Azure · chienyuanchang · Apr 20, 2026 · Apr 10, 2026 · Apr 13, 2026 · Apr 13, 2026
@@ -1,5 +1,10 @@
 # Release History
 
+## 1.0.2 (2026-04-20)
+
+### Bugs Fixed
+- Exposed `usage` property on `AnalyzeLROPoller` and `AnalyzeAsyncLROPoller` to surface billing and token consumption details (`UsageDetails`) returned by the REST API. Previously the `usage` field in the LRO response envelope was discarded by the generated deserialization callback.
+
 ## 1.0.1 (2026-03-06)
 
 ### Bugs Fixed

@@ -31,6 +31,7 @@ This table shows the relationship between SDK versions and supported API service
 
 | SDK version | Supported API service version |
 | ----------- | ----------------------------- |
+| 1.0.2       | 2025-11-01                    |
 | 1.0.1       | 2025-11-01                    |
 | 1.0.0       | 2025-11-01                    |
 

@@ -6,4 +6,4 @@
 # Changes may cause incorrect behavior and will be lost if the code is regenerated.
 # --------------------------------------------------------------------------
 
-VERSION = "1.0.1"
+VERSION = "1.0.2"
@@ -10,8 +10,10 @@
 """
 
 import re
-from typing import Any, TypeVar
+from typing import Any, Optional, TypeVar
 from azure.core.polling import AsyncLROPoller, AsyncPollingMethod
+from ... import models as _models
+from ..._utils.model_base import _deserialize
 
 PollingReturnType_co = TypeVar("PollingReturnType_co", covariant=True)
 
@@ -62,6 +64,26 @@ def operation_id(self) -> str:
         except (KeyError, ValueError) as e:
             raise ValueError(f"Could not extract operation ID: {str(e)}") from e
 
+    @property
+    def usage(self) -> Optional["_models.UsageDetails"]:
+        """Returns the usage details from the completed analyze operation.
+
+        This property is available after the operation has completed successfully.
+        It provides information about the resources consumed during analysis,
+        including document pages, contextualization tokens, and LLM token breakdown.
+
+        :return: The usage details, or None if the operation is not yet complete
+            or usage information is not available.
+        :rtype: ~azure.ai.contentunderstanding.models.UsageDetails or None
+        """
+        if not self.done():
+            return None
+        response = self.polling_method()._pipeline_response.http_response  # type: ignore # pylint: disable=protected-access
+        usage_data = response.json().get("usage")
+        if usage_data is None:
+            return None
+        return _deserialize(_models.UsageDetails, usage_data)
+
     @classmethod
     def from_poller(cls, poller: AsyncLROPoller[PollingReturnType_co]) -> "AnalyzeAsyncLROPoller[PollingReturnType_co]":  # pyright: ignore[reportInvalidTypeArguments]  # fmt: skip
         """Wrap an existing AsyncLROPoller without re-initializing the polling method.

@@ -14,6 +14,8 @@
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, TypeVar
 from azure.core import CaseInsensitiveEnumMeta
 from azure.core.polling import LROPoller, PollingMethod
+from . import _models
+from .._utils.model_base import _deserialize
 from ._models import (
     StringField,
     IntegerField,
@@ -155,6 +157,26 @@ def operation_id(self) -> str:
         except (KeyError, ValueError) as e:
             raise ValueError(f"Could not extract operation ID: {str(e)}") from e
 
+    @property
+    def usage(self) -> Optional["_models.UsageDetails"]:
+        """Returns the usage details from the completed analyze operation.
+
+        This property is available after the operation has completed successfully.
+        It provides information about the resources consumed during analysis,
+        including document pages, contextualization tokens, and LLM token breakdown.
+
+        :return: The usage details, or None if the operation is not yet complete
+            or usage information is not available.
+        :rtype: ~azure.ai.contentunderstanding.models.UsageDetails or None
+        """
+        if not self.done():
+            return None
+        response = self.polling_method()._pipeline_response.http_response  # type: ignore # pylint: disable=protected-access
+        usage_data = response.json().get("usage")
+        if usage_data is None:
+            return None
+        return _deserialize(_models.UsageDetails, usage_data)
+
     @classmethod
     def from_poller(cls, poller: LROPoller[PollingReturnType_co]) -> "AnalyzeLROPoller[PollingReturnType_co]":  # pyright: ignore[reportInvalidTypeArguments]  # fmt: skip
         """Wrap an existing LROPoller without re-initializing the polling method.

