type | Data to extract | Your best solution |
 | -----------------|--------------|-------------------|
-|**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**](concept-tax-document.md)|
-|**US Tax W-2 tax**|You want to extract key information such as salary, wages, and taxes withheld.|[**US tax W-2 model**](concept-tax-document.md)|
-|**US Tax 1098**|You want to extract mortgage interest details such as principal, points, and tax.|[**US tax 1098 model**](concept-tax-document.md)|
-|**US Tax 1098-E**|You want to extract student loan interest details such as lender and interest amount.|[**US tax 1098-E model**](concept-tax-document.md)|
-|**US Tax 1098T**|You want to extract qualified tuition details such as scholarship adjustments, student status, and lender information.|[**US tax 1098-T model**](concept-tax-document.md)|
-|**US Tax 1099(Variations)**|You want to extract information from `1099` forms and its variations (A, B, C, CAP, DIV, G, H, INT, K, LS, LTC, MISC, NEC, OID, PATR, Q, QA, R, S, SA, SB).|[**US tax 1099 model**](concept-tax-document.md)|
-|**US Tax 1040(Variations)**|You want to extract information from `1040` forms and its variations (Schedule 1, Schedule 2, Schedule 3, Schedule 8812, Schedule A, Schedule B, Schedule C, Schedule D, Schedule E, Schedule `EIC`, Schedule F, Schedule H, Schedule J, Schedule R, Schedule `SE`, Schedule Senior).|[**US tax 1040 model**](concept-tax-document.md)|
-|**Bank Statement**  |You want to extract key information from US bank statement | [**\Bank Statement**](concept-bank-statement.md)|
-|**Check** |You want to extract key information from check document. | [**Bank Check**](concept-bank-check.md)|
-|**Contract** (legal agreement between parties).|You want to extract contract agreement details such as parties, dates, and intervals.|[**Contract model**](concept-contract.md)|
-|**Health insurance card** or health insurance ID.| You want to extract key information such as insurer, member ID, prescription coverage, and group number.|[**Health insurance card model**](./concept-health-insurance-card.md)|
-|**Credit/Debit card** |You want to extract key information bank cards such as card number and bank name. | [**Credit/Debit card model**](concept-credit-card.md)|
-|**Marriage Certificate** |You want to extract key information from marriage certificates. | [**Marriage certificate model**](concept-marriage-certificate.md)|
-|**Invoice** or billing statement|You want to extract key information such as customer name, billing address, and amount due.|[**Invoice model**](concept-invoice.md)
-|**Receipt**, voucher, or single-page hotel receipt. |You want to extract key information such as merchant name, transaction date, and transaction total.|[**Receipt model**](concept-receipt.md)|
-|**Identity document (ID)** like a U.S. driver's license or international passport |You want to extract key information such as first name, surname, date of birth, address, and signature. | [**Identity document (ID) model**](concept-id-document.md)|
-|**Pay stub** |You want to extract key information from the pay stub document. | [**Pay stub Model**](concept-pay-stub.md)|
-|**US Mortgage 1003** |You want to extract key information from the Uniform Residential loan application. | [**1003 form model**](concept-mortgage-documents.md)|
-|**US Mortgage 1004** |You want to extract key information from the Uniform Residential Appraisal Report (URAR). | [**1004 form model**](concept-mortgage-documents.md)|
-|**US Mortgage 1005** |You want to extract key information from the Verification of employment form | [**1005 form model**](concept-mortgage-documents.md)|
-|**US Mortgage 1008**  |You want to extract key information from the Uniform Underwriting and Transmittal summary. | [**1008 form model**](concept-mortgage-documents.md)|
-|**US Mortgage Closing Disclosure** |You want to extract key information from a mortgage closing disclosure form. | [**Mortgage closing disclosure form model**](concept-mortgage-documents.md)|
-|**Mixed-type document(s)** with structured, semi-structured, and/or unstructured elements | You want to extract key-value pairs, selection marks, tables, signature fields, and selected regions not extracted by prebuilt or general document models.| [**Custom model**](concept-custom.md)|
+|**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)|
+|**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)|
+|**US Tax 1098**|You want to extract mortgage interest details such as principal, points, and tax.|[**US tax 1098 model**](../prebuilt/tax-document.md)|
+|**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)|
+|**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)|
+|**US Tax 1099(Variations)**|You want to extract information from `1099` forms and its variations (A, B, C, CAP, DIV, G, H, INT, K, LS, LTC, MISC, NEC, OID, PATR, Q, QA, R, S, SA, SB).|[**US tax 1099 model**](../prebuilt/tax-document.md)|
+|**US Tax 1040(Variations)**|You want to extract information from `1040` forms and its variations (Schedule 1, Schedule 2, Schedule 3, Schedule 8812, Schedule A, Schedule B, Schedule C, Schedule D, Schedule E, Schedule `EIC`, Schedule F, Schedule H, Schedule J, Schedule R, Schedule `SE`, Schedule Senior).|[**US tax 1040 model**](../prebuilt/tax-document.md)|
+|**Bank Statement**  |You want to extract key information from US bank statement | [**\Bank Statement**](../concept-bank-statement.md)|
+|**Bank check** |You want to extract key information from check document. | [**Bank Check**](../concept-bank-check.md)|
+|**Contract** (legal agreement between parties).|You want to extract contract agreement details such as parties, dates, and intervals.|[**Contract model**](../prebuilt/contract.md)|
+|**Health insurance card** or health insurance ID.| You want to extract key information such as insurer, member ID, prescription coverage, and group number.|[**Health insurance card model**](../prebuilt/health-insurance-card.md)|
+|**Credit/Debit card** |You want to extract key information bank cards such as card number and bank name. | [**Credit/Debit card model**](../concept-credit-card.md)|
+|**Marriage Certificate** |You want to extract key information from marriage certificates. | [**Marriage certificate model**](../concept-marriage-certificate.md)|
+|**Invoice** or billing statement|You want to extract key information such as customer name, billing address, and amount due.|[**Invoice model**](../prebuilt/invoice.md)|
+|**Receipt**, voucher, or single-page hotel receipt. |You want to extract key information such as merchant name, transaction date, and transaction total.|[**Receipt model**](../prebuilt/receipt.md)|
+|**Identity document (ID)** like a U.S. driver's license or international passport |You want to extract key information such as first name, surname, date of birth, address, and signature. | [**Identity document (ID) model**](../prebuilt/id-document.md)|
+|**Pay stub** |You want to extract key information from the pay stub document. | [**Pay stub Model**](../concept-pay-stub.md)|
+|**US Mortgage 1003** |You want to extract key information from the Uniform Residential loan application. | [**1003 form model**](../concept-mortgage-documents.md)|
+|**US Mortgage 1004** |You want to extract key information from the Uniform Residential Appraisal Report (URAR). | [**1004 form model**](../concept-mortgage-documents.md)|
+|**US Mortgage 1005** |You want to extract key information from the Verification of employment form | [**1005 form model**](../concept-mortgage-documents.md)|
+|**US Mortgage 1008**  |You want to extract key information from the Uniform Underwriting and Transmittal summary. | [**1008 form model**](../concept-mortgage-documents.md)|
+|**US Mortgage Closing Disclosure** |You want to extract key information from a mortgage closing disclosure form. | [**Mortgage closing disclosure form model**](../concept-mortgage-documents.md)|
+|**Mixed-type document(s)** with structured, semi-structured, and/or unstructured elements | You want to extract key-value pairs, selection marks, tables, signature fields, and selected regions not extracted by prebuilt or general document models.| [**Custom model**](../train/custom-model.md)|
 
 >[!Tip]
 >
@@ -81,17 +71,17 @@ The following decision charts highlight the features of each **Document Intellig
 
 | Training set | Example documents | Your best solution |
 | -----------------|--------------|-------------------|
-|**Structured, consistent, documents with a static layout**. |Structured forms such as questionnaires or applications. | [**Custom template model**](./concept-custom-template.md)|
-|**Structured and semi-structured**.|&#9679; Structured &rightarrow; surveys</br>&#9679; Semi-structured &rightarrow; invoices | [**Custom neural model**](concept-custom-neural.md)|
-|**Unstructured documents, documents with varying templates**.|&#9679; Unstructured documents like contracts or letters</br> &#9679; Varying document templates like loan statements from different mortgage companies| [**Custom generative model**](concept-custom-generative.md)|
-|**A collection of several models each trained on similar-type documents.** |&#9679; Supply purchase orders</br>&#9679; Equipment purchase orders</br>&#9679; Furniture purchase orders</br> **All composed into a single model**.| [**Composed custom model**](concept-composed-models.md)|
+|**Structured, consistent, documents with a static layout**. |Structured forms such as questionnaires or applications. | [**Custom template model**](./../train/custom-template.md)|
+|**Structured and semi-structured**.|&#9679; Structured &rightarrow; surveys</br>&#9679; Semi-structured &rightarrow; invoices | [**Custom neural model**](../train/custom-neural.md)|
+|**Unstructured documents, documents with varying templates**.|&#9679; Unstructured documents like contracts or letters</br> &#9679; Varying document templates like loan statements from different mortgage companies| [**Custom generative model**](../train/custom-generative-extraction.md)|
+|**A collection of several models each trained on similar-type documents.** |&#9679; Supply purchase orders</br>&#9679; Equipment purchase orders</br>&#9679; Furniture purchase orders</br> **All composed into a single model**.| [**Composed custom model**](../train/composed-models.md)|
 
