Python: Rebase durable task feature branch with main (#2806)

python/packages/azure-ai/agent_framework_azure_ai/_chat_client.py

@@ -43,7 +43,7 @@ from agent_framework import (
     use_function_invocation,
 )
 from agent_framework.exceptions import ServiceInitializationError, ServiceResponseException
-from agent_framework.observability import use_observability
+from agent_framework.observability import use_instrumentation
 from azure.ai.agents.aio import AgentsClient
 from azure.ai.agents.models import (
     Agent,
@@ -63,6 +63,8 @@ from azure.ai.agents.models import (
     McpTool,
     MessageDeltaChunk,
     MessageDeltaTextContent,
+    MessageDeltaTextFileCitationAnnotation,
+    MessageDeltaTextFilePathAnnotation,
     MessageDeltaTextUrlCitationAnnotation,
     MessageImageUrlParam,
     MessageInputContentBlock,
@@ -105,7 +107,7 @@ TAzureAIAgentClient = TypeVar("TAzureAIAgentClient", bound="AzureAIAgentClient")
 
 
 @use_function_invocation
-@use_observability
+@use_instrumentation
 @use_chat_middleware
 class AzureAIAgentClient(BaseChatClient):
     """Azure AI Agent Chat client."""
@@ -471,6 +473,45 @@ class AzureAIAgentClient(BaseChatClient):
 
         return url_citations
 
+    def _extract_file_path_contents(self, message_delta_chunk: MessageDeltaChunk) -> list[HostedFileContent]:
+        """Extract file references from MessageDeltaChunk annotations.
+
+        Code interpreter generates files that are referenced via file path or file citation
+        annotations in the message content. This method extracts those file IDs and returns
+        them as HostedFileContent objects.
+
+        Handles two annotation types:
+        - MessageDeltaTextFilePathAnnotation: Contains file_path.file_id
+        - MessageDeltaTextFileCitationAnnotation: Contains file_citation.file_id
+
+        Args:
+            message_delta_chunk: The message delta chunk to process
+
+        Returns:
+            List of HostedFileContent objects for any files referenced in annotations
+        """
+        file_contents: list[HostedFileContent] = []
+
+        for content in message_delta_chunk.delta.content:
+            if isinstance(content, MessageDeltaTextContent) and content.text and content.text.annotations:
+                for annotation in content.text.annotations:
+                    if isinstance(annotation, MessageDeltaTextFilePathAnnotation):
+                        # Extract file_id from the file_path annotation
+                        file_path = getattr(annotation, "file_path", None)
+                        if file_path is not None:
+                            file_id = getattr(file_path, "file_id", None)
+                            if file_id:
+                                file_contents.append(HostedFileContent(file_id=file_id))
+                    elif isinstance(annotation, MessageDeltaTextFileCitationAnnotation):
+                        # Extract file_id from the file_citation annotation
+                        file_citation = getattr(annotation, "file_citation", None)
+                        if file_citation is not None:
+                            file_id = getattr(file_citation, "file_id", None)
+                            if file_id:
+                                file_contents.append(HostedFileContent(file_id=file_id))
+
+        return file_contents
+
     def _get_real_url_from_citation_reference(
         self, citation_url: str, azure_search_tool_calls: list[dict[str, Any]]
     ) -> str:
@@ -530,6 +571,9 @@ class AzureAIAgentClient(BaseChatClient):
                         # Extract URL citations from the delta chunk
                         url_citations = self._extract_url_citations(event_data, azure_search_tool_calls)
 
+                        # Extract file path contents from code interpreter outputs
+                        file_contents = self._extract_file_path_contents(event_data)
+
                         # Create contents with citations if any exist
                         citation_content: list[Contents] = []
                         if event_data.text or url_citations:
@@ -538,6 +582,9 @@ class AzureAIAgentClient(BaseChatClient):
                                 text_content_obj.annotations = url_citations
                             citation_content.append(text_content_obj)
 
