@@ -71,7 +71,7 @@ sections:
 
           For more information, see [overview of RAG in Document Intelligence](concept/retrieval-augmented-generation.md)
 
-  - name: Document Intelligence Studio
+  - name: Document Intelligence/Content Understanding Studio
     questions:
       - question: |
           Do I need specific permissions to access Document Intelligence Studio?

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: include
-ms.date: 11/18/2025
+ms.date: 01/30/2026
 ms.author: lajanuar
 monikerRange: ">=doc-intel-3.0.0"
 ---
@@ -190,7 +190,7 @@ Extract text, selection marks, text styles, table structures, and bounding regio
 >
 > * For this example, you'll need a **document file from a URI**. You can use our [sample document](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf) for this quickstart.
 > * We've added the file URI value to the `Uri fileUri` variable at the top of the script.
-> * To extract the layout from a given file at a URI, use the `StartAnalyzeDocumentFromUri` method and pass `prebuilt-layout` as the model ID. The returned value is an `AnalyzeResult` object containing data from the submitted document.
+> * To extract the layout from a given file at a URI, use the `AnalyzeDocumentAsync` method and pass `prebuilt-layout` as the model ID. The returned value is an `AnalyzeResult` object containing data from the submitted document.
 
 :::moniker range="doc-intel-4.0.0"
 
@@ -625,7 +625,7 @@ Analyze and extract common fields from specific document types using a prebuilt
 >
 > * Analyze an invoice using the prebuilt-invoice model. You can use our [sample invoice document](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf) for this quickstart.
 > * We've added the file URI value to the `Uri invoiceUri` variable at the top of the Program.cs file.
-> * To analyze a given file at a URI, use the `StartAnalyzeDocumentFromUri` method and pass `prebuilt-invoice` as the model ID. The returned value is an `AnalyzeResult` object containing data from the submitted document.
+> * To analyze a given file at a URI, use the `AnalyzeDocumentAsync` method and pass `prebuilt-invoice` as the model ID. The returned value is an `AnalyzeResult` object containing data from the submitted document.
 > * For simplicity, all the key-value pairs that the service returns are not shown here. To see the list of all supported fields and corresponding types, see our [Invoice](../../prebuilt/invoice.md#field-extraction) concept page.
 
 :::moniker range="doc-intel-4.0.0"
@@ -644,9 +644,14 @@ AzureKeyCredential credential = new AzureKeyCredential(key);
 DocumentIntelligenceClient client = new DocumentIntelligenceClient(new Uri(endpoint), credential);
 
 //sample invoice document
-Uri uriSource = new Uri("https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf");
+Uri invoiceUri = new Uri("https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf");
 
-Operation<AnalyzeResult> operation = await client.AnalyzeDocumentAsync(WaitUntil.Completed, "prebuilt-invoice", uriSource);
+AnalyzeDocumentContent content = new AnalyzeDocumentContent()
+{
+    UrlSource = invoiceUri
+};
+
+Operation<AnalyzeResult> operation = await client.AnalyzeDocumentAsync(WaitUntil.Completed, "prebuilt-invoice", content);
 
 AnalyzeResult result = operation.Value;
 

@@ -255,7 +255,7 @@ Extract text, selection marks, text styles, table structures, and bounding regio
 > [!div class="checklist"]
 >
 > * For this example, you'll need a **document file at a URI**. You can use our [sample document](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf) for this quickstart.
-> * To analyze a given file at a URI, you'll use the `beginAnalyzeDocumentFromUrl` method and pass `prebuilt-layout` as the model Id. The returned value is an `AnalyzeResult` object containing data about the submitted document.
+> * To analyze a given file at a URI, you'll use the `beginAnalyzeDocument` method and pass `prebuilt-layout` as the model Id. The returned value is an `AnalyzeResult` object containing data about the submitted document.
 > * We've added the file URI value to the `documentUrl` variable in the main method.
 
