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
89 lines
3.3 KiB
Python
89 lines
3.3 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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# type: ignore
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import asyncio
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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 HostedCodeInterpreterTool
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from agent_framework.foundry import FoundryChatClient
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from agent_framework.observability import get_tracer, setup_observability
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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 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 leverage the built-in telemetry in Foundry.
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It uses the Foundry client to setup the telemetry, this calls
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out to Foundry for a telemetry connection strings,
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and then call the setup_observability function in the agent framework.
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If you want to compare with the trace sent to a generic OTLP endpoint,
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switch the `use_foundry_telemetry` variable to False.
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"""
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# ANSI color codes for printing in blue and resetting after each print
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BLUE = "\x1b[34m"
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RESET = "\x1b[0m"
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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() -> 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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In foundry you will also see specific operations happening that are called by the Foundry implementation,
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such as `create_agent`.
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"""
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use_foundry_obs = True
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questions = [
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"What's the weather in Amsterdam and in Paris?",
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"Why is the sky blue?",
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"Tell me about AI.",
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"Can you write a python function that adds two numbers? and use it to add 8483 and 5692?",
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]
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async with (
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AzureCliCredential() as credential,
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AIProjectClient(endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"], credential=credential) as project,
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FoundryChatClient(client=project, setup_tracing=False) as client,
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):
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if use_foundry_obs:
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await client.setup_foundry_observability(enable_live_metrics=True)
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else:
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setup_observability()
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with get_tracer().start_as_current_span(
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name="Foundry Telemetry from Agent Framework", kind=SpanKind.CLIENT
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) as span:
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for question in questions:
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print(f"{BLUE}User: {question}{RESET}")
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print(f"{BLUE}Assistant: {RESET}", end="")
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async for chunk in client.get_streaming_response(
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question, tools=[get_weather, HostedCodeInterpreterTool()]
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):
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if str(chunk):
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print(f"{BLUE}{str(chunk)}{RESET}", end="")
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print(f"{BLUE}{RESET}")
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print(f"{BLUE}Done{RESET}")
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print(f"{BLUE}Operation ID: {format_trace_id(span.get_span_context().trace_id)}{RESET}")
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if __name__ == "__main__":
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asyncio.run(main())
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