 ## Custom classification model
 
 | Training set | Example documents | Your best solution |
 | -----------------|--------------|-------------------|
-|**At least two different types of documents**. |Forms, letters, or documents | [**Custom classification model**](./concept-custom-classifier.md)|
+|**At least two different types of documents**. |Forms, letters, or documents | [**Custom classification model**](./../train/custom-classifier.md)|
 
 ## Next steps
 
-* [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)
+* [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)

@@ -1,28 +1,34 @@
 ---
-title: Document Intelligence support for incremental classifier training
+title: Use Document Intelligence incremental classifiers
 titleSuffix: Azure AI services
 description: Incrementally train custom classifiers by adding new samples to existing classes or adding new classes.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 05/23/2024
+ms.date: 10/03/2024
 ms.author: vikurpad
 ms.custom:
 monikerRange: '>=doc-intel-4.0.0'
 ---
 
+<!-- markdownlint-disable MD033 -->
 
+# Use Document Intelligence incremental classifiers
 
-# Incremental classifier training
-
-**This content applies to:** ![checkmark](media/yes-icon.png) **v4.0 (preview)** ![checkmark](media/yes-icon.png)
+**This content applies to:** ![checkmark](../media/yes-icon.png) **v4.0 (preview)** ![checkmark](../media/yes-icon.png)
 
 Azure AI Document Intelligence is a cloud-based Azure AI service that enables you to build intelligent document processing solutions. Document Intelligence APIs analyze images, PDFs, and other document files to extract and detect various content, layout, style, and semantic elements.
 
-[Document Intelligence custom classification models](concept-custom-classifier.md) are deep-learning-model types that combine layout and language features to accurately detect and identify documents you process within your applications. Custom classification models perform classification of input files one page at a time to identify the documents within and can also identify multiple documents or multiple instances of a single document within an input file.
+[Document Intelligence custom classification models](../train/custom-classifier.md) are deep-learning-model types that combine layout and language features to accurately detect and identify documents you process within your applications. Custom classification models perform classification of input files one page at a time to identify the documents within and can also identify multiple documents or multiple instances of a single document within an input file.
+
+Document Intelligence document classifiers identify known document types in files. When processing an input file with multiple document types or when you don't know the document type, use a classifier to identify the document. Classifiers should be periodically updated whenever the following changes occur:
+
+* You add new templates for an existing class.
+* You add new document types for recognition.
+* Classifier confidence is low.
 
-Document Intelligence document classifiers identify known document types in files. When processing an input file with multiple document types or when you don't know the document type, use a classifier to identify the document. Classifiers should be periodically updated when you add new templates for an existing class, add new document types for recognition, or classifier confidence is low. In some scenarios, you can no longer have the original set of documents used to train the classifier. With incremental training, you can now update the classifier with just the new labeled samples.
+In some scenarios, you can no longer have the original set of documents used to train the classifier. With incremental training, you can update the classifier with just the new labeled samples.
 
 >[!NOTE]
 > Incremental training only applies to document classifier models and not custom models.
@@ -44,7 +50,7 @@ Incremental training is useful when you want to improve the quality of a custom
 
 ### Create an incremental classifier build request
 
-The incremental classifier build request is similar to the [classify document build request](/rest/api/aiservices/document-classifiers?view=rest-aiservices-v4.0%20(2024-02-29-preview)&preserve-view=true) but includes the new `baseClassifierId` property. The `baseClassifierId` is set to the existing classifier that you want to extend. You also need to provide the `docTypes` for the different document types in the sample set. By providing a `docType` that exists in the baseClassifier, the samples provided in the request are added to the samples provided when the base classifier was trained. New `docType` values added in the incremental training are only added to the new classifier. The process to specify the samples remains unchanged. For more information, *see* [training a classifier model](concept-custom-classifier.md#training-a-model).
+The incremental classifier build request is similar to the [`classify document` build request](/rest/api/aiservices/document-classifiers?view=rest-aiservices-v4.0%20(2024-02-29-preview)&preserve-view=true) but includes the new `baseClassifierId` property. The `baseClassifierId` is set to the existing classifier that you want to extend. You also need to provide the `docTypes` for the different document types in the sample set. By providing a `docType` that exists in the baseClassifier, the samples provided in the request are added to the samples provided when the base classifier was trained. New `docType` values added in the incremental training are only added to the new classifier. The process to specify the samples remains unchanged. For more information, *see* [training a classifier model](../train/custom-classifier.md#training-a-model).
 
 ### Sample POST request
 
@@ -124,10 +130,10 @@ The `GET` response from an incrementally trained classifier differs from the sta
 
 ## Limits
 
-* Incremental training only works when the base classifier and the incrementally trained classifier are both trained on the same API version. As a result, the incrementally trained classifier has the same [model lifecycle](concept-custom-lifecycle.md) as the base classifier.
+* Incremental training only works when the base classifier and the incrementally trained classifier are both trained on the same API version. As a result, the incrementally trained classifier has the same [model lifecycle](../concept-custom-lifecycle.md) as the base classifier.
 
-* Training dataset size limits for the incremental classifier are the same as for other classifier model. See [service limits](service-limits.md) for a complete list of applicable limits.
+* Training dataset size limits for the incremental classifier are the same as for other classifier model. See [service limits](../service-limits.md) for a complete list of applicable limits.
 
 ## Next steps
 
-* Learn more about [document classification](concept-custom-classifier.md)
+* Learn more about [document classification](../train/custom-classifier.md)

@@ -5,8 +5,6 @@ description: Use Document Intelligence query fields to extend model schema.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: conceptual
 ms.date: 01/19/2024
 ms.author: nitinme
@@ -38,18 +36,18 @@ For query field extraction, specify the fields you want to extract and Document
 
 * If you're processing a contract in the [Document Intelligence Studio](https://documentintelligence.ai.azure.com/studio/layout), use the `2024-02-29-preview`, `2023-10-31-preview`, or later API version:
 
-    :::image type="content" source="media/studio/query-fields.png" alt-text="Screenshot of the query fields button in Document Intelligence Studio.":::
+    :::image type="content" source="../media/studio/query-fields.png" alt-text="Screenshot of the query fields button in Document Intelligence Studio.":::
 
 * You can pass a list of field labels like `Party1`, `Party2`, `TermsOfUse`, `PaymentTerms`, `PaymentDate`, and `TermEndDate`" as part of the `AnalyzeDocument` request.
 
-   :::image type="content" source="media/studio/query-field-select.png" alt-text="Screenshot of query fields selection window in Document Intelligence Studio.":::
+   :::image type="content" source="../media/studio/query-field-select.png" alt-text="Screenshot of query fields selection window in Document Intelligence Studio.":::
 
-* In addition to the query fields, the response includes the model output. For a list of features or schema extracted by each model, see [model analysis features](concept-model-overview.md#model-analysis-features).
+* In addition to the query fields, the response includes the model output. For a list of features or schema extracted by each model, see [model analysis features](../model-overview.md#model-analysis-features).
 