 :::moniker range="doc-intel-4.0.0"
@@ -630,16 +630,17 @@ Analyze and extract common fields from specific document types using a prebuilt
 
 ```java
 
-import com.azure.ai.documentintelligence.models.AnalyzeDocumentRequest;
+import com.azure.ai.documentintelligence.DocumentIntelligenceClient;
+import com.azure.ai.documentintelligence.DocumentIntelligenceClientBuilder;
+import com.azure.ai.documentintelligence.models.AnalyzeDocumentOptions;
+import com.azure.ai.documentintelligence.models.AnalyzeOperationDetails;
 import com.azure.ai.documentintelligence.models.AnalyzeResult;
-import com.azure.ai.documentintelligence.models.AnalyzeResultOperation;
-import com.azure.ai.documentintelligence.models.Document;
+import com.azure.ai.documentintelligence.models.AnalyzedDocument;
 import com.azure.ai.documentintelligence.models.DocumentField;
 import com.azure.ai.documentintelligence.models.DocumentFieldType;
 import com.azure.core.credential.AzureKeyCredential;
 import com.azure.core.util.polling.SyncPoller;
 
-import java.io.IOException;
 import java.time.LocalDate;
 import java.util.List;
 import java.util.Map;
@@ -652,140 +653,130 @@ public class DocIntelligence {
 
   public static void main(String[] args) {
 
+    // create your `DocumentIntelligenceClient` instance and `AzureKeyCredential` variable
+    DocumentIntelligenceClient client = new DocumentIntelligenceClientBuilder()
+      .credential(new AzureKeyCredential(key))
+      .endpoint(endpoint)
+      .buildClient();
+
     // sample document
     String modelId = "prebuilt-invoice";
     String invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf";
 
-    public static void main(final String[] args) throws IOException {
-
-      // Instantiate a client that will be used to call the service.
-      DocumentIntelligenceClient client = new DocumentIntelligenceClientBuilder()
-        .credential(new AzureKeyCredential(key))
-        .endpoint(endpoint)
-        .buildClient();
-
-      SyncPoller<AnalyzeResultOperation, AnalyzeResultOperation > analyzeInvoicesPoller =
-        client.beginAnalyzeDocument(modelId, 
-            null,
-            null,
-            null,
-            null,
-            null,
-            null,
-            new AnalyzeDocumentRequest().setUrlSource(invoiceUrl));
-
-      AnalyzeResult analyzeInvoiceResult = analyzeInvoicesPoller.getFinalResult().getAnalyzeResult();
-
-      for (int i = 0; i < analyzeInvoiceResult.getDocuments().size(); i++) {
-        Document analyzedInvoice = analyzeInvoiceResult.getDocuments().get(i);
-        Map < String, DocumentField > invoiceFields = analyzedInvoice.getFields();
-        System.out.printf("----------- Analyzing invoice  %d -----------%n", i);
-        DocumentField vendorNameField = invoiceFields.get("VendorName");
-        if (vendorNameField != null) {
-          if (DocumentFieldType.STRING == vendorNameField.getType()) {
-            String merchantName = vendorNameField.getValueString();
-            System.out.printf("Vendor Name: %s, confidence: %.2f%n",
-              merchantName, vendorNameField.getConfidence());
-          }
+    SyncPoller<AnalyzeOperationDetails, AnalyzeResult> analyzeInvoicesPoller =
+      client.beginAnalyzeDocument(modelId, new AnalyzeDocumentOptions(invoiceUrl));
+
+    AnalyzeResult analyzeInvoiceResult = analyzeInvoicesPoller.getFinalResult();
+
+    for (int i = 0; i < analyzeInvoiceResult.getDocuments().size(); i++) {
+      AnalyzedDocument analyzedInvoice = analyzeInvoiceResult.getDocuments().get(i);
+      Map<String, DocumentField> invoiceFields = analyzedInvoice.getFields();
+      System.out.printf("----------- Analyzing invoice  %d -----------%n", i);
+      DocumentField vendorNameField = invoiceFields.get("VendorName");
+      if (vendorNameField != null) {
+        if (DocumentFieldType.STRING == vendorNameField.getType()) {
+          String merchantName = vendorNameField.getValueAsString();
+          System.out.printf("Vendor Name: %s, confidence: %.2f%n",
+            merchantName, vendorNameField.getConfidence());
         }
+      }
 
-        DocumentField vendorAddressField = invoiceFields.get("VendorAddress");
-        if (vendorAddressField != null) {
-          if (DocumentFieldType.STRING == vendorAddressField.getType()) {
-            String merchantAddress = vendorAddressField.getValueString();
-            System.out.printf("Vendor address: %s, confidence: %.2f%n",
-              merchantAddress, vendorAddressField.getConfidence());
-          }
+      DocumentField vendorAddressField = invoiceFields.get("VendorAddress");
+      if (vendorAddressField != null) {
+        if (DocumentFieldType.STRING == vendorAddressField.getType()) {
+          String merchantAddress = vendorAddressField.getValueAsString();
+          System.out.printf("Vendor address: %s, confidence: %.2f%n",
+            merchantAddress, vendorAddressField.getConfidence());
         }
+      }
 
-        DocumentField customerNameField = invoiceFields.get("CustomerName");
-        if (customerNameField != null) {
-          if (DocumentFieldType.STRING == customerNameField.getType()) {
-            String merchantAddress = customerNameField.getValueString();
-            System.out.printf("Customer Name: %s, confidence: %.2f%n",
-              merchantAddress, customerNameField.getConfidence());
-          }
+      DocumentField customerNameField = invoiceFields.get("CustomerName");
+      if (customerNameField != null) {
+        if (DocumentFieldType.STRING == customerNameField.getType()) {
+          String merchantAddress = customerNameField.getValueAsString();
+          System.out.printf("Customer Name: %s, confidence: %.2f%n",
+            merchantAddress, customerNameField.getConfidence());
         }
+      }
 
-        DocumentField customerAddressRecipientField = invoiceFields.get("CustomerAddressRecipient");
-        if (customerAddressRecipientField != null) {
-          if (DocumentFieldType.STRING == customerAddressRecipientField.getType()) {
-            String customerAddr = customerAddressRecipientField.getValueString();
-            System.out.printf("Customer Address Recipient: %s, confidence: %.2f%n",
-              customerAddr, customerAddressRecipientField.getConfidence());
-          }
+      DocumentField customerAddressRecipientField = invoiceFields.get("CustomerAddressRecipient");
+      if (customerAddressRecipientField != null) {
+        if (DocumentFieldType.STRING == customerAddressRecipientField.getType()) {
+          String customerAddr = customerAddressRecipientField.getValueAsString();
+          System.out.printf("Customer Address Recipient: %s, confidence: %.2f%n",
+            customerAddr, customerAddressRecipientField.getConfidence());
         }
+      }
 
-        DocumentField invoiceIdField = invoiceFields.get("InvoiceId");
-        if (invoiceIdField != null) {
-          if (DocumentFieldType.STRING == invoiceIdField.getType()) {
-            String invoiceId = invoiceIdField.getValueString();
-            System.out.printf("Invoice ID: %s, confidence: %.2f%n",
-              invoiceId, invoiceIdField.getConfidence());
-          }
+      DocumentField invoiceIdField = invoiceFields.get("InvoiceId");
+      if (invoiceIdField != null) {
+        if (DocumentFieldType.STRING == invoiceIdField.getType()) {
+          String invoiceId = invoiceIdField.getValueAsString();
+          System.out.printf("Invoice ID: %s, confidence: %.2f%n",
+            invoiceId, invoiceIdField.getConfidence());
         }
+      }
 
-        DocumentField invoiceDateField = invoiceFields.get("InvoiceDate");
-        if (customerNameField != null) {
-          if (DocumentFieldType.DATE == invoiceDateField.getType()) {
