Remove ContentRange class and use plain strings for content range

Co-authored-by: changjian-wang <15209050+changjian-wang@users.noreply.github.com>

Author: Copilot

sdk/contentunderstanding/azure-ai-contentunderstanding/azure/ai/contentunderstanding/_patch.py

@@ -236,7 +236,7 @@ def begin_analyze_binary(
         analyzer_id: str,
         binary_input: bytes,
         *,
-        content_range: Optional[Union[str, _models.ContentRange]] = None,
+        content_range: Optional[str] = None,
         content_type: str = "application/octet-stream",
         processing_location: Optional[Union[str, _models.ProcessingLocation]] = None,
         **kwargs: Any,
@@ -247,11 +247,10 @@ def begin_analyze_binary(
         :type analyzer_id: str
         :param binary_input: The binary content of the document to analyze. Required.
         :type binary_input: bytes
-        :keyword content_range: Range of the input to analyze. Accepts a
-         :class:`~azure.ai.contentunderstanding.models.ContentRange` or a raw string
+        :keyword content_range: Range of the input to analyze. Accepts a raw string
          (ex. ``"1-3,5,9-"``). Document content uses 1-based page numbers,
          while audio visual content uses integer milliseconds. Default value is None.
-        :paramtype content_range: str or ~azure.ai.contentunderstanding.models.ContentRange
+        :paramtype content_range: str
         :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
          Default value is "application/octet-stream".
         :paramtype content_type: str
@@ -268,16 +267,13 @@ def begin_analyze_binary(
            matches Python's native string indexing behavior (len() and str[i] use code points).
            This ensures ContentSpan offsets work correctly with Python string slicing.
         """
-        # Convert ContentRange to string if needed
-        content_range_str = str(content_range) if content_range is not None else None
-
         # Call parent implementation with string_encoding set to "codePoint"
         # (matches Python's string indexing)
         poller = super().begin_analyze_binary(
             analyzer_id=analyzer_id,
             binary_input=binary_input,
             string_encoding="codePoint",
-            content_range=content_range_str,
+            content_range=content_range,
             content_type=content_type,
             processing_location=processing_location,
             **kwargs,

sdk/contentunderstanding/azure-ai-contentunderstanding/azure/ai/contentunderstanding/aio/_patch.py

@@ -237,7 +237,7 @@ async def begin_analyze_binary(
         analyzer_id: str,
         binary_input: bytes,
         *,
-        content_range: Optional[Union[str, _models.ContentRange]] = None,
+        content_range: Optional[str] = None,
         content_type: str = "application/octet-stream",
         processing_location: Optional[Union[str, _models.ProcessingLocation]] = None,
         **kwargs: Any,
@@ -248,11 +248,10 @@ async def begin_analyze_binary(
         :type analyzer_id: str
         :param binary_input: The binary content of the document to analyze. Required.
         :type binary_input: bytes
-        :keyword content_range: Range of the input to analyze. Accepts a
-         :class:`~azure.ai.contentunderstanding.models.ContentRange` or a raw string
+        :keyword content_range: Range of the input to analyze. Accepts a raw string
          (ex. ``"1-3,5,9-"``). Document content uses 1-based page numbers,
          while audio visual content uses integer milliseconds. Default value is None.
-        :paramtype content_range: str or ~azure.ai.contentunderstanding.models.ContentRange
+        :paramtype content_range: str
         :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
          Default value is "application/octet-stream".
         :paramtype content_type: str
@@ -269,16 +268,13 @@ async def begin_analyze_binary(
            matches Python's native string indexing behavior (len() and str[i] use code points).
            This ensures ContentSpan offsets work correctly with Python string slicing.
         """
-        # Convert ContentRange to string if needed
-        content_range_str = str(content_range) if content_range is not None else None
-
         # Call parent implementation with string_encoding set to "codePoint"
         # (matches Python's string indexing)
         poller = await super().begin_analyze_binary(
             analyzer_id=analyzer_id,
             binary_input=binary_input,
             string_encoding="codePoint",
-            content_range=content_range_str,
+            content_range=content_range,
             content_type=content_type,
             processing_location=processing_location,
             **kwargs,

sdk/contentunderstanding/azure-ai-contentunderstanding/azure/ai/contentunderstanding/models/_content_range.py

@@ -14,7 +14,6 @@
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, TypeVar
 from azure.core import CaseInsensitiveEnumMeta
 from azure.core.polling import LROPoller, PollingMethod
-from ._content_range import ContentRange
 from ._models import (
     StringField,
     IntegerField,
@@ -77,7 +76,6 @@ def value(self) -> Optional[Any]: ...
 PollingReturnType_co = TypeVar("PollingReturnType_co", covariant=True)
 