 
 ## Query fields REST API request**
 
-Use the query fields feature with the [general document model](concept-general-document.md), and add fields to the extraction process without having to train a custom model:
+Use the query fields feature with the [general document model](../prebuilt/general-document.md), and add fields to the extraction process without having to train a custom model:
 
 ```http
 POST https://{endpoint}/documentintelligence/documentModels/prebuilt-layout:analyze?api-version=2024-02-29-preview&features=queryFields&queryFields=Terms,PaymentDate HTTP/1.1
@@ -65,7 +63,7 @@ Ocp-Apim-Subscription-Key:
 ## Next steps
 
 > [!div class="nextstepaction"]
-> [Try the Document Intelligence Studio quickstart](./quickstarts/try-document-intelligence-studio.md)
+> [Try the Document Intelligence Studio quickstart](../quickstarts/try-document-intelligence-studio.md)
 
 > [!div class="nextstepaction"]
-> [Learn about other add-on capabilities](concept-add-on-capabilities.md)
+> [Learn about other add-on capabilities](../concept/add-on-capabilities.md)

@@ -15,15 +15,15 @@ monikerRange: '>=doc-intel-3.1.0'
 
 <!-- markdownlint-disable MD036 -->
 
-**This content applies to:** ![checkmark](media/yes-icon.png) **v4.0 (preview)**
+**This content applies to:** ![checkmark](../media/yes-icon.png) **v4.0 (preview)**
 
 ## Introduction
 
 Retrieval-Augmented Generation (RAG) is a design pattern that combines a pretrained Large Language Model (LLM) like ChatGPT with an external data retrieval system to generate an enhanced response incorporating new data outside of the original training data. Adding an information retrieval system to your applications enables you to chat with your documents, generate captivating content, and access the power of Azure OpenAI models for your data. You also have more control over the data used by the LLM as it formulates a response.
 
-The Document Intelligence [Layout model](concept-layout.md) is an advanced machine-learning based document analysis API. The Layout model offers a comprehensive solution for advanced content extraction and document structure analysis capabilities. With the Layout model, you can easily extract text and structural elements to divide large bodies of text into smaller, meaningful chunks based on semantic content rather than arbitrary splits. The extracted information can be conveniently outputted to Markdown format, enabling you to define your semantic chunking strategy based on provided building blocks.
+The Document Intelligence [Layout model](../prebuilt/layout.md) is an advanced machine-learning based document analysis API. The Layout model offers a comprehensive solution for advanced content extraction and document structure analysis capabilities. With the Layout model, you can easily extract text and structural elements to divide large bodies of text into smaller, meaningful chunks based on semantic content rather than arbitrary splits. The extracted information can be conveniently outputted to Markdown format, enabling you to define your semantic chunking strategy based on provided building blocks.
 
-:::image type="content" source="media/rag/azure-rag-processing.png" alt-text="Screenshot depicting semantic chunking with RAG using Azure AI Document Intelligence.":::
+:::image type="content" source="../media/rag/azure-rag-processing.png" alt-text="Screenshot depicting semantic chunking with RAG using Azure AI Document Intelligence.":::
 
 ## Semantic chunking
 
@@ -37,23 +37,23 @@ Text data chunking strategies play a key role in optimizing the RAG response and
 
 ## Semantic chunking with Document Intelligence Layout model
 
-Markdown is a structured and formatted markup language and a popular input for enabling semantic chunking in RAG (Retrieval-Augmented Generation). You can use the Markdown content from the [Layout model](concept-layout.md) to split documents based on paragraph boundaries, create specific chunks for tables, and fine-tune your chunking strategy to improve the quality of the generated responses.
+Markdown is a structured and formatted markup language and a popular input for enabling semantic chunking in RAG (Retrieval-Augmented Generation). You can use the Markdown content from the [Layout model](../prebuilt/layout.md) to split documents based on paragraph boundaries, create specific chunks for tables, and fine-tune your chunking strategy to improve the quality of the generated responses.
 
 ### Benefits of using the Layout model
 
 * **Simplified processing**. You can parse different document types, such as digital and scanned PDFs, images, office files (docx, xlsx, pptx), and HTML, with just a single API call.
 
-* **Scalability and AI quality**. The Layout model is highly scalable in Optical Character Recognition (OCR), table extraction, and [document structure analysis](concept-layout.md#document-layout-analysis). It supports [309 printed and 12 handwritten languages](language-support-ocr.md#model-id-prebuilt-layout), further ensuring high-quality results driven by AI capabilities.
+* **Scalability and AI quality**. The Layout model is highly scalable in Optical Character Recognition (OCR), table extraction, and [document structure analysis](../prebuilt/layout.md#document-layout-analysis). It supports [309 printed and 12 handwritten languages](../language-support/ocr.md#model-id-prebuilt-layout), further ensuring high-quality results driven by AI capabilities.
 
 * **Large language model (LLM) compatibility**. The Layout model Markdown formatted output is LLM friendly and facilitates seamless integration into your workflows. You can turn any table in a document into Markdown format and avoid extensive effort parsing the documents for greater LLM understanding.
 
 **Text image processed with Document Intelligence Studio and output to MarkDown using Layout model**
 
-  :::image type="content" source="media/rag/markdown-text-output.png" alt-text="Screenshot of newspaper article processed by Layout model and outputted to Markdown.":::
+  :::image type="content" source="../media/rag/markdown-text-output.png" alt-text="Screenshot of newspaper article processed by Layout model and outputted to Markdown.":::
 
 **Table image processed with Document Intelligence Studio using Layout model**
 
-  :::image type="content" source="media/rag/markdown-table-output.png" alt-text="Screenshot of table processed by Layout model and outputted to Markdown.":::
+  :::image type="content" source="../media/rag/markdown-table-output.png" alt-text="Screenshot of table processed by Layout model and outputted to Markdown.":::
 
 ## Get started
 
@@ -63,13 +63,13 @@ The Document Intelligence Layout model **2024-07-31-preview** and **2023-10-31-p
 
 * [REST API](/rest/api/aiservices/document-models/build-model?view=rest-aiservices-2024-02-29-preview&preserve-view=true&branch=docintelligence&tabs=HTTP).
 
-* [.NET &bull; Java &bull; JavaScript &bull; Python programming language client libraries (SDKs).](sdk-overview-v4-0.md#supported-programming-languages)
+* [.NET &bull; Java &bull; JavaScript &bull; Python programming language client libraries (SDKs).](../sdk-overview-v4-0.md#supported-programming-languages)
 
 **Ready to begin?**
 
 ### Document Intelligence Studio
 
-You can follow the [Document Intelligence Studio quickstart](quickstarts/try-document-intelligence-studio.md) to get started. Next, you can integrate Document Intelligence features with your own application using the sample code provided.
+You can follow the [Document Intelligence Studio quickstart](../quickstarts/try-document-intelligence-studio.md) to get started. Next, you can integrate Document Intelligence features with your own application using the sample code provided.
 
 * Start with the [Layout model](https://documentintelligence.ai.azure.com/studio/layout). You need to select the following **Analyze options** to use RAG in the studio:
 
@@ -85,15 +85,15 @@ You can follow the [Document Intelligence Studio quickstart](quickstarts/try-doc
 
 * Select **Save**.
 
-  :::image type="content" source="media/rag/rag-analyze-options.png" alt-text="Screenshot of Analyze options dialog window with RAG required options in the Document Intelligence studio.":::
+  :::image type="content" source="../media/rag/rag-analyze-options.png" alt-text="Screenshot of Analyze options dialog window with RAG required options in the Document Intelligence studio.":::
 
 * Select the **Run analysis** button to view the output.
 
-  :::image type="content" source="media/rag/run-analysis.png" alt-text="Screenshot of the Run Analysis button in the Document Intelligence Studio.":::
+  :::image type="content" source="../media/rag/run-analysis.png" alt-text="Screenshot of the Run Analysis button in the Document Intelligence Studio.":::
 
 ### SDK or REST API
 
-* You can follow the [Document Intelligence quickstart](quickstarts/get-started-sdks-rest-api.md) for your preferred programming language SDK or REST API. Use the Layout model to extract content and structure from your documents.
+* You can follow the [Document Intelligence quickstart](../quickstarts/get-started-sdks-rest-api.md) for your preferred programming language SDK or REST API. Use the Layout model to extract content and structure from your documents.
 
 * You can also check out GitHub repos for code samples and tips for analyzing a document in markdown output format.
 
@@ -107,7 +107,7 @@ You can follow the [Document Intelligence Studio quickstart](quickstarts/try-doc
 
 ## Build document chat with semantic chunking
 
-* [Azure OpenAI on your data](../openai/concepts/use-your-data.md) enables you to run supported chat on your documents. Azure OpenAI on your data applies the Document Intelligence Layout model to extract and parse document data by chunking long text based on tables and paragraphs. You can also customize your chunking strategy using [Azure OpenAI sample scripts](https://github.com/microsoft/sample-app-aoai-chatGPT/tree/main/scripts) located in our GitHub repo.