-            LocalDate invoiceDate = invoiceDateField.getValueDate();
-            System.out.printf("Invoice Date: %s, confidence: %.2f%n",
-              invoiceDate, invoiceDateField.getConfidence());
-          }
+      DocumentField invoiceDateField = invoiceFields.get("InvoiceDate");
+      if (invoiceDateField != null) {
+        if (DocumentFieldType.DATE == invoiceDateField.getType()) {
+          LocalDate invoiceDate = invoiceDateField.getValueAsDate();
+          System.out.printf("Invoice Date: %s, confidence: %.2f%n",
+            invoiceDate, invoiceDateField.getConfidence());
         }
+      }
 
-        DocumentField invoiceTotalField = invoiceFields.get("InvoiceTotal");
-        if (customerAddressRecipientField != null) {
-          if (DocumentFieldType.NUMBER == invoiceTotalField.getType()) {
-            Double invoiceTotal = invoiceTotalField.getValueNumber();
-            System.out.printf("Invoice Total: %.2f, confidence: %.2f%n",
-              invoiceTotal, invoiceTotalField.getConfidence());
-          }
+      DocumentField invoiceTotalField = invoiceFields.get("InvoiceTotal");
+      if (invoiceTotalField != null) {
+        if (DocumentFieldType.DOUBLE == invoiceTotalField.getType()) {
+          Double invoiceTotal = invoiceTotalField.getValueAsDouble();
+          System.out.printf("Invoice Total: %.2f, confidence: %.2f%n",
+            invoiceTotal, invoiceTotalField.getConfidence());
         }
+      }
 
-        DocumentField invoiceItemsField = invoiceFields.get("Items");
-        if (invoiceItemsField != null) {
-          System.out.printf("Invoice Items: %n");
-          if (DocumentFieldType.ARRAY == invoiceItemsField.getType()) {
-            List < DocumentField > invoiceItems = invoiceItemsField.getValueArray();
-            invoiceItems.stream()
-              .filter(invoiceItem -> DocumentFieldType.OBJECT == invoiceItem.getType())
-              .map(documentField -> documentField.getValueObject())
-              .forEach(documentFieldMap -> documentFieldMap.forEach((key, documentField) -> {
-
-                // See a full list of fields found on an invoice here:
-                // https://aka.ms/documentintelligence/invoicefields
-
-                if ("Description".equals(key)) {
-                  if (DocumentFieldType.STRING == documentField.getType()) {
-                    String name = documentField.getValueString();
-                    System.out.printf("Description: %s, confidence: %.2fs%n",
-                      name, documentField.getConfidence());
-                  }
+      DocumentField invoiceItemsField = invoiceFields.get("Items");
+      if (invoiceItemsField != null) {
+        System.out.printf("Invoice Items: %n");
+        if (DocumentFieldType.LIST == invoiceItemsField.getType()) {
+          List<DocumentField> invoiceItems = invoiceItemsField.getValueAsList();
+          invoiceItems.stream()
+            .filter(invoiceItem -> DocumentFieldType.MAP == invoiceItem.getType())
+            .map(documentField -> documentField.getValueAsMap())
+            .forEach(documentFieldMap -> documentFieldMap.forEach((key, documentField) -> {
+
+              // See a full list of fields found on an invoice here:
+              // https://aka.ms/documentintelligence/invoicefields
+
+              if ("Description".equals(key)) {
+                if (DocumentFieldType.STRING == documentField.getType()) {
+                  String name = documentField.getValueAsString();
+                  System.out.printf("Description: %s, confidence: %.2fs%n",
+                    name, documentField.getConfidence());
                 }
-                if ("Quantity".equals(key)) {
-                  if (DocumentFieldType.NUMBER == documentField.getType()) {
-                    Double quantity = documentField.getValueNumber();
-                    System.out.printf("Quantity: %f, confidence: %.2f%n",
-                      quantity, documentField.getConfidence());
-                  }
+              }
+              if ("Quantity".equals(key)) {
+                if (DocumentFieldType.DOUBLE == documentField.getType()) {
+                  Double quantity = documentField.getValueAsDouble();
+                  System.out.printf("Quantity: %f, confidence: %.2f%n",
+                    quantity, documentField.getConfidence());
                 }
-                if ("UnitPrice".equals(key)) {
-                  if (DocumentFieldType.NUMBER == documentField.getType()) {
-                    Double unitPrice = documentField.getValueNumber();
-                    System.out.printf("Unit Price: %f, confidence: %.2f%n",
-                      unitPrice, documentField.getConfidence());
-                  }
+              }
+              if ("UnitPrice".equals(key)) {
+                if (DocumentFieldType.DOUBLE == documentField.getType()) {
+                  Double unitPrice = documentField.getValueAsDouble();
+                  System.out.printf("Unit Price: %f, confidence: %.2f%n",
+                    unitPrice, documentField.getConfidence());
                 }
-                if ("ProductCode".equals(key)) {
-                  if (DocumentFieldType.NUMBER == documentField.getType()) {
-                    Double productCode = documentField.getValueNumber();
-                    System.out.printf("Product Code: %f, confidence: %.2f%n",
-                      productCode, documentField.getConfidence());
-                  }
+              }
+              if ("ProductCode".equals(key)) {
+                if (DocumentFieldType.DOUBLE == documentField.getType()) {
+                  Double productCode = documentField.getValueAsDouble();
+                  System.out.printf("Product Code: %f, confidence: %.2f%n",
+                    productCode, documentField.getConfidence());
                 }
-              }));
-          }
+              }
+            }));
         }
       }
     }

@@ -6,13 +6,13 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: include
-ms.date: 11/18/2025
+ms.date: 01/30/2026
 ms.author: lajanuar
 ---
 <!-- markdownlint-disable MD025 -->
 
 :::moniker range="doc-intel-4.0.0"
-[Client library](/javascript/api/overview/azure/ai-document-intelligence-rest-readme?view=azure-node-latest&preserve-view=true) | [REST API reference](/rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true) | [Package (npm)]( https://www.npmjs.com/package/@azure-rest/ai-document-intelligence/v/1.0.0) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/documentintelligence/ai-document-intelligence-rest/samples/v1/javascript) |[Supported REST API version](../../sdk-overview-v4-0.md)
+[Client library](/javascript/api/overview/azure/ai-document-intelligence-rest-readme?view=azure-node-latest&preserve-view=true) | [REST API reference](/rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true) | [Package (npm)](https://www.npmjs.com/package/@azure-rest/ai-document-intelligence/v/1.1.0) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/documentintelligence/ai-document-intelligence-rest/samples/v1/javascript) |[Supported REST API version](../../sdk-overview-v4-0.md)
 :::moniker-end
 
 :::moniker range="doc-intel-3.1.0"
@@ -73,7 +73,7 @@ In this quickstart, use the following features to analyze and extract data and v
  4. Install the `ai-document-intelligence` client library and `azure/identity` npm packages:
 
     ```console
-    npm i @azure-rest/ai-document-intelligence@1.0.0 @azure/core-auth
+    npm i @azure-rest/ai-document-intelligence@1.1.0
     ```
 
     Your app's `package.json` file is updated with the dependencies.
@@ -144,56 +144,64 @@ Extract text, selection marks, text styles, table structures, and bounding regio
 :::moniker range="doc-intel-4.0.0"
 
