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
121 lines
4.1 KiB
Python
121 lines
4.1 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 __future__ import annotations
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from google.adk.errors.not_found_error import NotFoundError
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from google.adk.evaluation.eval_metrics import EvalMetric
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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.evaluator import Evaluator
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from google.adk.evaluation.metric_evaluator_registry import MetricEvaluatorRegistry
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import pytest
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_DUMMY_METRIC_NAME = "dummy_metric_name"
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class TestMetricEvaluatorRegistry:
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"""Test cases for MetricEvaluatorRegistry."""
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@pytest.fixture
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def registry(self):
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return MetricEvaluatorRegistry()
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class DummyEvaluator(Evaluator):
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def __init__(self, eval_metric: EvalMetric):
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self._eval_metric = eval_metric
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def evaluate_invocations(self, actual_invocations, expected_invocations):
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return "dummy_result"
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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=_DUMMY_METRIC_NAME,
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description="Dummy 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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class AnotherDummyEvaluator(Evaluator):
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def __init__(self, eval_metric: EvalMetric):
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self._eval_metric = eval_metric
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def evaluate_invocations(self, actual_invocations, expected_invocations):
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return "another_dummy_result"
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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=_DUMMY_METRIC_NAME,
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description="Another dummy 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 test_register_evaluator(self, registry):
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metric_info = TestMetricEvaluatorRegistry.DummyEvaluator.get_metric_info()
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registry.register_evaluator(
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metric_info,
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TestMetricEvaluatorRegistry.DummyEvaluator,
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)
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assert _DUMMY_METRIC_NAME in registry._registry
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assert registry._registry[_DUMMY_METRIC_NAME] == (
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TestMetricEvaluatorRegistry.DummyEvaluator,
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metric_info,
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)
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def test_register_evaluator_updates_existing(self, registry):
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metric_info = TestMetricEvaluatorRegistry.DummyEvaluator.get_metric_info()
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registry.register_evaluator(
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metric_info,
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TestMetricEvaluatorRegistry.DummyEvaluator,
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)
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assert registry._registry[_DUMMY_METRIC_NAME] == (
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TestMetricEvaluatorRegistry.DummyEvaluator,
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metric_info,
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)
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metric_info = (
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TestMetricEvaluatorRegistry.AnotherDummyEvaluator.get_metric_info()
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)
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registry.register_evaluator(
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metric_info, TestMetricEvaluatorRegistry.AnotherDummyEvaluator
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)
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assert registry._registry[_DUMMY_METRIC_NAME] == (
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TestMetricEvaluatorRegistry.AnotherDummyEvaluator,
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metric_info,
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)
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def test_get_evaluator(self, registry):
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metric_info = TestMetricEvaluatorRegistry.DummyEvaluator.get_metric_info()
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registry.register_evaluator(
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metric_info,
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TestMetricEvaluatorRegistry.DummyEvaluator,
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)
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eval_metric = EvalMetric(metric_name=_DUMMY_METRIC_NAME, threshold=0.5)
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evaluator = registry.get_evaluator(eval_metric)
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assert isinstance(evaluator, TestMetricEvaluatorRegistry.DummyEvaluator)
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def test_get_evaluator_not_found(self, registry):
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eval_metric = EvalMetric(metric_name="non_existent_metric", threshold=0.5)
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with pytest.raises(NotFoundError):
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registry.get_evaluator(eval_metric)
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