+* [Azure OpenAI on your data](../../openai/concepts/use-your-data.md) enables you to run supported chat on your documents. Azure OpenAI on your data applies the Document Intelligence Layout model to extract and parse document data by chunking long text based on tables and paragraphs. You can also customize your chunking strategy using [Azure OpenAI sample scripts](https://github.com/microsoft/sample-app-aoai-chatGPT/tree/main/scripts) located in our GitHub repo.
 
 * Azure AI Document Intelligence is now integrated with [LangChain](https://python.langchain.com/docs/integrations/document_loaders/azure_document_intelligence) as one of its document loaders. You can use it to easily load the data and output to Markdown format. For more information, see our [sample code](https://github.com/microsoft/Form-Recognizer-Toolkit/blob/main/SampleCode/Python/sample_rag_langchain.ipynb) that shows a simple demo for RAG pattern with Azure AI Document Intelligence as document loader and Azure Search as retriever in LangChain.
 
@@ -153,8 +153,8 @@ splits
 
 ## Next steps
 
-* Learn more about [Azure AI Document Intelligence](overview.md).
+* Learn more about [Azure AI Document Intelligence](../overview.md).
 
-* [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).
+* [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).
 
-* Complete a [Document Intelligence quickstart](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-4.0.0&preserve-view=true) and get started creating a document processing app in the development language of your choice.
+* Complete a [Document Intelligence quickstart](../quickstarts/get-started-sdks-rest-api.md?view=doc-intel-4.0.0&preserve-view=true) and get started creating a document processing app in the development language of your choice.

@@ -5,8 +5,6 @@ description: Learn how to configure the Document Intelligence container to parse
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: how-to
 ms.date: 05/23/2024
 ms.author: lajanuar
@@ -18,12 +16,12 @@ ms.author: lajanuar
 
 :::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, Layout, Invoice, Receipt and ID Document models:
+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, Invoice, Receipt, and ID Document 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)
-* [SDKs targeting `REST API 2022-08-31 (GA)`](../sdk-overview-v3-0.md)
-* [SDKs targeting `REST API 2023-07-31 (GA)`](../sdk-overview-v3-1.md)
+* [Client libraries targeting `REST API 2022-08-31 (GA)`](../sdk-overview-v3-0.md)
+* [Client libraries targeting `REST API 2023-07-31 (GA)`](../sdk-overview-v3-1.md)
 
 ✔️ See [**Configure Document Intelligence v3.0 containers**](?view=doc-intel-3.0.0&preserve-view=true) for supported container documentation.
 
@@ -42,7 +40,7 @@ Each container has the following configuration settings:
 |Required|Setting|Purpose|
 |--|--|--|
 |Yes|[Key](#key-and-billing-configuration-setting)|Tracks billing information.|
-|Yes|[Billing](#key-and-billing-configuration-setting)|Specifies the endpoint URI of the service resource on Azure.  For more information, _see_ [Billing](install-run.md#billing). For more information and a complete list of regional endpoints, _see_ [Custom subdomain names for Azure AI services](../../../ai-services/cognitive-services-custom-subdomains.md).|
+|Yes|[Billing](#key-and-billing-configuration-setting)|Specifies the endpoint URI of the service resource on Azure. For more information, _see_ [Billing](install-run.md#billing). For more information and a complete list of regional endpoints, _see_ [Custom subdomain names for Azure AI services](../../../ai-services/cognitive-services-custom-subdomains.md).|
 |Yes|[Eula](#eula-setting)| Indicates that you accepted the license for the container.|
 |No|[ApplicationInsights](#applicationinsights-setting)|Enables adding [Azure Application Insights](/azure/application-insights) customer support for your container.|
 |No|[Fluentd](#fluentd-settings)|Writes log and, optionally, metric data to a Fluentd server.|
@@ -62,7 +60,7 @@ The `Billing` setting specifies the endpoint URI of the resource on Azure that i
 
    :::image type="content" source="../media/containers/keys-and-endpoint.png" alt-text="Screenshot of Azure portal keys and endpoint page.":::
 
-## EULA setting
+## `EULA` setting
 
 [!INCLUDE [Container shared configuration eula settings](../../includes/cognitive-services-containers-configuration-shared-settings-eula.md)]
 
@@ -84,7 +82,7 @@ The `Billing` setting specifies the endpoint URI of the resource on Azure that i
 
 ## Volume settings
 
-Use [**volumes**](https://docs.docker.com/storage/volumes/) to read and write data to and from the container. Volumes are the preferred for persisting data generated and used by Docker containers. You can specify an input mount or an output mount by including the `volumes` option and specifying `type` (bind), `source` (path to the folder) and `target` (file path parameter).
+Use [**volumes**](https://docs.docker.com/storage/volumes/) to read and write data to and from the container. Volumes are the preferred for persisting data generated and used by Docker containers. You can specify an input mount or an output mount by including the `volumes` option and specifying `type` (bind), `source` (path to the folder), and `target` (file path parameter).
 
 The Document Intelligence container requires an input volume and an output volume. The input volume can be read-only (`ro`), and is required for access to the data that is used for training and scoring. The output volume has to be writable, and you use it to store the models and temporary data.
 

@@ -3,8 +3,6 @@ title: Use Document Intelligence containers in disconnected environments
 titleSuffix: Azure AI services
 description: Learn how to run Cognitive Services Docker containers disconnected from the internet.
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 author: laujan
 manager: nitinme
 ms.topic: reference
@@ -17,12 +15,12 @@ ms.author: lajanuar
 
 :::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, Layout, Invoice, Receipt and ID Document models:
+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, Invoice, Receipt, and ID Document 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)
-* [SDKs targeting `REST API 2022-08-31 (GA)`](../sdk-overview-v3-0.md)
-* [SDKs targeting `REST API 2023-07-31 (GA)`](../sdk-overview-v3-1.md)
+* [Client libraries targeting `REST API 2022-08-31 (GA)`](../sdk-overview-v3-0.md)
+* [Client libraries targeting `REST API 2023-07-31 (GA)`](../sdk-overview-v3-1.md)
 
 ✔️ See [**Document Intelligence v3.0 containers in disconnected environments**](?view=doc-intel-3.0.0&preserve-view=true) for supported container documentation.
 
@@ -56,13 +54,13 @@ Before you can use Document Intelligence containers in disconnected environments
 Start by provisioning a new resource in the portal.
 
 * Ensure you select the `Commitment tier disconnected containers DC0` option for Pricing tier
-* Select the appropriate pricing tier from at least one of custom, read or prebuilt commitment tiers
+* Select the appropriate pricing tier from at least one of custom, read, or prebuilt commitment tiers
 
   :::image type="content" source="../media/containers/disconnected.png" alt-text="Screenshot of disconnected tier configuration in the Azure portal.":::
 
 | Container | Minimum | Recommended | Commitment plan |
 |-----------|---------|-------------|-------------|
-| `Read` | `8` cores, 10-GB memory | `8` cores, 24-GB memory| OCR (Read) |
+| `Read` | `8` cores, 10-GB memory | `8` cores, 24-GB memory| `OCR` (Read) |
 | `Layout` | `8` cores, 16-GB memory | `8` cores, 24-GB memory | Prebuilt |
 | `Business Card` | `8` cores, 16-GB memory | `8` cores, 24-GB memory | Prebuilt |
 | `General Document` | `8` cores, 12-GB memory | `8` cores, 24-GB memory| Prebuilt |

@@ -5,8 +5,6 @@ description: A listing of all Document Intelligence container image tags.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: reference
 ms.date: 05/23/2024
 ms.author: lajanuar
@@ -18,12 +16,12 @@ ms.author: lajanuar
 
 :::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, Layout, Invoice, Receipt and ID Document models:
+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, Invoice, Receipt, and ID Document 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)
-* [SDKs targeting `REST API 2022-08-31 (GA)`](../sdk-overview-v3-0.md)
-* [SDKs targeting `REST API 2023-07-31 (GA)`](../sdk-overview-v3-1.md)
+* [Client libraries targeting `REST API 2022-08-31 (GA)`](../sdk-overview-v3-0.md)
+* [Client libraries targeting `REST API 2023-07-31 (GA)`](../sdk-overview-v3-1.md)
 
 ✔️ See [**Document Intelligence container image tags**](?view=doc-intel-3.0.0&preserve-view=true) for supported container documentation.
 

@@ -52,12 +52,12 @@ sections:
         answer: |
           **Yes.**
 
-          Document Intelligence now includes [custom generative](concept-custom.md) a new type of extraction model that uses Generative AI and large language models (LLMs) to extract fields from documents. In the past, you used a RAG (retrieval augmented generation) pattern to extract fields. The new model provides high quality results with a single API call.
+          Document Intelligence now includes [custom generative](train/custom-generative-extraction.md) a new type of extraction model that uses Generative AI and large language models (LLMs) to extract fields from documents. In the past, you used a RAG (retrieval augmented generation) pattern to extract fields. The new model provides high quality results with a single API call.
