Ren Finlayson
539852f81c
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102 lines
3.6 KiB
Python
102 lines
3.6 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from random import randint
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from typing import TYPE_CHECKING, Annotated
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from agent_framework.observability import get_tracer
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from agent_framework.openai import OpenAIResponsesClient
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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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if TYPE_CHECKING:
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from agent_framework import ChatClientProtocol
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"""
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This sample shows how you can configure observability of an application with zero code changes.
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It relies on the OpenTelemetry auto-instrumentation capabilities, and the observability setup
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is done via environment variables.
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Follow the install guidance from https://opentelemetry.io/docs/zero-code/python/ to install the OpenTelemetry CLI tool.
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And setup a local OpenTelemetry Collector instance to receive the traces and metrics (and update the endpoint below).
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Then you can run:
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```bash
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opentelemetry-enable_instrumentation \
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--traces_exporter otlp \
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--metrics_exporter otlp \
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--service_name agent_framework \
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--exporter_otlp_endpoint http://localhost:4317 \
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python samples/getting_started/observability/advanced_zero_code.py
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```
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(or use uv run in front when you have did the install within your uv virtual environment)
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You can also set the environment variables instead of passing them as CLI arguments.
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"""
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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 run_chat_client(client: "ChatClientProtocol", stream: bool = False) -> None:
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"""Run an AI service.
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This function runs an AI service and prints the output.
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Telemetry will be collected for the service execution behind the scenes,
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and the traces will be sent to the configured telemetry backend.
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The telemetry will include information about the AI service execution.
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Args:
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stream: Whether to use streaming for the plugin
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Remarks:
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When function calling is outside the open telemetry loop
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each of the call to the model is handled as a seperate span,
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while when the open telemetry is put last, a single span
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is shown, which might include one or more rounds of function calling.
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So for the scenario below, you should see the following:
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2 spans with gen_ai.operation.name=chat
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The first has finish_reason "tool_calls"
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The second has finish_reason "stop"
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2 spans with gen_ai.operation.name=execute_tool
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"""
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message = "What's the weather in Amsterdam and in Paris?"
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print(f"User: {message}")
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if stream:
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print("Assistant: ", end="")
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async for chunk in client.get_streaming_response(message, tools=get_weather):
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if str(chunk):
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print(str(chunk), end="")
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print("")
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else:
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response = await client.get_response(message, tools=get_weather)
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print(f"Assistant: {response}")
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async def main() -> None:
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with get_tracer().start_as_current_span("Zero Code", 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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client = OpenAIResponsesClient()
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await run_chat_client(client, stream=True)
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await run_chat_client(client, stream=False)
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if __name__ == "__main__":
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asyncio.run(main())
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