+                        # Add file contents from file path annotations
+                        citation_content.extend(file_contents)
+
                         yield ChatResponseUpdate(
                             role=role,
                             contents=citation_content if citation_content else None,

python/packages/azure-ai/agent_framework_azure_ai/_client.py

@@ -15,7 +15,7 @@ from agent_framework import (
     use_function_invocation,
 )
 from agent_framework.exceptions import ServiceInitializationError, ServiceInvalidRequestError
-from agent_framework.observability import use_observability
+from agent_framework.observability import use_instrumentation
 from agent_framework.openai._responses_client import OpenAIBaseResponsesClient
 from azure.ai.projects.aio import AIProjectClient
 from azure.ai.projects.models import (
@@ -49,7 +49,7 @@ TAzureAIClient = TypeVar("TAzureAIClient", bound="AzureAIClient")
 
 
 @use_function_invocation
-@use_observability
+@use_instrumentation
 @use_chat_middleware
 class AzureAIClient(OpenAIBaseResponsesClient):
     """Azure AI Agent client."""
@@ -164,27 +164,94 @@ class AzureAIClient(OpenAIBaseResponsesClient):
         # Track whether we should close client connection
         self._should_close_client = should_close_client
 
-    async def setup_azure_ai_observability(self, enable_sensitive_data: bool | None = None) -> None:
-        """Use this method to setup tracing in your Azure AI Project.
+    async def configure_azure_monitor(
+        self,
+        enable_sensitive_data: bool = False,
+        **kwargs: Any,
+    ) -> None:
+        """Setup observability with Azure Monitor (Azure AI Foundry integration).
 
-        This will take the connection string from the project project_client.
-        It will override any connection string that is set in the environment variables.
-        It will disable any OTLP endpoint that might have been set.
+        This method configures Azure Monitor for telemetry collection using the
+        connection string from the Azure AI project client.
+
+        Args:
+            enable_sensitive_data: Enable sensitive data logging (prompts, responses).
+                Should only be enabled in development/test environments. Default is False.
+            **kwargs: Additional arguments passed to configure_azure_monitor().
+                Common options include:
+                - enable_live_metrics (bool): Enable Azure Monitor Live Metrics
+                - credential (TokenCredential): Azure credential for Entra ID auth
+                - resource (Resource): Custom OpenTelemetry resource
+                See https://learn.microsoft.com/python/api/azure-monitor-opentelemetry/azure.monitor.opentelemetry.configure_azure_monitor
+                for full list of options.
+
+        Raises:
+            ImportError: If azure-monitor-opentelemetry-exporter is not installed.
+
+        Examples:
+            .. code-block:: python
+
+                from agent_framework.azure import AzureAIClient
+                from azure.ai.projects.aio import AIProjectClient
+                from azure.identity.aio import DefaultAzureCredential
+
+                async with (
+                    DefaultAzureCredential() as credential,
+                    AIProjectClient(
+                        endpoint="https://your-project.api.azureml.ms", credential=credential
+                    ) as project_client,
+                    AzureAIClient(project_client=project_client) as client,
+                ):
+                    # Setup observability with defaults
+                    await client.configure_azure_monitor()
+
+                    # With live metrics enabled
+                    await client.configure_azure_monitor(enable_live_metrics=True)
+
+                    # With sensitive data logging (dev/test only)
+                    await client.configure_azure_monitor(enable_sensitive_data=True)
+
+        Note:
+            This method retrieves the Application Insights connection string from the
+            Azure AI project client automatically. You must have Application Insights
+            configured in your Azure AI project for this to work.
         """
+        # Get connection string from project client
         try:
             conn_string = await self.project_client.telemetry.get_application_insights_connection_string()
         except ResourceNotFoundError:
             logger.warning(
-                "No Application Insights connection string found for the Azure AI Project, "
-                "please call setup_observability() manually."
+                "No Application Insights connection string found for the Azure AI Project. "
+                "Please ensure Application Insights is configured in your Azure AI project, "
+                "or call configure_otel_providers() manually with custom exporters."
             )
             return
-        from agent_framework.observability import setup_observability
 
