mirror of
https://github.com/encounter/adk-python.git
synced 2026-07-09 18:19:28 -07:00
Ankur SharmaandCopybara-Service
c69dcf8779
This endpoint could be used by ADK Web to dynamically know: - What are the available eval metrics in an App - A description of those metrics - A value range supported by those metrics We also update the metric registry to make it mandatory to supply these details. The goal is to improve usability and interpretability of the eval metrics. PiperOrigin-RevId: 787277695
364 lines
12 KiB
Python
364 lines
12 KiB
Python
# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from unittest import mock
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from google.adk.agents.llm_agent import LlmAgent
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from google.adk.errors.not_found_error import NotFoundError
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from google.adk.evaluation.base_eval_service import EvaluateConfig
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from google.adk.evaluation.base_eval_service import EvaluateRequest
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from google.adk.evaluation.base_eval_service import InferenceConfig
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from google.adk.evaluation.base_eval_service import InferenceRequest
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from google.adk.evaluation.base_eval_service import InferenceResult
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from google.adk.evaluation.eval_case import Invocation
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from google.adk.evaluation.eval_metrics import EvalMetric
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from google.adk.evaluation.eval_metrics import EvalMetricResult
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from google.adk.evaluation.eval_metrics import Interval
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from google.adk.evaluation.eval_metrics import MetricInfo
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from google.adk.evaluation.eval_metrics import MetricValueInfo
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from google.adk.evaluation.eval_result import EvalCaseResult
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from google.adk.evaluation.eval_set import EvalCase
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from google.adk.evaluation.eval_set import EvalSet
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from google.adk.evaluation.eval_set_results_manager import EvalSetResultsManager
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from google.adk.evaluation.eval_sets_manager import EvalSetsManager
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from google.adk.evaluation.evaluator import EvalStatus
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from google.adk.evaluation.evaluator import EvaluationResult
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from google.adk.evaluation.evaluator import Evaluator
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from google.adk.evaluation.evaluator import PerInvocationResult
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from google.adk.evaluation.local_eval_service import LocalEvalService
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from google.adk.evaluation.metric_evaluator_registry import DEFAULT_METRIC_EVALUATOR_REGISTRY
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from google.adk.models.registry import LLMRegistry
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from google.genai import types as genai_types
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import pytest
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@pytest.fixture
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def mock_eval_sets_manager():
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return mock.create_autospec(EvalSetsManager)
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@pytest.fixture
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def dummy_agent():
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llm = LLMRegistry.new_llm("gemini-pro")
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return LlmAgent(name="test_agent", model=llm)
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@pytest.fixture
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def mock_eval_set_results_manager():
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return mock.create_autospec(EvalSetResultsManager)
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@pytest.fixture
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def eval_service(
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dummy_agent, mock_eval_sets_manager, mock_eval_set_results_manager
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):
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DEFAULT_METRIC_EVALUATOR_REGISTRY.register_evaluator(
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metric_info=FakeEvaluator.get_metric_info(), evaluator=FakeEvaluator
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)
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return LocalEvalService(
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root_agent=dummy_agent,
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eval_sets_manager=mock_eval_sets_manager,
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eval_set_results_manager=mock_eval_set_results_manager,
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)
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class FakeEvaluator(Evaluator):
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def __init__(self, eval_metric: EvalMetric):
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self._eval_metric = eval_metric
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@staticmethod
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def get_metric_info() -> MetricInfo:
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return MetricInfo(
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metric_name="fake_metric",
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description="Fake metric description",
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metric_value_info=MetricValueInfo(
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interval=Interval(min_value=0.0, max_value=1.0)
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),
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)
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def evaluate_invocations(
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self,
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actual_invocations: list[Invocation],
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expected_invocations: list[Invocation],
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):
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per_invocation_results = []
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for actual, expected in zip(actual_invocations, expected_invocations):
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per_invocation_results.append(
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PerInvocationResult(
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actual_invocation=actual,
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expected_invocation=expected,
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score=0.9,
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eval_status=EvalStatus.PASSED,
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)
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)
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return EvaluationResult(
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overall_score=0.9,
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overall_eval_status=EvalStatus.PASSED,
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per_invocation_results=per_invocation_results,
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)
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@pytest.mark.asyncio
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async def test_perform_inference_success(
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eval_service,
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dummy_agent,
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mock_eval_sets_manager,
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):
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eval_set = EvalSet(
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eval_set_id="test_eval_set",
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eval_cases=[
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EvalCase(eval_id="case1", conversation=[], session_input=None),
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EvalCase(eval_id="case2", conversation=[], session_input=None),
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],
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)
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mock_eval_sets_manager.get_eval_set.return_value = eval_set
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mock_inference_result = mock.MagicMock()
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eval_service._perform_inference_sigle_eval_item = mock.AsyncMock(
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return_value=mock_inference_result
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)
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inference_request = InferenceRequest(
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app_name="test_app",
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eval_set_id="test_eval_set",
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inference_config=InferenceConfig(parallelism=2),
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)
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results = []
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async for result in eval_service.perform_inference(inference_request):
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results.append(result)
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assert len(results) == 2
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assert results[0] == mock_inference_result
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assert results[1] == mock_inference_result
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mock_eval_sets_manager.get_eval_set.assert_called_once_with(
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app_name="test_app", eval_set_id="test_eval_set"
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)
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assert eval_service._perform_inference_sigle_eval_item.call_count == 2
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@pytest.mark.asyncio
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async def test_perform_inference_with_case_ids(
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eval_service,
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dummy_agent,
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mock_eval_sets_manager,
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):
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eval_set = EvalSet(
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eval_set_id="test_eval_set",
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eval_cases=[
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EvalCase(eval_id="case1", conversation=[], session_input=None),
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EvalCase(eval_id="case2", conversation=[], session_input=None),
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EvalCase(eval_id="case3", conversation=[], session_input=None),
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],
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)
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mock_eval_sets_manager.get_eval_set.return_value = eval_set
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mock_inference_result = mock.MagicMock()
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eval_service._perform_inference_sigle_eval_item = mock.AsyncMock(
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return_value=mock_inference_result
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)
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inference_request = InferenceRequest(
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app_name="test_app",
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eval_set_id="test_eval_set",
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eval_case_ids=["case1", "case3"],
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inference_config=InferenceConfig(parallelism=1),
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)
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results = []
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async for result in eval_service.perform_inference(inference_request):
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results.append(result)
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assert len(results) == 2
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eval_service._perform_inference_sigle_eval_item.assert_any_call(
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app_name="test_app",
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eval_set_id="test_eval_set",
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eval_case=eval_set.eval_cases[0],
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root_agent=dummy_agent,
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)
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eval_service._perform_inference_sigle_eval_item.assert_any_call(
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app_name="test_app",
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eval_set_id="test_eval_set",
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eval_case=eval_set.eval_cases[2],
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root_agent=dummy_agent,
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)
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@pytest.mark.asyncio
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async def test_perform_inference_eval_set_not_found(
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eval_service,
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mock_eval_sets_manager,
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):
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mock_eval_sets_manager.get_eval_set.return_value = None
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inference_request = InferenceRequest(
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app_name="test_app",
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eval_set_id="not_found_set",
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inference_config=InferenceConfig(parallelism=1),
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)
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with pytest.raises(NotFoundError):
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async for _ in eval_service.perform_inference(inference_request):
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pass
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@pytest.mark.asyncio
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async def test_evaluate_success(
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eval_service, mock_eval_sets_manager, mock_eval_set_results_manager
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):
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inference_results = [
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InferenceResult(
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app_name="test_app",
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eval_set_id="test_eval_set",
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eval_case_id="case1",
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inferences=[],
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session_id="session1",
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),
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InferenceResult(
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app_name="test_app",
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eval_set_id="test_eval_set",
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eval_case_id="case2",
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inferences=[],
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session_id="session2",
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),
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]
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eval_metric = EvalMetric(metric_name="fake_metric", threshold=0.5)
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evaluate_request = EvaluateRequest(
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inference_results=inference_results,
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evaluate_config=EvaluateConfig(eval_metrics=[eval_metric], parallelism=2),
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)
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mock_eval_case = mock.MagicMock(spec=EvalCase)
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mock_eval_case.conversation = []
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mock_eval_case.session_input = None
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mock_eval_sets_manager.get_eval_case.return_value = mock_eval_case
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results = []
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async for result in eval_service.evaluate(evaluate_request):