@@ -63,6 +63,7 @@ Extracts structured fields from invoices using `prebuilt-invoice` analyzer. Show
 - Extracting structured fields (customer name, totals, dates, line items)
 - Working with field confidence scores and source locations
 - Accessing object fields and array fields
+- Accessing usage details (billing metrics, token consumption per model)
 - Financial document processing (invoices, receipts, credit cards, bank statements, checks)
 
 ### Sample 04: Create Analyzer

@@ -107,7 +107,7 @@ async def main() -> None:
         if isinstance(range_result.contents[0], DocumentContent):
             range_doc = range_result.contents[0]
             print(
-                f"Content range analysis returned pages"
+                "Content range analysis returned pages"
                 f" {range_doc.start_page_number} - {range_doc.end_page_number}"
             )
         # [END analyze_binary_with_content_range]
@@ -125,7 +125,7 @@ async def main() -> None:
         if isinstance(combine_range_result.contents[0], DocumentContent):
             combine_doc = combine_range_result.contents[0]
             print(
-                f"Combined content range analysis returned pages"
+                "Combined content range analysis returned pages"
                 f" {combine_doc.start_page_number} - {combine_doc.end_page_number}"
             )
         # [END analyze_binary_with_combined_content_range]

@@ -211,6 +211,37 @@ async def main() -> None:
                     )
         # [END extract_invoice_fields]
 
+        # [START get_usage]
+        # Access usage details from the poller (available after result() completes).
+        # Usage reports resource consumption for billing estimation:
+        #
+        # - document_pages_standard/basic/minimal: Pages processed at each extraction tier.
+        #   Standard = layout + OCR (scanned docs), Basic = OCR only, Minimal = digital formats
+        #   (DOCX, XLSX, HTML, TXT) that need no OCR. Charged per 1,000 pages.
+        #
+        # - contextualization_tokens: Fixed-rate tokens charged by Content Understanding for
+        #   preparing context, generating confidence scores, source grounding, and formatting
+        #   output. Typically 1,000 tokens per page. Charged separately from LLM tokens.
+        #
+        # - tokens: Dict of "{model}-input" / "{model}-output" token counts consumed by your
+        #   Foundry model deployment (e.g. "gpt-4.1-input", "gpt-4.1-output"). These are
+        #   billed on your Foundry deployment, not on Content Understanding.
+        #
+        # For full pricing details, see:
+        # https://learn.microsoft.com/azure/ai-services/content-understanding/pricing-explainer
+        usage = poller.usage
+        if usage:
+            print("\nUsage Details:")
+            if usage.document_pages_standard is not None:
+                print(f"  Document pages (standard): {usage.document_pages_standard}")
+            if usage.contextualization_tokens is not None:
+                print(f"  Contextualization tokens: {usage.contextualization_tokens}")
+            if usage.tokens:
+                print("  Model tokens:")
+                for model, count in usage.tokens.items():
+                    print(f"    {model}: {count}")
+        # [END get_usage]
+
         if not isinstance(credential, AzureKeyCredential):
             await credential.close()
 

@@ -74,7 +74,7 @@ async def main() -> None:
         # You can replace this URL with your own publicly accessible document URL.
         document_url = "https://raw.githubusercontent.com/Azure-Samples/azure-ai-content-understanding-assets/main/document/mixed_financial_docs.pdf"
 
-        print(f"Analyzing document from URL with prebuilt-documentSearch...")
+        print("Analyzing document from URL with prebuilt-documentSearch...")
         print(f"  URL: {document_url}")
 
         poller = await client.begin_analyze(
@@ -119,7 +119,7 @@ async def main() -> None:
 
         range_doc_content = cast(DocumentContent, range_result.contents[0])
         print(
-            f"Content range analysis returned pages"
+            "Content range analysis returned pages"
             f" {range_doc_content.start_page_number} - {range_doc_content.end_page_number}"
         )
 
@@ -133,7 +133,7 @@ async def main() -> None:
 
         combine_doc_content = cast(DocumentContent, combine_range_result.contents[0])
         print(
-            f"Combined content range analysis returned pages"
+            "Combined content range analysis returned pages"
             f" {combine_doc_content.start_page_number} - {combine_doc_content.end_page_number}"
         )
         # [END analyze_document_url_with_content_range]
@@ -144,7 +144,7 @@ async def main() -> None:
         print("=" * 60)
         video_url = "https://raw.githubusercontent.com/Azure-Samples/azure-ai-content-understanding-assets/main/videos/sdk_samples/FlightSimulator.mp4"
 
