mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Eduard van Valkenburg
534e5f5bf7
* Python: improve .env precedence and observability samples - Switch load_settings to explicit precedence: overrides -> explicit .env -> environment -> defaults\n- Raise when env_file_path is provided but missing\n- Update settings docs and tests for new behavior\n- Refresh observability samples and README guidance for env loading options\n\nCloses #3864\n\nCo-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fixed some imports * Fix load_settings CI regressions Allow explicit env_file_path values that exist but are not regular files (for example /dev/null) by checking path existence before dotenv parsing, and restore a dict accumulator with typed return cast to satisfy mypy. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Avoid implicit dotenv in observability Only load dotenv in observability helpers when env_file_path is explicitly provided, and remove test os.devnull workarounds that are no longer necessary. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
108 lines
4.5 KiB
Python
108 lines
4.5 KiB
Python
# /// script
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# requires-python = ">=3.10"
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# dependencies = [
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# "azure-monitor-opentelemetry",
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# ]
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# ///
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# Run with any PEP 723 compatible runner, e.g.:
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# uv run python/samples/02-agents/observability/agent_with_foundry_tracing.py
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import logging
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import os
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from random import randint
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from typing import Annotated
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from agent_framework import Agent, tool
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from agent_framework.observability import create_resource, enable_instrumentation, get_tracer
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from agent_framework.openai import OpenAIResponsesClient
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from azure.ai.projects.aio import AIProjectClient
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from azure.identity.aio import AzureCliCredential
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from azure.monitor.opentelemetry import configure_azure_monitor
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from dotenv import load_dotenv
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from opentelemetry.trace import SpanKind
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from opentelemetry.trace.span import format_trace_id
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from pydantic import Field
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"""
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This sample shows you can can setup telemetry in Microsoft Foundry for a custom agent.
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First ensure you have a Foundry workspace with Application Insights enabled.
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And use the Operate tab to Register an Agent.
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Set the OpenTelemetry agent ID to the value used below in the Agent creation: `weather-agent` (or change both).
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The sample uses the Azure Monitor OpenTelemetry exporter to send traces to Application Insights.
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So ensure you have the `azure-monitor-opentelemetry` package installed.
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"""
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# For loading the `AZURE_AI_PROJECT_ENDPOINT` environment variable
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load_dotenv()
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logger = logging.getLogger(__name__)
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# NOTE: approval_mode="never_require" is for sample brevity.
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# Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py
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# and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
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@tool(approval_mode="never_require")
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async def get_weather(
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location: Annotated[str, Field(description="The location to get the weather for.")],
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) -> str:
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"""Get the weather for a given location."""
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await asyncio.sleep(randint(0, 10) / 10.0) # Simulate a network call
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conditions = ["sunny", "cloudy", "rainy", "stormy"]
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return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
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async def main():
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async with (
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AzureCliCredential() as credential,
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AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project_client,
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):
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# This will enable tracing and configure the application to send telemetry data to the
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# Application Insights instance attached to the Azure AI project.
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# This will override any existing configuration.
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try:
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conn_string = await project_client.telemetry.get_application_insights_connection_string()
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except Exception:
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logger.warning(
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"No Application Insights connection string found for the Azure AI Project. "
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"Please ensure Application Insights is configured in your Azure AI project, "
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"or call configure_otel_providers() manually with custom exporters."
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)
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return
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configure_azure_monitor(
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connection_string=conn_string,
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enable_live_metrics=True,
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resource=create_resource(),
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enable_performance_counters=False,
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)
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# This call is not necessary if you have the environment variable ENABLE_INSTRUMENTATION=true set
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# If not or set to false, or if you want to enable or disable sensitive data collection, call this function.
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enable_instrumentation(enable_sensitive_data=True)
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print("Observability is set up. Starting Weather Agent...")
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questions = ["What's the weather in Amsterdam?", "and in Paris, and which is better?", "Why is the sky blue?"]
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with get_tracer().start_as_current_span("Weather Agent Chat", kind=SpanKind.CLIENT) as current_span:
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print(f"Trace ID: {format_trace_id(current_span.get_span_context().trace_id)}")
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agent = Agent(
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client=OpenAIResponsesClient(),
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tools=get_weather,
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name="WeatherAgent",
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instructions="You are a weather assistant.",
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id="weather-agent",
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)
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session = agent.create_session()
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for question in questions:
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print(f"\nUser: {question}")
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print(f"{agent.name}: ", end="")
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async for update in agent.run(question, session=session, stream=True):
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if update.text:
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print(update.text, end="")
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if __name__ == "__main__":
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asyncio.run(main())
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