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79 lines
2.9 KiB
Python
79 lines
2.9 KiB
Python
# Copyright 2026 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 google.adk.dependencies.vertexai import vertexai
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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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vertexai_types = vertexai.types
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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(self, mocker):
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"""Test evaluate_invocations function for Coherence metric."""
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mock_perform_eval = mocker.patch(
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"google.adk.evaluation.vertex_ai_eval_facade._VertexAiEvalFacade._perform_eval"
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)
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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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