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
4.0 KiB
Python
108 lines
4.0 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
|
|
|
|
import asyncio
|
|
from random import randint
|
|
from typing import TYPE_CHECKING, Annotated
|
|
|
|
from agent_framework import Message, tool
|
|
from agent_framework.observability import get_tracer
|
|
from agent_framework.openai import OpenAIResponsesClient
|
|
from opentelemetry.trace import SpanKind
|
|
from opentelemetry.trace.span import format_trace_id
|
|
from pydantic import Field
|
|
|
|
if TYPE_CHECKING:
|
|
from agent_framework import SupportsChatGetResponse
|
|
|
|
|
|
"""
|
|
This sample shows how you can configure observability of an application with zero code changes.
|
|
It relies on the OpenTelemetry auto-instrumentation capabilities, and the observability setup
|
|
is done via environment variables.
|
|
|
|
Follow the install guidance from https://opentelemetry.io/docs/zero-code/python/ to install the OpenTelemetry CLI tool,
|
|
when using `uv` there are some additional steps, so follow the instructions carefully.
|
|
|
|
And setup a local OpenTelemetry Collector instance to receive the traces and metrics (and update the endpoint below).
|
|
|
|
Then you can run:
|
|
```bash
|
|
opentelemetry-instrument \
|
|
--traces_exporter otlp \
|
|
--metrics_exporter otlp \
|
|
--service_name agent_framework \
|
|
--exporter_otlp_endpoint http://localhost:4317 \
|
|
python python/samples/02-agents/observability/advanced_zero_code.py
|
|
```
|
|
(or use uv run in front when you've done the install within your uv virtual environment)
|
|
|
|
You can also set the environment variables instead of passing them as CLI arguments.
|
|
|
|
"""
|
|
|
|
|
|
# NOTE: approval_mode="never_require" is for sample brevity.
|
|
# Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py
|
|
# and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
|
|
@tool(approval_mode="never_require")
|
|
async def get_weather(
|
|
location: Annotated[str, Field(description="The location to get the weather for.")],
|
|
) -> str:
|
|
"""Get the weather for a given location."""
|
|
await asyncio.sleep(randint(0, 10) / 10.0) # Simulate a network call
|
|
conditions = ["sunny", "cloudy", "rainy", "stormy"]
|
|
return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
|
|
|
|
|
|
async def run_chat_client(client: "SupportsChatGetResponse", stream: bool = False) -> None:
|
|
"""Run an AI service.
|
|
|
|
This function runs an AI service and prints the output.
|
|
Telemetry will be collected for the service execution behind the scenes,
|
|
and the traces will be sent to the configured telemetry backend.
|
|
|
|
The telemetry will include information about the AI service execution.
|
|
|
|
Args:
|
|
stream: Whether to use streaming for the plugin
|
|
|
|
Remarks:
|
|
When function calling is outside the open telemetry loop
|
|
each of the call to the model is handled as a separate span,
|
|
while when the open telemetry is put last, a single span
|
|
is shown, which might include one or more rounds of function calling.
|
|
|
|
So for the scenario below, you should see the following:
|
|
|
|
2 spans with gen_ai.operation.name=chat
|
|
The first has finish_reason "tool_calls"
|
|
The second has finish_reason "stop"
|
|
2 spans with gen_ai.operation.name=execute_tool
|
|
|
|
"""
|
|
message = "What's the weather in Amsterdam and in Paris?"
|
|
print(f"User: {message}")
|
|
if stream:
|
|
print("Assistant: ", end="")
|
|
async for chunk in client.get_response([Message(role="user", text=message)], tools=get_weather, stream=True):
|
|
if chunk.text:
|
|
print(chunk.text, end="")
|
|
print("")
|
|
else:
|
|
response = await client.get_response([Message(role="user", text=message)], tools=get_weather)
|
|
print(f"Assistant: {response}")
|
|
|
|
|
|
async def main() -> None:
|
|
with get_tracer().start_as_current_span("Zero Code", kind=SpanKind.CLIENT) as current_span:
|
|
print(f"Trace ID: {format_trace_id(current_span.get_span_context().trace_id)}")
|
|
|
|
client = OpenAIResponsesClient()
|
|
|
|
await run_chat_client(client, stream=True)
|
|
await run_chat_client(client, stream=False)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|