 ```javascript
-    const DocumentIntelligence = require("@azure-rest/ai-document-intelligence").default,
-  { getLongRunningPoller, isUnexpected } = require("@azure-rest/ai-document-intelligence");
-
-  const { AzureKeyCredential } = require("@azure/core-auth");
-
-    // set `<your-key>` and `<your-endpoint>` variables with the values from the Azure portal.
-    const key = "<your-key>";
-    const endpoint = "<your-endpoint>";
+const DocumentIntelligence = require("@azure-rest/ai-document-intelligence").default,
+{ getLongRunningPoller, isUnexpected } = require("@azure-rest/ai-document-intelligence");
 
-    // sample document
-    const formUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf"
+// set `<your-key>` and `<your-endpoint>` variables with the values from the Azure portal.
+const key = "<your-key>";
+const endpoint = "<your-endpoint>";
 
-   async function main() {
-    const client = DocumentIntelligence(endpoint, new AzureKeyCredential(key));
+// sample document
+const formUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf"
 
+async function main() {
+  const client = DocumentIntelligence(endpoint, { key });
 
-    const initialResponse = await client
-      .path("/documentModels/{modelId}:analyze", "prebuilt-layout")
-      .post({
-        contentType: "application/json",
-        body: {
-          urlSource: formUrl
-        },
-       });
+  const initialResponse = await client
+    .path("/documentModels/{modelId}:analyze", "prebuilt-layout")
+    .post({
+      contentType: "application/json",
+      body: {
+        urlSource: formUrl
+      },
+    });
 
-       if (isUnexpected(initialResponse)) {
-       throw initialResponse.body.error;
-     }
+  if (isUnexpected(initialResponse)) {
+    throw initialResponse.body.error;
+  }
 
-    const poller = await getLongRunningPoller(client, initialResponse);
-    const analyzeResult = (await poller.pollUntilDone()).body.analyzeResult;
+  const poller = getLongRunningPoller(client, initialResponse);
+  const analyzeResult = (await poller.pollUntilDone()).body.analyzeResult;
 
-    const documents = analyzeResult?.documents;
+  const pages = analyzeResult?.pages;
+  const tables = analyzeResult?.tables;
 
-    const document = documents && documents[0];
-    if (!document) {
-    throw new Error("Expected at least one document in the result.");
+  if (pages && pages.length > 0) {
+    console.log("Pages:");
+    for (const page of pages) {
+      console.log("- Page", page.pageNumber, `(unit: ${page.unit})`);
+      console.log(`  ${page.width}x${page.height}`);
+      console.log(`  ${page.lines?.length || 0} lines, ${page.words?.length || 0} words`);
     }
+  } else {
+    console.log("No pages were extracted from the document.");
+  }
 
-    console.log(
-    "Extracted document:",
-    document.docType,
-    `(confidence: ${document.confidence || "<undefined>"})`,
-    );
-    console.log("Fields:", document.fields);
+  if (tables && tables.length > 0) {
+    console.log("Tables:");
+    for (const table of tables) {
+      console.log(
+        `- Extracted table: ${table.columnCount} columns, ${table.rowCount} rows (${table.cells.length} cells)`
+      );
+    }
+  } else {
+    console.log("No tables were extracted from the document.");
+  }
 }
 
 main().catch((error) => {
-    console.error("An error occurred:", error);
-    process.exit(1);
+  console.error("An error occurred:", error);
+  process.exit(1);
 });
 
 ```
@@ -313,63 +321,53 @@ In this example, we analyze an invoice using the **prebuilt-invoice** model.
 :::moniker range="doc-intel-4.0.0"
 
 ```javascript
-
 const DocumentIntelligence = require("@azure-rest/ai-document-intelligence").default,
-  { getLongRunningPoller, isUnexpected } = require("@azure-rest/ai-document-intelligence");
-
-const { AzureKeyCredential } = require("@azure/core-auth");
+{ getLongRunningPoller, isUnexpected } = require("@azure-rest/ai-document-intelligence");
 
-    // set `<your-key>` and `<your-endpoint>` variables with the values from the Azure portal.
-    const key = "<your-key>";
-    const endpoint = "<your-endpoint>";
+// set `<your-key>` and `<your-endpoint>` variables with the values from the Azure portal.
+const key = "<your-key>";
+const endpoint = "<your-endpoint>";
 
-    // sample document
-    const invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf"
+// sample document
+const invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf"
 
 async function main() {
+  const client = DocumentIntelligence(endpoint, { key });
 
-    const client = DocumentIntelligence(endpoint, new AzureKeyCredential(key));
-
-    const initialResponse = await client
+  const initialResponse = await client
     .path("/documentModels/{modelId}:analyze", "prebuilt-invoice")
     .post({
       contentType: "application/json",
       body: {
-        // The Document Intelligence service will access the URL to the invoice image and extract data from it
         urlSource: invoiceUrl,
       },
     });
 
-    if (isUnexpected(initialResponse)) {
-       throw initialResponse.body.error;
-     }
-
-    const poller = await getLongRunningPoller(client, initialResponse);
+  if (isUnexpected(initialResponse)) {
+    throw initialResponse.body.error;
+  }
 
-    poller.onProgress((state) => console.log("Operation:", state.result, state.status));
-    const analyzeResult = (await poller.pollUntilDone()).body.analyzeResult;
+  const poller = getLongRunningPoller(client, initialResponse);
+  const analyzeResult = (await poller.pollUntilDone()).body.analyzeResult;
 
-    const documents = analyzeResult?.documents;
+  const documents = analyzeResult?.documents;
 
-    const result = documents && documents[0];
-    if (result) {
-      console.log(result.fields);
-    } else {
-      throw new Error("Expected at least one invoice in the result.");
-    }
+  const invoice = documents && documents[0];
+  if (!invoice) {
+    throw new Error("Expected at least one invoice in the result.");
+  }
 
-console.log(
+  console.log(
     "Extracted invoice:",
-    document.docType,
-    `(confidence: ${document.confidence || "<undefined>"})`,
+    invoice.docType,
+    `(confidence: ${invoice.confidence || "<undefined>"})`
   );
-  console.log("Fields:", document.fields);
+  console.log("Fields:", invoice.fields);
 }
 
-
 main().catch((error) => {
-    console.error("An error occurred:", error);
-    process.exit(1);
+  console.error("An error occurred:", error);
+  process.exit(1);
 });
 ```
 

@@ -12,7 +12,7 @@ ms.author: lajanuar
 <!-- markdownlint-disable MD025 -->
 
 :::moniker range="doc-intel-4.0.0"
-[Client library](/python/api/overview/azure/ai-documentintelligence-readme?view=azure-python-preview&preserve-view=true) |[REST API reference](/rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true) | [Package (PyPi)](https://pypi.org/project/azure-ai-documentintelligence/1.0.0b4/) | [Samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples) | [Supported REST API version](../../sdk-overview-v4-0.md#supported-programming-languages)
+[Client library](/python/api/overview/azure/ai-documentintelligence-readme?view=azure-python&preserve-view=true) |[REST API reference](/rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true) | [Package (PyPi)](https://pypi.org/project/azure-ai-documentintelligence/1.0.2/) | [Samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/documentintelligence/azure-ai-documentintelligence/samples) | [Supported REST API version](../../sdk-overview-v4-0.md#supported-programming-languages)
 :::moniker-end
 
 :::moniker range="doc-intel-3.1.0"
@@ -55,7 +55,7 @@ Open a terminal window in your local environment and install the Azure Document
 :::moniker range="doc-intel-4.0.0"
 
 ```console
-pip install azure-ai-documentintelligence==1.0.0b4
+pip install azure-ai-documentintelligence==1.0.2
 
 ```
 
@@ -751,8 +751,7 @@ def analyze_invoice():
                     f"Remittance Address Recipient: {remittance_address_recipient.get('content')} has confidence: {remittance_address_recipient.get('confidence')}"
                 )
 
-
-          print("----------------------------------------")
+            print("----------------------------------------")
 
 
 if __name__ == "__main__":

@@ -79,14 +79,15 @@ Before you run the cURL command, make the following changes to the [post request
 | **Invoice**  | `prebuilt-invoice` | `https://github.com/Azure-Samples/cognitive-services-REST-api-samples/raw/master/curl/form-recognizer/rest-api/invoice.pdf` |
 | **Receipt**  | `prebuilt-receipt` | `https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/receipt.png` |
 | **ID document**  | `prebuilt-idDocument` | `https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/identity_documents.png` |
+
 :::moniker-end
 
 :::moniker range="doc-intel-3.1.0 || doc-intel-3.0.0"
 
 **Sample documents**
 
 | **Feature**   | **{modelID}**   | **{your-document-url}** |
-| --- | --- |--|
+| --- | --- | --- |
 | **General Document** | `prebuilt-document` | `https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf` |
 | **Read** | `prebuilt-read` | `https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/read.png` |
 | **Layout** | `prebuilt-layout` | `https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/layout.png` |
@@ -96,6 +97,7 @@ Before you run the cURL command, make the following changes to the [post request
 | **Receipt**  | `prebuilt-receipt` | `https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/receipt.png` |
 | **ID document**  | `prebuilt-idDocument` | `https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/identity_documents.png` |
 | **Business card**  | `prebuilt-businessCard` | `https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/de5e0d8982ab754823c54de47a47e8e499351523/curl/form-recognizer/rest-api/business_card.jpg` |
+
 :::moniker-end
 
 > [!IMPORTANT]
@@ -106,7 +108,7 @@ Before you run the cURL command, make the following changes to the [post request
 :::moniker range="doc-intel-4.0.0"
 
 ```bash
-curl -v -i POST "{endpoint}/documentintelligence/documentModels/{modelId}:analyze?api-version=2024-11-30" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {key}" --data-ascii "{'urlSource': '{your-document-url}'}"
+curl -v -i -X POST "{endpoint}/documentintelligence/documentModels/{modelId}:analyze?api-version=2024-11-30" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {key}" --data-ascii "{'urlSource': '{your-document-url}'}"
 ```
 
 :::moniker-end
@@ -137,7 +139,7 @@ You receive a `202 (Success)` response that includes a read-only **Operation-Loc
 
 :::moniker range="doc-intel-4.0.0"
 
-After you call the [`Analyze document`](/rest/api/aiservices/operation-groups) API, call the [**Get analyze result**](/rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true)&preserve-view=true&tabs=HTTP) API to get the status of the operation and the extracted data. Before you run the command, make these changes:
+After you call the [`Analyze document`](/rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true) API, call the [**Get analyze result**](/rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true) API to get the status of the operation and the extracted data. Before you run the command, make these changes:
 :::moniker-end
 
 :::moniker range="doc-intel-3.1.0"

@@ -27,47 +27,64 @@ Document Intelligence service is updated on an ongoing basis. Bookmark this page
 > [!IMPORTANT]
 > Preview API versions are retired once the GA API is released. The 2023-02-28-preview API version is retiring. If you're still using the preview API or the associated SDK versions, update your code to target the latest API version `2024-11-30 (GA)`. </br>
 
+## Latest updates
+
+**Content Understanding: The Next Step Forward for Document Intelligence**
+</br>
+In November 2025, the GA version of **Content Understanding** was released (**2025-11-01** API version). Content Understanding is an evolution of Document Intelligence that expands multimodal processing capabilities to support text, images, audio, and video content types.
+
+Key features include:
+
+* **Multimodal content analysis**. Process and extract insights from text, images, audio, and video within a unified API framework.
+* **Enhanced AI integration**. Seamlessly integrate with Azure AI services for intelligent content processing and decision-making workflows.
+* **Flexible deployment options**. Build applications, automate document workflows, or enable AI-driven analytics with scalable cloud infrastructure.
+* **Unified content extraction**. Utilize a single service to handle diverse content types, reducing complexity and improving operational efficiency.
+
 ## June 2025
+
 **Document Intelligence v4.0 Read container is now available!**
-<br>
+</br>
 This container image includes highly requested Read features like searchable PDF! For more information, *see:*
+
 * [Install and run containers](containers/install-run.md?view=doc-intel-4.0.0&preserve-view=true)
 * [Container image tags](containers/image-tags.md?view=doc-intel-4.0.0&preserve-view=true)
 
 ## April 2025
+
 **Document Intelligence v4.0 Layout container is now available!**
-<br>
+</br>
 For more information, *see:*
+
 * [Install and run containers](containers/install-run.md?view=doc-intel-4.0.0&preserve-view=true)
 * [Container image tags](containers/image-tags.md?view=doc-intel-4.0.0&preserve-view=true)
 
 ## December 2024
 
-**Document Intelligence v4.0 programming language SDKs are now generally available (GA)**! <br><br>The latest client libraries default to the [**2024-11-30 REST API (GA)**](/rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true) version of the service.<br><br>
+**Document Intelligence v4.0 programming language SDKs are now generally available (GA)**! </br></br>The latest client libraries default to the [**2024-11-30 REST API (GA)**](/rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true) version of the service.</br></br>
 For more information, *see* client libraries for the following supported programming languages:
 
-* [🆕 .NET (C#)](versioning/changelog-release-history.md?view=doc-intel-4.0.0&tabs=csharp&preserve-view=true)
+* [.NET (C#)](versioning/changelog-release-history.md?view=doc-intel-4.0.0&tabs=csharp&preserve-view=true)
 
-* [🆕 Java](versioning/changelog-release-history.md?view=doc-intel-4.0.0&tabs=java&preserve-view=true)
+* [Java](versioning/changelog-release-history.md?view=doc-intel-4.0.0&tabs=java&preserve-view=true)
 
-* [🆕 JavaScript](versioning/changelog-release-history.md?view=doc-intel-4.0.0&tabs=javascript&preserve-view=true)
+* [JavaScript](versioning/changelog-release-history.md?view=doc-intel-4.0.0&tabs=javascript&preserve-view=true)
 
-* [🆕 Python](versioning/changelog-release-history.md?view=doc-intel-4.0.0&tabs=python&preserve-view=true)
+* [Python](versioning/changelog-release-history.md?view=doc-intel-4.0.0&tabs=python&preserve-view=true)
 
 ## November 2024
 
 **Document Intelligence REST API v4.0: [**2024-11-30 REST API (GA)**](/rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true) is now generally available (GA)**! The v4.0 REST API includes the following changes:
 
-* [🆕 Batch API](concept-batch-analysis.md)
+* [Batch API](concept-batch-analysis.md)
   * Batch API now supports all models, including all read, layout, prebuilt verticals, and custom models.
   * Batch API supports LIST function to allow users to list batch jobs within past seven days.
   * Batch API supports DELETE function to explicitly delete batch job for GDPR and privacy compliance.
   * GetAnalyzeBatchResult supports resultId in response to LIST all resultIds.
- 
-* 🆕 Searchable PDF. The [prebuilt read](prebuilt/read.md) model now supports images formats (JPEG/JPG, PNG, BMP, TIFF, HEIF)  and language expansion to include Chinese, Japanese, and Korean for  [PDF output](prebuilt/read.md#searchable-pdf).
- 
+
+* Searchable PDF. The [prebuilt read](prebuilt/read.md) model now supports images formats (JPEG/JPG, PNG, BMP, TIFF, HEIF)  and language expansion to include Chinese, Japanese, and Korean for  [PDF output](prebuilt/read.md#searchable-pdf).
+
 * [Custom classification model](train/custom-model.md#custom-classification-model)
-  * Custom classification model supports incremental training. You can add new samples to existing classes or add new classes by referencing an existing classifier. 
+  * Custom classification model supports incremental training. You can add new samples to existing classes or add new classes by referencing an existing classifier.
