agent/agent-framework
2
0
Fork 0
mirror of https://github.com/microsoft/agent-framework.git synced 2026-06-16 21:04:09 +08:00
Code Issues Packages Projects Releases Wiki Activity

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

Browse Source
This commit is contained in:
Laveesh Rohra
2025-12-17 14:02:36 -08:00
committed by GitHub
parent a48a8dd524
commit 87a38bc7da
227 changed files with 11968 additions and 2637 deletions
Download Patch File Download Diff File Expand all files Collapse all files
python/packages/azure-ai/agent_framework_azure_ai/_chat_client.py
+49 -2
View File
@@ -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
+84 -13
View File
@@ -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)