chore: import upstream snapshot with attribution
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# Copyright (c) Microsoft. All rights reserved.
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"""Evaluate an agent with expected outputs and tool call checks.
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Demonstrates ground-truth comparison and tool usage evaluation:
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1. Provide expected outputs alongside queries
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2. Use built-in tool_calls_present for tool verification
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3. Combine multiple evaluation criteria
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Usage:
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uv run python samples/02-agents/evaluation/evaluate_with_expected.py
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"""
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import asyncio
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import os
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from agent_framework import (
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Agent,
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LocalEvaluator,
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evaluate_agent,
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evaluator,
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tool_calls_present,
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)
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from agent_framework.foundry import FoundryChatClient
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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load_dotenv()
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@evaluator
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def response_matches_expected(response: str, expected_output: str) -> float:
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"""Score based on word overlap with expected output."""
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if not expected_output:
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return 1.0
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response_words = set(response.lower().split())
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expected_words = set(expected_output.lower().split())
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return len(response_words & expected_words) / max(len(expected_words), 1)
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async def main() -> None:
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ.get("FOUNDRY_MODEL", "gpt-4o"),
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credential=AzureCliCredential(),
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)
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agent = Agent(
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client=client,
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name="math-tutor",
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instructions="You are a math tutor. Answer concisely.",
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)
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local = LocalEvaluator(
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response_matches_expected,
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tool_calls_present, # verifies expected tools were called
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)
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results = await evaluate_agent(
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agent=agent,
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queries=["What is 2 + 2?", "What is the square root of 144?"],
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expected_output=["4", "12"],
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evaluators=local,
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)
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for r in results:
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print(f"{r.provider}: {r.passed}/{r.total} passed")
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for item in r.items:
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print(f" [{item.status}] {item.input_text} -> {(item.output_text or '')[:80]}")
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if __name__ == "__main__":
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asyncio.run(main())
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