advanced_zero_code.py

# Copyright (c) Microsoft. All rights reserved.

import asyncio
from random import randint
from typing import TYPE_CHECKING, Annotated

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 ChatClientProtocol


"""
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.

This sample requires the `APPLICATIONINSIGHTS_CONNECTION_STRING` environment variable to be set.

Run the sample with the following command:
```
uv run --env-file=.env opentelemetry-instrument python advanced_zero_code.py
```
"""


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: "ChatClientProtocol", 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 seperate 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_streaming_response(message, tools=get_weather):
            if str(chunk):
                print(str(chunk), end="")
        print("")
    else:
        response = await client.get_response(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())