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results.append(result)
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assert len(results) == 2
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assert isinstance(results[0], EvalCaseResult)
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assert isinstance(results[1], EvalCaseResult)
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assert mock_eval_sets_manager.get_eval_case.call_count == 2
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assert mock_eval_set_results_manager.save_eval_set_result.call_count == 2
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@pytest.mark.asyncio
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async def test_evaluate_eval_case_not_found(
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eval_service,
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mock_eval_sets_manager,
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):
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inference_results = [
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InferenceResult(
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app_name="test_app",
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eval_set_id="test_eval_set",
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eval_case_id="case1",
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inferences=[],
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session_id="session1",
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),
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]
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eval_metric = EvalMetric(metric_name="fake_metric", threshold=0.5)
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evaluate_request = EvaluateRequest(
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inference_results=inference_results,
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evaluate_config=EvaluateConfig(eval_metrics=[eval_metric], parallelism=1),
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)
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mock_eval_sets_manager.get_eval_case.return_value = None
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with pytest.raises(NotFoundError):
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async for _ in eval_service.evaluate(evaluate_request):
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pass
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mock_eval_sets_manager.get_eval_case.assert_called_once()
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@pytest.mark.asyncio
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async def test_evaluate_single_inference_result(
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eval_service, mock_eval_sets_manager, mock_eval_set_results_manager
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):
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invocation = Invocation(
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user_content=genai_types.Content(
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parts=[genai_types.Part(text="test user content.")]
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),
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final_response=genai_types.Content(
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parts=[genai_types.Part(text="test final response.")]
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),
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)
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inference_result = InferenceResult(
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app_name="test_app",
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eval_set_id="test_eval_set",
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eval_case_id="case1",
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inferences=[
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invocation.model_copy(deep=True),
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invocation.model_copy(deep=True),
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invocation.model_copy(deep=True),
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],
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session_id="session1",
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)
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eval_metric = EvalMetric(metric_name="fake_metric", threshold=0.5)
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evaluate_config = EvaluateConfig(eval_metrics=[eval_metric], parallelism=1)
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mock_eval_case = mock.MagicMock(spec=EvalCase)
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mock_eval_case.conversation = [
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invocation.model_copy(deep=True),
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invocation.model_copy(deep=True),
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invocation.model_copy(deep=True),
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]
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mock_eval_case.session_input = None
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mock_eval_sets_manager.get_eval_case.return_value = mock_eval_case
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_, result = await eval_service._evaluate_single_inference_result(
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inference_result=inference_result, evaluate_config=evaluate_config
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)
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assert isinstance(result, EvalCaseResult)
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assert result.eval_id == "case1"
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assert result.session_id == "session1"
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assert len(result.overall_eval_metric_results) == 1
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assert result.overall_eval_metric_results[0].metric_name == "fake_metric"
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assert result.overall_eval_metric_results[0].score == 0.9
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mock_eval_sets_manager.get_eval_case.assert_called_once_with(
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app_name="test_app", eval_set_id="test_eval_set", eval_case_id="case1"
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)
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assert len(result.eval_metric_result_per_invocation) == 3
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for i in range(3):
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invocation_result = result.eval_metric_result_per_invocation[i]
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assert invocation_result.actual_invocation == inference_result.inferences[i]
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assert (
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invocation_result.expected_invocation == mock_eval_case.conversation[i]
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)
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assert len(invocation_result.eval_metric_results) == 1
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metric_result = invocation_result.eval_metric_results[0]
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assert metric_result.metric_name == "fake_metric"
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assert metric_result.score == 0.9
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assert metric_result.eval_status == EvalStatus.PASSED
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def test_generate_final_eval_status_doesn_t_throw_on(eval_service):
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# How to fix if this test case fails?
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# This test case has failed mainly because a new EvalStatus got added. You
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# mostly need to update _generate_final_eval_status method to handle the new
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# eval case.
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# We go over all the possible values of EvalStatus one by one and expect
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# the _generate_final_eval_status to handle it without throwing an exeception.
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for status in EvalStatus:
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eval_metric_result = EvalMetricResult(
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metric_name="metric1", threshold=0.5, eval_status=status
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)
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eval_service._generate_final_eval_status([eval_metric_result])
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