mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Eduard van Valkenburg
2576e7a091
* updated telemetry work * updated telemetry * slight improvement * updated tests * fixes for telemetry * fixes for mypy * added settings setup to runner to avoid error * streamline usage * updated tests * updated tests * further refinement * fix dumped item for otel * removed enable_workflow_otel * final fixes * final fixes * updated samples * removed exporters * fix tests * fixed last import' * fixed devui
127 lines
4.7 KiB
Python
127 lines
4.7 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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# type: ignore
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import argparse
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import asyncio
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from contextlib import suppress
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from random import randint
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from typing import TYPE_CHECKING, Annotated, Literal
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from agent_framework import ai_function
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from agent_framework.observability import get_tracer, setup_observability
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from agent_framework.openai import OpenAIResponsesClient
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from opentelemetry import trace
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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, show how you can get telemetry from a chat client and tool.
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it uses the `tracer` that is configured by agent framework,
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which also sets up the traces with the configured environment.
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"""
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# Define the scenarios that can be run
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SCENARIOS = ["chat_client", "chat_client_stream", "ai_function", "all"]
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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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client: The chat client to use.
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stream: Whether to use streaming for the response
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Remarks:
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For the scenario below, you should see the following:
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1 Client span, with 4 children:
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2 Internal span 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 Internal span with gen_ai.operation.name=execute_tool
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"""
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scenario_name = "Chat Client Stream" if stream else "Chat Client"
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with get_tracer().start_as_current_span(name=f"Scenario: {scenario_name}", kind=trace.SpanKind.CLIENT):
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print("Running scenario:", scenario_name)
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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 run_ai_function() -> None:
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"""Run a AI function.
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This function runs a AI function and prints the output.
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Telemetry will be collected for the function 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 function execution
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and the AI service execution.
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"""
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with get_tracer().start_as_current_span("Scenario: AI Function", kind=trace.SpanKind.CLIENT):
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print("Running scenario: AI Function")
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func = ai_function(get_weather)
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weather = await func.invoke(location="Amsterdam")
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print(f"Weather in Amsterdam:\n{weather}")
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async def main(scenario: Literal["chat_client", "chat_client_stream", "ai_function", "all"] = "all"):
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"""Run the selected scenario(s)."""
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setup_observability()
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with get_tracer().start_as_current_span("Sample Scenario's", kind=trace.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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# Scenarios where telemetry is collected in the SDK, from the most basic to the most complex.
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if scenario == "ai_function" or scenario == "all":
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with suppress(Exception):
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await run_ai_function()
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if scenario == "chat_client_stream" or scenario == "all":
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with suppress(Exception):
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await run_chat_client(client, stream=True)
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if scenario == "chat_client" or scenario == "all":
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with suppress(Exception):
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await run_chat_client(client, stream=False)
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if __name__ == "__main__":
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument(
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"--scenario",
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type=str,
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choices=SCENARIOS,
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default="all",
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help="The scenario to run. Default is all.",
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)
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args = arg_parser.parse_args()
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asyncio.run(main(args.scenario))
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