           You can also use a document generative AI solution to chat with your documents (RAG), generate captivating content from those documents, and access Azure OpenAI Service 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 language. You can easily find answers, gain valuable insights, and generate new and engaging content from existing documents.
 
-          - You can find more details on the [retrieval augmented generation pattern here](concept-retrieval-augmented-generation.md).
+          - You can find more details on the [retrieval augmented generation pattern here](concept/retrieval-augmented-generation.md).
 
       - question: |
          Can Document Intelligence help with semantic chunking within documents for retrieval-augmented generation?
@@ -70,7 +70,7 @@ sections:
 
           - You can then choose to retrieve the results in markdown format, to further chunk the document on section or paragraph boundaries.
 
-          For more information, see [overview of RAG in Document Intelligence](concept-retrieval-augmented-generation.md)
+          For more information, see [overview of RAG in Document Intelligence](concept/retrieval-augmented-generation.md)
 
   - name: Document Intelligence Studio
     questions:
@@ -134,15 +134,15 @@ sections:
         answer: |
          **Yes.**
 
-         If your Document Intelligence resource is configured with a firewall or virtual network, you need to add the dedicated IP address 20.3.165.95 to the firewall allowlist for your Document Intelligence resource.  Please note that some functions in custom projects (e.g. auto-label, project management and human in the loop) won't work if the public network access is disabled.
+         If your Document Intelligence resource is configured with a firewall or virtual network, you need to add the dedicated IP address 20.3.165.95 to the firewall allowlist for your Document Intelligence resource. Some functions in custom projects (for example, autolabel, project management and human in the loop) don't work if the public network access is disabled.
 
       - question: |
          When I upload a file in Document Intelligence Studio by "Fetch from URL" function, can I use a URL from my blob storage?
 
         answer: |
          **Yes.** 
          
-         If your Azure blob storage URL includes a SAS token and is accessible from public networks.  You cannot use the **Fetch** function for storage accounts where the key access is disabled or behind a firewall/VNet.
+         If your Azure blob storage URL includes a SAS token, and is accessible from public networks. You can't use the **Fetch** function for storage accounts where the key access is disabled or behind a firewall/VNet.
 
       - question: |
           Can I reuse or customize the labeling experience from Document Intelligence Studio and build it into my own application?
@@ -264,7 +264,7 @@ sections:
 
         - For signature and region labeling, don't include the surrounding text.
 
-        For more information, see [Accuracy and confidence scores](concept-accuracy-confidence.md).
+        For more information, see [Accuracy and confidence scores](concept/accuracy-confidence.md).
 
     - question: |
         Can I retrain a custom model?
@@ -285,7 +285,7 @@ sections:
 
           4. Compose your new model with the existing model into a single endpoint. Document Intelligence can then determine the best model for each document to be analyzed.
 
-          For more information, see [composed models](concept-custom.md).
+          For more information, see [composed models](train/custom-model.md).
 
     - question: |
         Can I move my trained models from one environment (like beta) to another (like production)?
@@ -305,9 +305,9 @@ sections:
         
         Custom generative models also rely on the auto label feature to speed up the generation of the labeled dataset. There's a cost associated with this action. While the build operation for template and generative models is free, creating the labeled dataset can result in some minimal costs.
 
-        For `v4.0 2024-07-31-preview`, custom neural models can be trained for free for a **maximum of 10 hours**. Whether you are training a single model for the 10 hours, or training multiple models for the total of 10 hours, you won't be charged for the first 10 hours. After using up the free 10 hours, you will be **automatically charged by the extra training hour**. For details on the prices, refer to the [pricing page](https://azure.microsoft.com/pricing/details/ai-document-intelligence/). This new paid training feature enables training models for an extended duration to process larger documents. For more information on this paid training feature, check [custom neural model billing section](concept-custom-neural.md#billing).
+        For `v4.0 2024-07-31-preview`, custom neural models can be trained for free for a **maximum of 10 hours**. Whether you're training a single model for the 10 hours, or training multiple models for the total of 10 hours, you aren't charged for the first 10 hours. After using up the free 10 hours, you're **automatically charged by the extra training hour**. For details on the prices, refer to the [pricing page](https://azure.microsoft.com/pricing/details/ai-document-intelligence/). This new paid training feature enables training models for an extended duration to process larger documents. For more information on this paid training feature, check [custom neural model billing section](train/custom-neural.md#billing).
         
-        For `v3.0 2022-08-31` or `v3.1 2023-07-31`, custom neural models can be trained for free for a maximum of 20 training sessions, with each session capped at 30 minutes of training duration. Once you use up all of the 20 training sessions, you can submit Azure support ticket to increase the training session limit. For increasing the limit, 2 training sessions will be considered as 1 training hour, and you will be charged per 2 sessions / 1 training hour. For details on the prices, refer to the [pricing page]. For more information on ways to increase the limit, check [custom neural model billing section](concept-custom-neural.md#billing). **Note that for `v3.0` and `v3.1`, paid training feature is unavailable. Paid training feature for custom neural model is only available on `v4.0`.**
+        For `v3.0 2022-08-31` or `v3.1 2023-07-31`, custom neural models can be trained for free for a maximum of 20 training sessions, with each session capped at 30 minutes of training duration. Once you use up all of the 20 training sessions, you can submit Azure support ticket to increase the training session limit. To increase the limit, two training sessions are considered as one training hour, and you're charged per two sessions / one training hour. For details on the prices, refer to the [pricing page]. For more information on ways to increase the limit, check [custom neural model billing section](train/custom-neural.md#billing). **For `v3.0` and `v3.1`, paid training feature is unavailable. Paid training feature for custom neural model is only available on `v4.0`.**
 
   - name: Storage account
     questions:
@@ -331,7 +331,7 @@ sections:
 
           If you intend to analyze your private storage account data by using FOTT, you must deploy the tool behind the virtual network or firewall.
 
-          Learn how to [create and use a managed identity for your Document Intelligence resource](managed-identities.md).
+          Learn how to [create and use a managed identity for your Document Intelligence resource](authentication/managed-identities.md).
 
   - name: Containers
     questions:

@@ -5,8 +5,6 @@ description: Learn how to label, and build a custom document classification mode
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: how-to
 ms.date: 05/23/2024
 ms.author: lajanuar
@@ -23,7 +21,7 @@ monikerRange: '>=doc-intel-3.0.0'
 > Custom classification model is currently in public preview. Features, approaches, and processes may change, prior to General Availability (GA), based on user feedback.
 >
 
-Custom classification models can classify each page in an input file to identify the document(s) within. Classifier models can also identify multiple documents or multiple instances of a single document in the input file. Document Intelligence custom models require as few as five training documents per document class to get started. To get started training a custom classification model, you need at least **five documents** for each class and **two classes** of documents.
+Custom classification models can classify each page in an input file to identify one or more documents within. Classifier models can also identify multiple documents or multiple instances of a single document in the input file. Document Intelligence custom models require as few as five training documents per document class to get started. To get started training a custom classification model, you need at least **five documents** for each class and **two classes** of documents.
 
 ## Custom classification model input requirements
 
@@ -53,9 +51,9 @@ The Document Intelligence Studio provides and orchestrates all the API calls req
 
     :::image type="content" source="../media/how-to/studio-create-classifier-project.png" alt-text="Screenshot of how to create a classifier project in the Document Intelligence Studio.":::
 
-    1. On the create project dialog, provide a name for your project, optionally a description, and select continue.
+    1. On the `Create Project` dialog, provide a name for your project, optionally a description, and select continue.
 
-    1. Next, choose or create a Document Intelligence resource before you select continue.
+    1. Next, choose, or select create a Document Intelligence resource before you continue.
 
     :::image type="content" source="../media/how-to/studio-select-resource.png" alt-text="Screenshot showing the project setup dialog window.":::
 