 __all__ = [
-    "ContentRange",
     "RecordMergePatchUpdate",
     "AnalyzeLROPoller",
     "ProcessingLocation",

sdk/contentunderstanding/azure-ai-contentunderstanding/azure/ai/contentunderstanding/models/_patch.py

@@ -58,7 +58,6 @@
 from azure.ai.contentunderstanding.aio import ContentUnderstandingClient
 from azure.ai.contentunderstanding.models import (
     AnalysisResult,
-    ContentRange,
     DocumentContent,
 )
 from azure.core.credentials import AzureKeyCredential
@@ -91,55 +90,50 @@ async def main() -> None:
         # [END analyze_document_from_binary]
 
         # [START analyze_binary_with_content_range]
-        # Use a multi-page document for ContentRange demonstrations.
+        # Use a multi-page document for content range demonstrations.
         multi_page_path = "sample_files/mixed_financial_invoices.pdf"
         with open(multi_page_path, "rb") as f:
             multi_page_bytes = f.read()
 
         # Analyze only pages 3 onward.
-        print("\nAnalyzing pages 3 onward with ContentRange...")
+        print("\nAnalyzing pages 3 onward with content range '3-'...")
         range_poller = await client.begin_analyze_binary(
             analyzer_id="prebuilt-documentSearch",
             binary_input=multi_page_bytes,
-            content_range=ContentRange.pages_from(3),
+            content_range="3-",
         )
         range_result: AnalysisResult = await range_poller.result()
 
         if isinstance(range_result.contents[0], DocumentContent):
             range_doc = range_result.contents[0]
             print(
-                f"ContentRange analysis returned pages"
+                f"Content range analysis returned pages"
                 f" {range_doc.start_page_number} - {range_doc.end_page_number}"
             )
         # [END analyze_binary_with_content_range]
 
         # [START analyze_binary_with_combined_content_range]
         # Analyze pages 1-3, page 5, and pages 9 onward.
-        print("\nAnalyzing combined pages (1-3, 5, 9-) with ContentRange...")
+        print("\nAnalyzing combined pages (1-3, 5, 9-) with content range '1-3,5,9-'...")
         combine_range_poller = await client.begin_analyze_binary(
             analyzer_id="prebuilt-documentSearch",
             binary_input=multi_page_bytes,
-            content_range=ContentRange.combine(
-                ContentRange.pages(1, 3),
-                ContentRange.page(5),
-                ContentRange.pages_from(9),
-            ),
+            content_range="1-3,5,9-",
         )
         combine_range_result: AnalysisResult = await combine_range_poller.result()
 
         if isinstance(combine_range_result.contents[0], DocumentContent):
             combine_doc = combine_range_result.contents[0]
             print(
-                f"Combined ContentRange analysis returned pages"
+                f"Combined content range analysis returned pages"
                 f" {combine_doc.start_page_number} - {combine_doc.end_page_number}"
             )
         # [END analyze_binary_with_combined_content_range]
 
         # [START analyze_binary_with_raw_content_range]
-        # You can also pass content range as a plain string directly, without using
-        # ContentRange factory methods. The wire format is the same as what the factory methods produce.
+        # Content range is passed as a plain string. Document ranges use 1-based page numbers.
 
-        # "1-3" — pages 1 through 3 (equivalent to ContentRange.pages(1, 3))
+        # "1-3" — pages 1 through 3
         print("\nAnalyzing pages 1-3 with raw string '1-3'...")
         raw_pages_poller = await client.begin_analyze_binary(
             analyzer_id="prebuilt-documentSearch",
@@ -155,7 +149,7 @@ async def main() -> None:
                 f" {raw_pages_doc.start_page_number} - {raw_pages_doc.end_page_number}"
             )
 
-        # "9-" — pages 9 onward (equivalent to ContentRange.pages_from(9))
+        # "9-" — pages 9 onward
         print("\nAnalyzing pages 9 onward with raw string '9-'...")
         raw_from_poller = await client.begin_analyze_binary(
             analyzer_id="prebuilt-documentSearch",
@@ -171,7 +165,7 @@ async def main() -> None:
                 f" {raw_from_doc.start_page_number} - {raw_from_doc.end_page_number}"
             )
 
-        # "1-3,5,9-" — combined ranges (equivalent to ContentRange.combine(pages(1,3), page(5), pages_from(9)))
+        # "1-3,5,9-" — combined ranges
         print("\nAnalyzing combined pages (1-3, 5, 9-) with raw string '1-3,5,9-'...")
         raw_combine_poller = await client.begin_analyze_binary(
             analyzer_id="prebuilt-documentSearch",