   * With v4.0, custom classification model doesn't split documents by default during analysis. You need to explicitly set 'splitMode' property to auto to preserve the older behavior.
   * Custom classification model now supports 25,000 pages as new training page limit.
 
@@ -85,18 +102,18 @@ For more information, *see* client libraries for the following supported program
   * Mortgage model now supports signature detection for  forms 1003, 1004, 1005 and closing disclosure.
 
 * [Receipt Model](concept-receipt.md)
-  * Receipt Model now supports more fields including ReceiptType, Tax rate, CountryRegion, net amount and description. 
- 
-*  [🆕 US Tax model](prebuilt/tax-document.md)
-   *  New prebuilt tax models added for 1095A, 1095C, 1099SSA, and W4.
+  * Receipt Model now supports more fields including ReceiptType, Tax rate, CountryRegion, net amount and description.
+
+* [US Tax model](prebuilt/tax-document.md)
+  * New prebuilt tax models added for `1095A`, `1095C`, `1099SSA`, and `W4`.
 
 * [Delete analyze response](/rest/api/aiservices/document-models/delete-analyze-result?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true&tabs=HTTP)
-  * Analyze response is stored for 24 hours from when the operation completes for retrieval. For scenarios where you want to delete the response sooner, use the delete analyze response API to delete the response.  
+  * Analyze response is stored for 24 hours from when the operation completes for retrieval. For scenarios where you want to delete the response sooner, use the delete analyze response API to delete the response.
 
 * The v4.0 API includes cumulative updates from preview releases as listed:
   * [August 2024](#august-2024)
   * [May 2024](#may-2024)
-  * [Feb 2024](#february-2024) 
+  * [Feb 2024](#february-2024)
 
 ## August 2024
 
@@ -109,26 +126,26 @@ The Document Intelligence [**2024-07-31-preview**](/rest/api/aiservices/document
 * **West Europe**
 * **North Central US**
 
-* [🆕 Model compose with custom classifiers](train/composed-models.md)
+* [Model compose with custom classifiers](train/composed-models.md)
   * Document Intelligence now adds support for composing model with an explicit custom classification model. [Learn more about the benefits](train/composed-models.md) of using the new compose capability.
 * [Custom classification model](train/custom-model.md#custom-classification-model)
   * Custom classification model now supports updating the model in-place as well.
   * Custom classification model adds support for model copy operation to enable backup and disaster recovery.
   * Custom classification model now supports explicitly specifying pages to be classified from an input document.
-* [🆕 Mortgage documents model](concept-mortgage-documents.md)
+* [Mortgage documents model](concept-mortgage-documents.md)
   * Extract information from Appraisal (Form 1004).
   * Extract information from Validation of Employment (Form 1005).
-* [🆕 Check model](concept-bank-check.md)
+* [Check model](concept-bank-check.md)
   * Extract payee, amount, date, and other relevant information from checks.​
-* [🆕 Pay Stub model](concept-pay-stub.md)
+* [Pay Stub model](concept-pay-stub.md)
   * New prebuilt to process pay stubs to extract wages, hours, deductions, net pay and more.​
-* [🆕 Bank statement model](concept-bank-statement.md)
+* [Bank statement model](concept-bank-statement.md)
   * New prebuilt to extract account information including beginning and ending balances, transaction details from bank statements.​
-* [🆕 US Tax model](prebuilt/tax-document.md)
+* [US Tax model](prebuilt/tax-document.md)
   * New unified US tax model that can extract from forms such as W-2, 1098, 1099, and 1040.
-* 🆕 Searchable PDF. The [prebuilt read](prebuilt/read.md) model now supports [PDF output](prebuilt/read.md#searchable-pdf)  to download PDFs with embedded text from extraction results, allowing for PDF to be utilized in scenarios such as search copy of contents.
-* [Layout model](prebuilt/layout.md) now supports improved [figure detection](prebuilt/layout.md#figures) where figures from documents can now be downloaded as an image file to be used for further figure understanding. The layout model also features improvements to the OCR model for scanned text targeting improvements for single characters, boxed text, and dense text documents.
-* [🆕 Batch API](concept-batch-analysis.md)
+* Searchable PDF. The [prebuilt read](prebuilt/read.md) model now supports [PDF output](prebuilt/read.md#searchable-pdf)  to download PDFs with embedded text from extraction results, allowing for PDF to be utilized in scenarios such as search copy of contents.
+* [Layout model](prebuilt/layout.md) now supports improved [figure detection](prebuilt/layout.md#figures) where figures from documents can now be downloaded as an image file to be used for further figure understanding. The layout model also features improvements to the `OCR` model for scanned text targeting improvements for single characters, boxed text, and dense text documents.
+* [Batch API](concept-batch-analysis.md)
   * Document Intelligence now adds support for batch analysis operation to support analyzing a set of documents to simplify developer experience and improve efficiency.
 * [Add-on capabilities](concept-add-on-capabilities.md)
   * [Query fields](concept-add-on-capabilities.md#query-fields) AI quality of extraction is improved with the latest model.
@@ -188,14 +205,14 @@ The Document Intelligence [**2024-07-31-preview**](/rest/api/aiservices/document
   * Tax items support expansion for Germany (`de`), Spain (`es`), Portugal (`pt`), English Canada `en-CA`.
 
 * [ID model](prebuilt/id-document.md)
-  * [Expanded field support](prebuilt/id-document.md#supported-document-types) for European Union IDs and driver license.
-* [🆕 Mortgage documents](concept-mortgage-documents.md)
+  * [Expanded field support](prebuilt/id-document.md#supported-document-types) for European Union identification cards and driver licenses.
+* [Mortgage documents](concept-mortgage-documents.md)
   * Extract information from Uniform Residential Loan Application (Form 1003).
   * Extract information from Uniform Underwriting and Transmittal Summary or Form 1008.
   * Extract information from mortgage closing disclosure.
-* [🆕 Credit/Debit card model](concept-credit-card.md)
+* [Credit/Debit card model](concept-credit-card.md)
   * Extract information from bank cards.
-* [🆕 Marriage certificate](concept-marriage-certificate.md)
+* [Marriage certificate](concept-marriage-certificate.md)
   * New prebuilt to extract information from marriage certificates.
 
 ## December 2023