mirror of
https://github.com/encounter/adk-python.git
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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
87 lines
3.2 KiB
Python
87 lines
3.2 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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"""Tests for the Response Evaluator."""
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from unittest.mock import patch
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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 PrebuiltMetrics
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from google.adk.evaluation.evaluator import EvalStatus
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from google.adk.evaluation.safety_evaluator import SafetyEvaluatorV1
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from google.genai import types as genai_types
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from vertexai import types as vertexai_types
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@patch(
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"google.adk.evaluation.vertex_ai_eval_facade._VertexAiEvalFacade._perform_eval"
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)
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class TestSafetyEvaluatorV1:
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"""A class to help organize "patch" that are applicable to all tests."""
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def test_evaluate_invocations_coherence_metric_passed(
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self, mock_perform_eval
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):
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"""Test evaluate_invocations function for Coherence metric."""
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actual_invocations = [
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Invocation(
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user_content=genai_types.Content(
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parts=[genai_types.Part(text="This is a test query.")]
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),
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final_response=genai_types.Content(
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parts=[
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genai_types.Part(text="This is a test candidate response.")
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]
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),
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)
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]
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expected_invocations = [
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Invocation(
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user_content=genai_types.Content(
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parts=[genai_types.Part(text="This is a test query.")]
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),
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final_response=genai_types.Content(
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parts=[genai_types.Part(text="This is a test reference.")]
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),
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)
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]
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evaluator = SafetyEvaluatorV1(
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eval_metric=EvalMetric(threshold=0.8, metric_name="safety")
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)
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# Mock the return value of _perform_eval
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mock_perform_eval.return_value = vertexai_types.EvaluationResult(
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summary_metrics=[vertexai_types.AggregatedMetricResult(mean_score=0.9)],
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eval_case_results=[],
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)
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evaluation_result = evaluator.evaluate_invocations(
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actual_invocations, expected_invocations
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)
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assert evaluation_result.overall_score == 0.9
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assert evaluation_result.overall_eval_status == EvalStatus.PASSED
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mock_perform_eval.assert_called_once()
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_, mock_kwargs = mock_perform_eval.call_args
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# Compare the names of the metrics.
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assert [m.name for m in mock_kwargs["metrics"]] == [
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vertexai_types.PrebuiltMetric.SAFETY.name
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]
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def test_get_metric_info(self, mock_perform_eval):
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"""Test get_metric_info function for Safety metric."""
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metric_info = SafetyEvaluatorV1.get_metric_info()
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assert metric_info.metric_name == PrebuiltMetrics.SAFETY_V1.value
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assert metric_info.metric_value_info.interval.min_value == 0.0
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assert metric_info.metric_value_info.interval.max_value == 1.0
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