-        setup_observability(
-            applicationinsights_connection_string=conn_string, enable_sensitive_data=enable_sensitive_data
+        # Import Azure Monitor with proper error handling
+        try:
+            from azure.monitor.opentelemetry import configure_azure_monitor
+        except ImportError as exc:
+            raise ImportError(
+                "azure-monitor-opentelemetry is required for Azure Monitor integration. "
+                "Install it with: pip install azure-monitor-opentelemetry"
+            ) from exc
+
+        from agent_framework.observability import create_metric_views, create_resource, enable_instrumentation
+
+        # Create resource if not provided in kwargs
+        if "resource" not in kwargs:
+            kwargs["resource"] = create_resource()
+
+        # Configure Azure Monitor with connection string and kwargs
+        configure_azure_monitor(
+            connection_string=conn_string,
+            views=create_metric_views(),
+            **kwargs,
         )
 
+        # Complete setup with core observability
+        enable_instrumentation(enable_sensitive_data=enable_sensitive_data)
+
     async def __aenter__(self) -> "Self":
         """Async context manager entry."""
         return self
@@ -268,6 +335,10 @@ class AzureAIClient(OpenAIBaseResponsesClient):
 
             if "tools" in run_options:
                 args["tools"] = run_options["tools"]
+            if "temperature" in run_options:
+                args["temperature"] = run_options["temperature"]
+            if "top_p" in run_options:
+                args["top_p"] = run_options["top_p"]
 
             if "response_format" in run_options:
                 response_format = run_options["response_format"]
@@ -346,7 +417,7 @@ class AzureAIClient(OpenAIBaseResponsesClient):
 
         # Remove properties that are not supported on request level
         # but were configured on agent level
-        exclude = ["model", "tools", "response_format"]
+        exclude = ["model", "tools", "response_format", "temperature", "top_p"]
 
         for property in exclude:
             run_options.pop(property, None)

python/packages/azure-ai/pyproject.toml

@@ -4,7 +4,7 @@ description = "Azure AI Foundry integration for Microsoft Agent Framework."
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0b251209"
+version = "1.0.0b251216"
 license-files = ["LICENSE"]
 urls.homepage = "https://aka.ms/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"

python/packages/azure-ai/tests/test_azure_ai_agent_client.py

@@ -24,6 +24,7 @@ from agent_framework import (
     FunctionCallContent,
     FunctionResultContent,
     HostedCodeInterpreterTool,
+    HostedFileContent,
     HostedFileSearchTool,
     HostedMCPTool,
     HostedVectorStoreContent,
@@ -42,6 +43,8 @@ from azure.ai.agents.models import (
     FileInfo,
     MessageDeltaChunk,
     MessageDeltaTextContent,
+    MessageDeltaTextFileCitationAnnotation,
+    MessageDeltaTextFilePathAnnotation,
     MessageDeltaTextUrlCitationAnnotation,
     RequiredFunctionToolCall,
     RequiredMcpToolCall,
@@ -1362,6 +1365,108 @@ def test_azure_ai_chat_client_extract_url_citations_with_citations(mock_agents_c
     assert citation.annotated_regions[0].end_index == 20
 