@@ -66,7 +64,7 @@ The Document Intelligence Studio provides and orchestrates all the API calls req
 
     :::image type="content" source="../media/how-to/studio-select-storage.png" alt-text="Screenshot showing how to select the Document Intelligence resource.":::
 
-1. **Training a custom classifier requires the output from the Layout model for each document in your dataset**. Run layout on all documents prior to the model training process.
+1. **Training a custom classifier requires the output from the Layout model for each document in your dataset**. Run layout on all documents before the model training process.
 
 1. Finally, review your project settings and select **Create Project** to create a new project. You should now be in the labeling window and see the files in your dataset listed.
 
@@ -80,7 +78,7 @@ You see the files you uploaded to storage in the file list, ready to be labeled.
 
 1. If the documents are organized in folders, the Studio prompts you to use the folder names as labels. This step simplifies your labeling down to a single select.
 
-1. To assign a label to a document, select on the add label selection mark to assign a label.
+1. To assign a label to a document, select on the `add label selection mark` to assign a label.
 
 1. Control select to  multi-select documents to assign a label
 
@@ -116,18 +114,18 @@ Once the model training is complete, you can test your model by selecting the mo
 
 The Studio orchestrates the API calls for you to train a custom classifier. The classifier training dataset requires the output from the layout API that matches the version of the API for your training model. Using layout results from an older API version can result in a model with lower accuracy.
 
-The Studio generates the layout results for your training dataset if the dataset doesn't contain layout results. When using the API or SDK to train a classifier, you need to add the layout results to the folders containing the individual documents. The layout results should be in the format of the API response when calling layout directly. The SDK object model is different, make sure that the `layout results` are the API results and not the `SDK response`.
+The Studio generates the layout results for your training dataset if the dataset doesn't contain layout results. When using the API or SDK to train a classifier, you need to add the layout results to the folders containing the individual documents. The layout results should be in the format of the API response when calling layout directly. The SDK object model is different. Make sure that the `layout results` are the API results and not the `SDK response`.
 
 ## Troubleshoot
 
-The [classification model](../concept-custom-classifier.md) requires results from the [layout model](../concept-layout.md) for each training document. If you don't provide the layout results, the Studio attempts to run the layout model for each document prior to training the classifier. This process is throttled and can result in a 429 response. 
+The [classification model](../train/custom-classifier.md) requires results from the [layout model](../prebuilt/layout.md) for each training document. If you don't provide the layout results, the Studio attempts to run the layout model for each document before training the classifier. This process is throttled and can result in a 429 response.
 
-In the Studio, prior to training with the classification model, run the [layout model](https://formrecognizer.appliedai.azure.com/studio/layout) on each document and upload it to the same location as the original document. Once the layout results are added, you can train the classifier model with your documents. 
+In the Studio, before training with the classification model, run the [layout model](https://formrecognizer.appliedai.azure.com/studio/layout) on each document and upload it to the same location as the original document. Once the layout results are added, you can train the classifier model with your documents.
 
 ## Next steps
 
 > [!div class="nextstepaction"]
-> [Learn about custom model types](../concept-custom.md)
+> [Learn about custom model types](../train/custom-model.md)
 
 > [!div class="nextstepaction"]
-> [Learn about accuracy and confidence with custom models](../concept-accuracy-confidence.md)
+> [Learn about accuracy and confidence with custom models](../concept/accuracy-confidence.md)

@@ -10,7 +10,7 @@ ms.date: 08/07/2024
 ms.author: lajanuar
 monikerRange: '<=doc-intel-4.0.0'
 ---
-
+<!-- markdownlint-disable DOCSMD006 -->
 
 # Build and train a custom extraction model
 
@@ -19,9 +19,9 @@ monikerRange: '<=doc-intel-4.0.0'
 [!INCLUDE [applies to v4.0 v3.1 v3.0](../includes/applies-to-v40-v31-v30.md)]   ![blue-checkmark](../media/blue-yes-icon.png) [v2.1](?view=doc-intel-2.1.0&preserve-view=true)
 
 > [!IMPORTANT]
-> Custom generative model training behavior is different from custom template and neural model training. The following document covers training only for custom template and neural models. For guidance on custom generative, refer to [custom generative model](../concept-custom-generative.md)
+> Custom generative model training behavior is different from custom template and neural model training. The following document covers training only for custom template and neural models. For guidance on custom generative, refer to [custom generative model](../train/custom-generative-extraction.md)
 
-Document Intelligence custom models require a handful of training documents to get started. If you have at least five documents, you can get started training a custom model. You can train either a [custom template model (custom form)](../concept-custom-template.md) or a [custom neural model (custom document)](../concept-custom-neural.md) or [custom template model (custom form)](../concept-custom-generative.md). This document walks you through the process of training the custom models.
+Document Intelligence custom models require a handful of training documents to get started. If you have at least five documents, you can get started training a custom model. You can train either a [custom template model (custom form)](../train/custom-template.md) or a [custom neural model (custom document)](../train/custom-neural.md) or [custom template model (custom form)](../train/custom-generative-extraction.md). This document walks you through the process of training the custom models.
 
 ## Custom model input requirements
 
@@ -63,7 +63,7 @@ The Document Intelligence Studio provides and orchestrates all the API calls req
     1. On the next step in the workflow, choose or create a Document Intelligence resource before you select continue.
 
     > [!IMPORTANT]
-    > Custom neural models are only available in a few regions. If you plan on training a neural model, please select or create a resource in one of [these supported regions](../concept-custom-neural.md#supported-regions).
+    > Custom neural models are only available in a few regions. If you plan on training a neural model, please select or create a resource in one of [these supported regions](../train/custom-neural.md#supported-regions).
 
     :::image type="content" source="../media/how-to/studio-custom-configure-resource.png" alt-text="Screenshot of Select the Document Intelligence resource.":::
 
@@ -99,7 +99,7 @@ With your dataset labeled, you're now ready to train your model. Select the trai
 
 1. On the train model dialog, provide a unique model ID and, optionally, a description. The model ID accepts a string data type.
 
-1. For the build mode, select the type of model you want to train. Learn more about the [model types and capabilities](../concept-custom.md).
+1. For the build mode, select the type of model you want to train. Learn more about the [model types and capabilities](../train/custom-model.md).
 
     :::image type="content" source="../media/how-to/studio-train-model.png" alt-text="Screenshot of Train model dialog.":::
 
@@ -192,10 +192,10 @@ Now that you learned how to build a training data set, follow a quickstart to tr
 :::moniker range=">=doc-intel-3.0.0"
 
 > [!div class="nextstepaction"]
-> [Learn about custom model types](../concept-custom.md)
+> [Learn about custom model types](../train/custom-model.md)
 
 > [!div class="nextstepaction"]
-> [Learn about accuracy and confidence with custom models](../concept-accuracy-confidence.md)
+> [Learn about accuracy and confidence with custom models](../concept/accuracy-confidence.md)
 :::moniker-end
 
 :::moniker range="doc-intel-2.1.0"
@@ -207,5 +207,5 @@ Now that you learned how to build a training data set, follow a quickstart to tr
 ### See also
 
 * [Train a model and extract document data using the client library or REST API](../quickstarts/get-started-sdks-rest-api.md)
-* [Custom generative model](../concept-custom-generative.md)
+* [Custom generative model](../train/custom-generative-extraction.md)
 * [What is Document Intelligence?](../overview.md)

@@ -5,8 +5,6 @@ description: How to build and train a custom generative AI model with AI Studio
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: overview
 ms.date: 08/07/2024
 ms.author: lajanuar
@@ -33,7 +31,7 @@ You can choose one of the following options to authorize access to your Document
 
 **✔️ Managed Identity**. A managed identity is a service principal that creates a Microsoft Entra identity and specific permissions for an Azure managed resource. Managed identities enable you to run your Document Intelligence application without having to embed credentials in your code. Managed identities are a safer way to grant access to storage data and replace the requirement for you to include shared access signature tokens (SAS) with your source and result URLs.
 
-To learn more, *see* [Managed identities for Document Intelligence](../managed-identities.md).
+To learn more, *see* [Managed identities for Document Intelligence](../authentication/managed-identities.md).
 
   :::image type="content" source="../media/managed-identities/rbac-flow.png" alt-text="Screenshot of managed identity flow (role-based access control).":::
 
@@ -47,7 +45,7 @@ To learn more, *see* [Managed identities for Document Intelligence](../managed-i
 * Your **source** container or blob must designate **read**, **write**, **list**, and **delete** access.
 * Your **result** container or blob must designate **write**, **list**, **delete** access.
 
-To learn more, *see* [**Create SAS tokens**](../create-sas-tokens.md).
+To learn more, *see* [**Create SAS tokens**](../authentication/create-sas-tokens.md).
 
 ### Training data
 
@@ -81,7 +79,7 @@ Once you have your Azure blob storage containers, upload your training data to y
 
     :::image type="content" source="../media/custom-generative-model/create-document-extraction-project.png" alt-text="Screenshot of the create document extraction project overview page.":::
 
-1. Next, select the storage account you used to upload your custom model training dataset. 
+1. Next, select the storage account you used to upload your custom model training dataset.
 
     :::image type="content" source="../media/custom-generative-model/create-document-extraction-data-settings.png" alt-text="Screenshot of the document extraction project data settings page.":::
 
@@ -134,6 +132,6 @@ That's it! You learned to train a custom generative model in the Azure AI Studio
 
 ## Next steps
 
-[Learn more about the custom generative model](../concept-custom-generative.md)
+[Learn more about the custom generative model](../train/custom-generative-extraction.md)
 
-[Learn more about custom model accuracy and confidence](../concept-accuracy-confidence.md)
+[Learn more about custom model accuracy and confidence](../concept/accuracy-confidence.md)

@@ -10,9 +10,10 @@ ms.date: 08/07/2024
 ms.author: lajanuar
 ---
 
-
 # Compose custom models
 
+<!-- markdownlint-disable MD049 --> emphasis style
+
 <!-- markdownlint-disable MD051 -->
 <!-- markdownlint-disable MD024 -->
 
@@ -35,11 +36,11 @@ ms.author: lajanuar
 ::: moniker range=">=doc-intel-3.0.0"
 
 > [!IMPORTANT]
-> Model compose behavior is changing for api-version=2024-07-31-preview and later, for more info refer to [composed custom models](../concept-composed-models.md). The following behavior only applies to v3.1 and previous versions
+> Model compose behavior is changing for api-version=2024-07-31-preview and later, for more info refer to [composed custom models](../train/composed-models.md). The following behavior only applies to v3.1 and previous versions
 
 A composed model is created by taking a collection of custom models and assigning them to a single model ID. You can assign up to 200 trained custom models to a single composed model ID. When a document is submitted to a composed model, the service performs a classification step to decide which custom model accurately represents the form presented for analysis. Composed models are useful when you train several models and want to group them to analyze similar form types. For example, your composed model might include custom models trained to analyze your supply, equipment, and furniture purchase orders. Instead of manually trying to select the appropriate model, you can use a composed model to determine the appropriate custom model for each analysis and extraction.
 
-To learn more, see [Composed custom models](../concept-composed-models.md).
+To learn more, see [Composed custom models](../train/composed-models.md).
 
 In this article, you learn how to create and use composed custom models to analyze your forms and documents.
 
@@ -55,10 +56,10 @@ To get started, you need the following resources:
 
   1. Copy the **Keys and Endpoint** values from the Azure portal and paste them in a convenient location, such as *Microsoft Notepad*. You need the key and endpoint values to connect your application to the Document Intelligence API.
 
-    :::image type="content" source="../media/containers/keys-and-endpoint.png" alt-text="Still photo showing how to access resource key and endpoint URL.":::
+:::image type="content" source="../media/containers/keys-and-endpoint.png" alt-text="Still photo showing how to access resource key and endpoint URL.":::
 
-    > [!TIP]
-    > For more information, see [**create a Document Intelligence resource**](../create-document-intelligence-resource.md).
+  > [!TIP]
+  > For more information, see [**create a Document Intelligence resource**](../how-to-guides/create-document-intelligence-resource.md).
 
 * **An Azure storage account.** If you don't know how to create an Azure storage account, follow the [Azure Storage quickstart for Azure portal](/azure/storage/blobs/storage-quickstart-blobs-portal). You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.
 
@@ -116,7 +117,7 @@ While creating your custom models, you may need to extract data collections from
 
 * Specific collection of values for a given set of fields (columns and/or rows)
 
-See [Document Intelligence Studio: labeling as tables](../concept-custom-label.md#tabular-fields)
+See [Document Intelligence Studio: labeling as tables](../train/custom-labels.md#tabular-fields)
 
 ### [REST API](#tab/rest)
 
@@ -139,11 +140,10 @@ Training with labels leads to better performance in some scenarios. To train wit
 |**C#**|**StartBuildModel**|
 |**Java**| [**beginBuildModel**](/java/api/com.azure.ai.formrecognizer.documentanalysis.administration.documentmodeladministrationclient.beginbuildmodel)|
 |**JavaScript** | [**beginBuildModel**](/javascript/api/@azure/ai-form-recognizer/documentmodeladministrationclient?view=azure-node-latest#@azure-ai-form-recognizer-documentmodeladministrationclient-beginbuildmodel&preserve-view=true)|
-| **Python** | [**begin_build_document_model**](/python/api/azure-ai-formrecognizer/azure.ai.formrecognizer.documentmodeladministrationclient?view=azure-python#azure-ai-formrecognizer-documentmodeladministrationclient-begin-build-document-model&preserve-view=true)
+| **Python** | [**begin_build_document_model**](/python/api/azure-ai-formrecognizer/azure.ai.formrecognizer.documentmodeladministrationclient?view=azure-python#azure-ai-formrecognizer-documentmodeladministrationclient-begin-build-document-model&preserve-view=true)|
 
 ---
 
-
 ## Create a composed model
 
 > [!NOTE]
@@ -236,21 +236,21 @@ You can use the programming language of your choice to create a composed model:
 
 | Programming language| Code sample |
 |--|--|
-|**C#** | [Model compose](https://github.com/Azure/azure-sdk-for-net/blob/Azure.AI.FormRecognizer_4.0.0/sdk/formrecognizer/Azure.AI.FormRecognizer/samples/Sample_ModelCompose.md)
-|**Java** | [Model compose](https://github.com/Azure/azure-sdk-for-java/blob/afa0d44fa42979ae9ad9b92b23cdba493a562127/sdk/formrecognizer/azure-ai-formrecognizer/src/samples/java/com/azure/ai/formrecognizer/administration/ComposeDocumentModel.java)
-|**JavaScript** | [Compose model](https://github.com/witemple-msft/azure-sdk-for-js/blob/7e3196f7e529212a6bc329f5f06b0831bf4cc174/sdk/formrecognizer/ai-form-recognizer/samples/v4/javascript/composeModel.js)
-|**Python** | [Create composed model](https://github.com/Azure/azure-sdk-for-python/blob/azure-ai-formrecognizer_3.3.0/sdk/formrecognizer/azure-ai-formrecognizer/samples/v3.2_and_later/sample_compose_model.py)
+|**C#** | [Model compose](https://github.com/Azure/azure-sdk-for-net/blob/Azure.AI.FormRecognizer_4.0.0/sdk/formrecognizer/Azure.AI.FormRecognizer/samples/Sample_ModelCompose.md)|
+|**Java** | [Model compose](https://github.com/Azure/azure-sdk-for-java/blob/afa0d44fa42979ae9ad9b92b23cdba493a562127/sdk/formrecognizer/azure-ai-formrecognizer/src/samples/java/com/azure/ai/formrecognizer/administration/ComposeDocumentModel.java)|
+|**JavaScript** | [Compose model](https://github.com/witemple-msft/azure-sdk-for-js/blob/7e3196f7e529212a6bc329f5f06b0831bf4cc174/sdk/formrecognizer/ai-form-recognizer/samples/v4/javascript/composeModel.js)|
+|**Python** | [Create composed model](https://github.com/Azure/azure-sdk-for-python/blob/azure-ai-formrecognizer_3.3.0/sdk/formrecognizer/azure-ai-formrecognizer/samples/v3.2_and_later/sample_compose_model.py)|
 
 #### Analyze documents
 
 Once you build your composed model, you can use it to analyze forms and documents. Use your composed `model ID` and let the service decide which of your aggregated custom models fits best according to the document provided.
 
 |Programming language| Code sample |
 |--|--|
-|**C#** | [Analyze a document with a custom/composed model using model ID](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/formrecognizer/Azure.AI.FormRecognizer/samples/Sample_AnalyzeWithCustomModel.md)
-|**Java** | [Analyze a document with a custom/composed model using model ID](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/formrecognizer/azure-ai-formrecognizer/src/samples/java/com/azure/ai/formrecognizer/AnalyzeCustomDocumentFromUrl.java)
-|**JavaScript** | [Analyze a document with a custom/composed model using model ID](https://github.com/witemple-msft/azure-sdk-for-js/blob/7e3196f7e529212a6bc329f5f06b0831bf4cc174/sdk/formrecognizer/ai-form-recognizer/samples/v4/javascript/analyzeDocumentByModelId.js)
-|**Python** | [Analyze a document with a custom/composed model using model ID](https://github.com/Azure/azure-sdk-for-python/blob/azure-ai-formrecognizer_3.3.0/sdk/formrecognizer/azure-ai-formrecognizer/samples/v3.2_and_later/sample_analyze_custom_documents.py)
+|**C#** | [Analyze a document with a custom/composed model using model ID](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/formrecognizer/Azure.AI.FormRecognizer/samples/Sample_AnalyzeWithCustomModel.md)|
+|**Java** | [Analyze a document with a custom/composed model using model ID](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/formrecognizer/azure-ai-formrecognizer/src/samples/java/com/azure/ai/formrecognizer/AnalyzeCustomDocumentFromUrl.java)|
+|**JavaScript** | [Analyze a document with a custom/composed model using model ID](https://github.com/witemple-msft/azure-sdk-for-js/blob/7e3196f7e529212a6bc329f5f06b0831bf4cc174/sdk/formrecognizer/ai-form-recognizer/samples/v4/javascript/analyzeDocumentByModelId.js)|
+|**Python** | [Analyze a document with a custom/composed model using model ID](https://github.com/Azure/azure-sdk-for-python/blob/azure-ai-formrecognizer_3.3.0/sdk/formrecognizer/azure-ai-formrecognizer/samples/v3.2_and_later/sample_analyze_custom_documents.py)|
 
 #### Manage your composed models
 
@@ -318,11 +318,11 @@ In the Document Intelligence UI:
 
 1. Select **Use Custom to train a model with labels and get key value pairs**.
 
-      :::image type="content" source="../media/label-tool/fott-use-custom.png" alt-text="Screenshot of the `FOTT` tool select custom model option.":::
+  :::image type="content" source="../media/label-tool/fott-use-custom.png" alt-text="Screenshot of the `FOTT` tool select custom model option.":::
 
 1. In the next window, select **New project**:
 
-    :::image type="content" source="../media/label-tool/fott-new-project.png" alt-text="Screenshot of the `FOTT` tool select new project option.":::
+:::image type="content" source="../media/label-tool/fott-new-project.png" alt-text="Screenshot of the `FOTT` tool select new project option.":::
 
 ## Create your models
 
@@ -350,7 +350,7 @@ You [train your model](build-a-custom-model.md?view=doc-intel-2.1.0&preserve-vie
 
 When you train with labeled data, the model uses supervised learning to extract values of interest, using the labeled forms you provide. Labeled data results in better-performing models and can produce models that work with complex forms or forms containing values without keys.
 
-Document Intelligence uses the [Layout](../concept-layout.md) API to learn the expected sizes and positions of typeface and handwritten text elements and extract tables. Then it uses user-specified labels to learn the key/value associations and tables in the documents. We recommend that you use five manually labeled forms of the same type (same structure) to get started when training a new model. Add more labeled data as needed to improve the model accuracy. Document Intelligence enables training a model to extract key value pairs and tables using supervised learning capabilities.
+Document Intelligence uses the [Layout](../prebuilt/layout.md) API to learn the expected sizes and positions of typeface and handwritten text elements and extract tables. Then it uses user-specified labels to learn the key/value associations and tables in the documents. We recommend that you use five manually labeled forms of the same type (same structure) to get started when training a new model. Add more labeled data as needed to improve the model accuracy. Document Intelligence enables training a model to extract key value pairs and tables using supervised learning capabilities.
 
 [Get started with Train with labels](../label-tool.md)
 
@@ -440,12 +440,10 @@ Use the programming language code of your choice to create a composed model that
 
 ---
 
-
 ## Analyze documents with your custom or composed model
 
  The custom form **Analyze** operation requires you to provide the `modelID`  in the call to Document Intelligence. You can provide a single custom model ID or a composed model ID for the `modelID` parameter.
 
-
 ### [**Document Intelligence Sample Labeling tool**](#tab/studio)
 
 1. On the tool left-pane menu, select the **`Analyze` icon** (light bulb).
@@ -476,7 +474,6 @@ Using the programming language of your choice to analyze a form or document with
 
 ---
 
-
 Test your newly trained models by [analyzing forms](build-a-custom-model.md?view=doc-intel-2.1.0&preserve-view=true#test-the-model) that weren't part of the training dataset. Depending on the reported accuracy, you may want to do further training to improve the model. You can continue further training to [improve results](../label-tool.md#improve-results).
 
 ## Manage your custom models

@@ -5,21 +5,19 @@ description: Create a Document Intelligence resource in the Azure portal.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: how-to
-ms.date: 09/26/2024
+ms.date: 10/08/2024
 ms.author: lajanuar
 ---
 
 
 # Create a Document Intelligence resource
 
 ::: moniker range="<=doc-intel-4.0.0"
- [!INCLUDE [applies to v4.0 v3.1 v3.0 v2.1](includes/applies-to-v40-v31-v30-v21.md)]
+ [!INCLUDE [applies to v4.0 v3.1 v3.0 v2.1](../includes/applies-to-v40-v31-v30-v21.md)]
 ::: moniker-end
 
-Azure AI Document Intelligence is a cloud-based [Azure AI service](../../ai-services/index.yml) that uses machine-learning models to extract key-value pairs, text, and tables from your documents. In this article, learn how to create a Document Intelligence resource in the Azure portal.
+Azure AI Document Intelligence is a cloud-based [Azure AI service](../../../ai-services/index.yml) that uses machine-learning models to extract key-value pairs, text, and tables from your documents. In this article, learn how to create a Document Intelligence resource in the Azure portal.
 
 ## Visit the Azure portal
 
@@ -47,7 +45,7 @@ Let's get started:
 
 1. Select **Review + Create**.
 
-    :::image type="content" source="media/logic-apps-tutorial/logic-app-connector-demo-two.png" alt-text="Still image showing the correct values for creating Document Intelligence resource.":::
+    :::image type="content" source="../media/logic-apps-tutorial/logic-app-connector-demo-two.png" alt-text="Still image showing the correct values for creating Document Intelligence resource.":::
 
 1. Azure will run a quick validation check, after a few seconds you should see a green banner that says **Validation Passed**.
 
@@ -63,17 +61,17 @@ Let's get started:
 
 1. If your overview page doesn't have the keys and endpoint visible, you can select the **Keys and Endpoint** button, on the left navigation bar, and retrieve them there.
 
-    :::image border="true" type="content" source="media/containers/keys-and-endpoint.png" alt-text="Still photo showing how to access resource key and endpoint URL":::
+    :::image border="true" type="content" source="../media/containers/keys-and-endpoint.png" alt-text="Still photo showing how to access resource key and endpoint URL.":::
 
 That's it! You're now ready to start automating data extraction using Azure AI Document Intelligence.
 
 ## Next steps
 
-* Try the [Document Intelligence Studio](concept-document-intelligence-studio.md), an online tool for visually exploring, understanding, and integrating features from the Document Intelligence service into your applications.
+* Try the [Document Intelligence Studio](../concept-document-intelligence-studio.md), an online tool for visually exploring, understanding, and integrating features from the Document Intelligence service into your applications.
 
 * Complete a Document Intelligence quickstart and get started creating a document processing app in the development language of your choice:
 
-  * [C#](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true)
-  * [Python](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true)
-  * [Java](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true)
-  * [JavaScript](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true)
+  * [C#](../quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true)
+  * [Python](../quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true)
+  * [Java](../quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true)
+  * [JavaScript](../quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true)

@@ -5,10 +5,8 @@ description: Learn how to use the copy model API to back up your Document Intell
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: how-to
-ms.date: 08/07/2024
+ms.date: 10/15/2024
 ms.author: lajanuar
 ---
 
@@ -18,19 +16,19 @@ ms.author: lajanuar
 # Disaster recovery
 
 ::: moniker range="doc-intel-4.0.0"
-[!INCLUDE [applies to v4.0](includes/applies-to-v40.md)]
+[!INCLUDE [applies to v4.0](../includes/applies-to-v40.md)]
 ::: moniker-end
 
 ::: moniker range="doc-intel-3.1.0"
-[!INCLUDE [applies to v3.1](includes/applies-to-v31.md)]
+[!INCLUDE [applies to v3.1](../includes/applies-to-v31.md)]
 ::: moniker-end
 
 ::: moniker range="doc-intel-3.0.0"
-[!INCLUDE [applies to v3.0](includes/applies-to-v30.md)]
+[!INCLUDE [applies to v3.0](../includes/applies-to-v30.md)]
 ::: moniker-end
 
 ::: moniker range="doc-intel-2.1.0"
-[!INCLUDE [applies to v2.1](includes/applies-to-v21.md)]
+[!INCLUDE [applies to v2.1](../includes/applies-to-v21.md)]
 ::: moniker-end
 
 ::: moniker range=">= doc-intel-2.1.0"
@@ -40,7 +38,7 @@ When you create a Document Intelligence resource in the Azure portal, you specif
 The Copy API enables this scenario by allowing you to copy custom models and classifiers from one Document Intelligence account or into others, which can exist in any supported geographical region. This guide shows you how to use the Copy REST API with cURL for custom models. You can also use an HTTP request service to issue the requests.
 
 > [!NOTE]
-> Starting with the `2024-07-31-preview` API, custom clasification models also support the Copy API. This guide specifically uses custom models to copy models. For classifier model copy, follow this [guide](concept-custom-classifier.md#copy-a-model).
+> Starting with the `2024-07-31-preview` API, custom clasification models also support the Copy API. This guide specifically uses custom models to copy models. For classifier model copy, follow this [guide](../train/custom-classifier.md#copy-a-model).
 
 ## Business scenarios