-        print(f"Analyzing video from URL with prebuilt-videoSearch...")
+        print("Analyzing video from URL with prebuilt-videoSearch...")
         print(f"  URL: {video_url}")
 
         poller = await client.begin_analyze(
@@ -197,7 +197,7 @@ async def main() -> None:
         for range_media in video_range_result.contents:
             range_video_content = cast(AudioVisualContent, range_media)
             print(
-                f"Content range segment:"
+                "Content range segment:"
                 f" {range_video_content.start_time_ms} ms - {range_video_content.end_time_ms} ms"
             )
         # [END analyze_video_url_with_content_range]
@@ -220,7 +220,7 @@ async def main() -> None:
         for from_media in video_from_result.contents:
             from_video = cast(AudioVisualContent, from_media)
             print(
-                f"'10000-' segment:"
+                "'10000-' segment:"
                 f" {from_video.start_time_ms} ms - {from_video.end_time_ms} ms"
             )
 
@@ -239,7 +239,7 @@ async def main() -> None:
         for subsec_media in video_subsec_result.contents:
             subsec_video = cast(AudioVisualContent, subsec_media)
             print(
-                f"'1200-3651' segment:"
+                "'1200-3651' segment:"
                 f" {subsec_video.start_time_ms} ms - {subsec_video.end_time_ms} ms"
             )
 
@@ -258,7 +258,7 @@ async def main() -> None:
         for combine_media in video_combine_result.contents:
             combine_video = cast(AudioVisualContent, combine_media)
             print(
-                f"'0-3000,30000-' segment:"
+                "'0-3000,30000-' segment:"
                 f" {combine_video.start_time_ms} ms - {combine_video.end_time_ms} ms"
             )
         # [END analyze_video_url_with_additional_content_ranges]
@@ -269,7 +269,7 @@ async def main() -> None:
         print("=" * 60)
         audio_url = "https://raw.githubusercontent.com/Azure-Samples/azure-ai-content-understanding-assets/main/audio/callCenterRecording.mp3"
 
-        print(f"Analyzing audio from URL with prebuilt-audioSearch...")
+        print("Analyzing audio from URL with prebuilt-audioSearch...")
         print(f"  URL: {audio_url}")
 
         poller = await client.begin_analyze(
@@ -316,7 +316,7 @@ async def main() -> None:
 
         range_audio_content = cast(AudioVisualContent, audio_range_result.contents[0])
         print(
-            f"Content range audio segment:"
+            "Content range audio segment:"
             f" {range_audio_content.start_time_ms} ms - {range_audio_content.end_time_ms} ms"
         )
         # [END analyze_audio_url_with_content_range]
@@ -338,7 +338,7 @@ async def main() -> None:
         audio_from_result = await audio_from_poller.result()
         audio_from_content = cast(AudioVisualContent, audio_from_result.contents[0])
         print(
-            f"'10000-':"
+            "'10000-':"
             f" {audio_from_content.start_time_ms} ms - {audio_from_content.end_time_ms} ms"
         )
 
@@ -356,7 +356,7 @@ async def main() -> None:
         audio_subsec_result = await audio_subsec_poller.result()
         audio_subsec_content = cast(AudioVisualContent, audio_subsec_result.contents[0])
         print(
-            f"'1200-3651':"
+            "'1200-3651':"
             f" {audio_subsec_content.start_time_ms} ms - {audio_subsec_content.end_time_ms} ms"
         )
 
@@ -374,7 +374,7 @@ async def main() -> None:
         audio_combine_result = await audio_combine_poller.result()
         audio_combine_content = cast(AudioVisualContent, audio_combine_result.contents[0])
         print(
-            f"'0-3000,30000-':"
+            "'0-3000,30000-':"
             f" {audio_combine_content.start_time_ms} ms - {audio_combine_content.end_time_ms} ms"
         )
         # [END analyze_audio_url_with_additional_content_ranges]
@@ -385,7 +385,7 @@ async def main() -> None:
         print("=" * 60)
         image_url = "https://raw.githubusercontent.com/Azure-Samples/azure-ai-content-understanding-assets/main/image/pieChart.jpg"
 
-        print(f"Analyzing image from URL with prebuilt-imageSearch...")
+        print("Analyzing image from URL with prebuilt-imageSearch...")
         print(f"  URL: {image_url}")
 
         poller = await client.begin_analyze(

@@ -119,7 +119,7 @@ async def main() -> None:
         )
         await poller.result()
 
-        print(f"Analyzer copied successfully!")
+        print("Analyzer copied successfully!")
         # [END copy_analyzer]
 
         # [START update_and_verify_analyzer]
@@ -133,7 +133,7 @@ async def main() -> None:
             tags={"modelType": "model_in_production"},
         )
 
-        print(f"Updating target analyzer with production tag...")
+        print("Updating target analyzer with production tag...")
         await client.update_analyzer(
             analyzer_id=target_analyzer_id, resource=updated_analyzer
         )
@@ -148,7 +148,7 @@ async def main() -> None:
         # [END update_and_verify_analyzer]
 
         # [START delete_copied_analyzers]
-        print(f"\nCleaning up analyzers...")
+        print("\nCleaning up analyzers...")
 
         try:
             await client.delete_analyzer(analyzer_id=source_analyzer_id)

@@ -75,8 +75,8 @@
     Example resource ID format:
     /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CognitiveServices/accounts/{name}
 
-    Important: Cross-resource copying requires credential-based authentication (such as DefaultAzureCredential).
-    API keys cannot be used for cross-resource operations.
+    Note: If API keys are not provided, DefaultAzureCredential will be used.
+    Cross-resource copying with DefaultAzureCredential requires 'Cognitive Services User' role on both source and target resources.
 """
 
 import asyncio
@@ -208,15 +208,15 @@ async def main() -> None:
                 resource=source_analyzer,
             )
             await poller.result()
-            print(f"  Source analyzer created successfully!")
+            print("  Source analyzer created successfully!")
 
             # Step 2: Grant copy authorization
             # Authorization must be granted by the source resource before the target resource can copy
             # The grant_copy_authorization method takes:
             # - The source analyzer ID to be copied
             # - The target Azure resource ID that is allowed to receive the copy
             # - The target region where the copy will be performed (optional, defaults to current region)
-            print(f"\nStep 2: Granting copy authorization from source resource...")
+            print("\nStep 2: Granting copy authorization from source resource...")
             print(f"  Target Azure Resource ID: {target_resource_id}")
             print(f"  Target Region: {target_region}")
 
@@ -226,7 +226,7 @@ async def main() -> None:
                 target_region=target_region,
             )
 
-            print(f"  Authorization granted successfully!")
+            print("  Authorization granted successfully!")
             print(f"  Target Azure Resource ID: {copy_auth.target_azure_resource_id}")
             print(f"  Target Region: {target_region}")
             print(f"  Expires at: {copy_auth.expires_at}")
@@ -235,7 +235,7 @@ async def main() -> None:
             # The copy_analyzer method must be called on the target client because the target
             # resource is the one receiving and creating the copy. The target resource validates
             # that authorization was previously granted by the source resource.
-            print(f"\nStep 3: Copying analyzer from source to target...")
+            print("\nStep 3: Copying analyzer from source to target...")
             print(f"  Source Analyzer ID: {source_analyzer_id}")
             print(f"  Source Azure Resource ID: {source_resource_id}")
             print(f"  Source Region: {source_region}")
@@ -248,22 +248,22 @@ async def main() -> None:
                 source_region=source_region,
             )
             await copy_poller.result()
-            print(f"  Analyzer copied successfully to target resource!")
+            print("  Analyzer copied successfully to target resource!")
 
             # Step 4: Verify the copy
             # Retrieve the analyzer from the target resource to verify the copy was successful
-            print(f"\nStep 4: Verifying the copied analyzer...")
+            print("\nStep 4: Verifying the copied analyzer...")
             copied_analyzer = await target_client.get_analyzer(
                 analyzer_id=target_analyzer_id
             )
             print(f"  Target Analyzer ID: {copied_analyzer.analyzer_id}")
             print(f"  Description: {copied_analyzer.description}")
             print(f"  Status: {copied_analyzer.status}")
-            print(f"\nCross-resource copy completed successfully!")
+            print("\nCross-resource copy completed successfully!")
 
     finally:
         # Clean up - create new client instances for cleanup since the original ones are closed
-        print(f"\nCleaning up...")
+        print("\nCleaning up...")
         cleanup_source_client = ContentUnderstandingClient(
             endpoint=source_endpoint, credential=source_credential
         )