 
+def test_azure_ai_chat_client_extract_file_path_contents_with_file_path_annotation(
+    mock_agents_client: MagicMock,
+) -> None:
+    """Test _extract_file_path_contents with MessageDeltaChunk containing file path annotation."""
+    chat_client = create_test_azure_ai_chat_client(mock_agents_client, agent_id="test-agent")
+
+    # Create mock file_path annotation
+    mock_file_path = MagicMock()
+    mock_file_path.file_id = "assistant-test-file-123"
+
+    mock_annotation = MagicMock(spec=MessageDeltaTextFilePathAnnotation)
+    mock_annotation.file_path = mock_file_path
+
+    # Create mock text content with annotations
+    mock_text = MagicMock()
+    mock_text.annotations = [mock_annotation]
+
+    mock_text_content = MagicMock(spec=MessageDeltaTextContent)
+    mock_text_content.text = mock_text
+
+    # Create mock delta
+    mock_delta = MagicMock()
+    mock_delta.content = [mock_text_content]
+
+    # Create mock MessageDeltaChunk
+    mock_chunk = MagicMock(spec=MessageDeltaChunk)
+    mock_chunk.delta = mock_delta
+
+    # Call the method
+    file_contents = chat_client._extract_file_path_contents(mock_chunk)
+
+    # Verify results
+    assert len(file_contents) == 1
+    assert isinstance(file_contents[0], HostedFileContent)
+    assert file_contents[0].file_id == "assistant-test-file-123"
+
+
+def test_azure_ai_chat_client_extract_file_path_contents_with_file_citation_annotation(
+    mock_agents_client: MagicMock,
+) -> None:
+    """Test _extract_file_path_contents with MessageDeltaChunk containing file citation annotation."""
+    chat_client = create_test_azure_ai_chat_client(mock_agents_client, agent_id="test-agent")
+
+    # Create mock file_citation annotation
+    mock_file_citation = MagicMock()
+    mock_file_citation.file_id = "cfile_test-citation-456"
+
+    mock_annotation = MagicMock(spec=MessageDeltaTextFileCitationAnnotation)
+    mock_annotation.file_citation = mock_file_citation
+
+    # Create mock text content with annotations
+    mock_text = MagicMock()
+    mock_text.annotations = [mock_annotation]
+
+    mock_text_content = MagicMock(spec=MessageDeltaTextContent)
+    mock_text_content.text = mock_text
+
+    # Create mock delta
+    mock_delta = MagicMock()
+    mock_delta.content = [mock_text_content]
+
+    # Create mock MessageDeltaChunk
+    mock_chunk = MagicMock(spec=MessageDeltaChunk)
+    mock_chunk.delta = mock_delta
+
+    # Call the method
+    file_contents = chat_client._extract_file_path_contents(mock_chunk)
+
+    # Verify results
+    assert len(file_contents) == 1
+    assert isinstance(file_contents[0], HostedFileContent)
+    assert file_contents[0].file_id == "cfile_test-citation-456"
+
+
+def test_azure_ai_chat_client_extract_file_path_contents_empty_annotations(
+    mock_agents_client: MagicMock,
+) -> None:
+    """Test _extract_file_path_contents with no annotations returns empty list."""
+    chat_client = create_test_azure_ai_chat_client(mock_agents_client, agent_id="test-agent")
+
+    # Create mock text content with no annotations
+    mock_text = MagicMock()
+    mock_text.annotations = []
+
+    mock_text_content = MagicMock(spec=MessageDeltaTextContent)
+    mock_text_content.text = mock_text
+
+    # Create mock delta
+    mock_delta = MagicMock()
+    mock_delta.content = [mock_text_content]
+
+    # Create mock MessageDeltaChunk
+    mock_chunk = MagicMock(spec=MessageDeltaChunk)
+    mock_chunk.delta = mock_delta
+
+    # Call the method
+    file_contents = chat_client._extract_file_path_contents(mock_chunk)
+
+    # Verify results
+    assert len(file_contents) == 0
+
+
 def get_weather(
     location: Annotated[str, Field(description="The location to get the weather for.")],
 ) -> str:

python/packages/azure-ai/tests/test_azure_ai_client.py

@@ -477,6 +477,30 @@ async def test_azure_ai_client_agent_creation_with_instructions(
     assert call_args[1]["definition"].instructions == "Message instructions. Option instructions. "
 
 
+async def test_azure_ai_client_agent_creation_with_additional_args(
+    mock_project_client: MagicMock,
+) -> None:
+    """Test agent creation with additional arguments."""
+    client = create_test_azure_ai_client(mock_project_client, agent_name="test-agent")
+
+    # Mock agent creation response
+    mock_agent = MagicMock()
+    mock_agent.name = "test-agent"
+    mock_agent.version = "1.0"
+    mock_project_client.agents.create_version = AsyncMock(return_value=mock_agent)
+
+    run_options = {"model": "test-model", "temperature": 0.9, "top_p": 0.8}
+    messages_instructions = "Message instructions. "
+
+    await client._get_agent_reference_or_create(run_options, messages_instructions)  # type: ignore
+
+    # Verify agent was created with provided arguments
+    call_args = mock_project_client.agents.create_version.call_args
+    definition = call_args[1]["definition"]
+    assert definition.temperature == 0.9
+    assert definition.top_p == 0.8
+
+
 async def test_azure_ai_client_agent_creation_with_tools(
     mock_project_client: MagicMock,
 ) -> None: