# Copyright 2026 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations from google.adk.evaluation.eval_case import Invocation from google.adk.evaluation.eval_metrics import EvalMetric from google.adk.evaluation.eval_metrics import PrebuiltMetrics from google.adk.evaluation.evaluator import EvalStatus from google.adk.evaluation.final_response_match_v1 import _calculate_rouge_1_scores from google.adk.evaluation.final_response_match_v1 import RougeEvaluator from google.genai import types as genai_types import pytest def _create_test_rouge_evaluator(threshold: float) -> RougeEvaluator: return RougeEvaluator( EvalMetric(metric_name="response_match_score", threshold=threshold) ) def _create_test_invocations( candidate: str, reference: str ) -> tuple[Invocation, Invocation]: """Returns tuple of (actual_invocation, expected_invocation).""" return Invocation( user_content=genai_types.Content( parts=[genai_types.Part(text="This is a test query.")] ), final_response=genai_types.Content( parts=[genai_types.Part(text=candidate)] ), ), Invocation( user_content=genai_types.Content( parts=[genai_types.Part(text="This is a test query.")] ), final_response=genai_types.Content( parts=[genai_types.Part(text=reference)] ), ) def test_calculate_rouge_1_scores_empty_candidate_and_reference(): candidate = "" reference = "" rouge_1_score = _calculate_rouge_1_scores(candidate, reference) assert rouge_1_score.precision == 0 assert rouge_1_score.recall == 0 assert rouge_1_score.fmeasure == 0 def test_calculate_rouge_1_scores_empty_candidate(): candidate = "" reference = "This is a test reference." rouge_1_score = _calculate_rouge_1_scores(candidate, reference) assert rouge_1_score.precision == 0 assert rouge_1_score.recall == 0 assert rouge_1_score.fmeasure == 0 def test_calculate_rouge_1_scores_empty_reference(): candidate = "This is a test candidate response." reference = "" rouge_1_score = _calculate_rouge_1_scores(candidate, reference) assert rouge_1_score.precision == 0 assert rouge_1_score.recall == 0 assert rouge_1_score.fmeasure == 0 def test_calculate_rouge_1_scores(): candidate = "This is a test candidate response." reference = "This is a test reference." rouge_1_score = _calculate_rouge_1_scores(candidate, reference) assert rouge_1_score.precision == pytest.approx(2 / 3) assert rouge_1_score.recall == pytest.approx(4 / 5) assert rouge_1_score.fmeasure == pytest.approx(8 / 11) @pytest.mark.parametrize( "candidates, references, expected_score, expected_status", [ ( ["The quick brown fox jumps.", "hello world"], ["The quick brown fox jumps over the lazy dog.", "hello"], 0.69048, # (5/7 + 2/3) / 2 EvalStatus.FAILED, ), ( ["This is a test.", "Another test case."], ["This is a test.", "This is a different test."], 0.625, # (1 + 1/4) / 2 EvalStatus.FAILED, ), ( ["No matching words here.", "Second candidate."], ["Completely different text.", "Another reference."], 0.0, # (0 + 1/2) / 2 EvalStatus.FAILED, ), ( ["Same words", "Same words"], ["Same words", "Same words"], 1.0, EvalStatus.PASSED, ), ], ) def test_rouge_evaluator_multiple_invocations( candidates: list[str], references: list[str], expected_score: float, expected_status: EvalStatus, ): rouge_evaluator = _create_test_rouge_evaluator(threshold=0.8) actual_invocations = [] expected_invocations = [] for candidate, reference in zip(candidates, references): actual_invocation, expected_invocation = _create_test_invocations( candidate, reference ) actual_invocations.append(actual_invocation) expected_invocations.append(expected_invocation) evaluation_result = rouge_evaluator.evaluate_invocations( actual_invocations, expected_invocations ) assert evaluation_result.overall_score == pytest.approx( expected_score, rel=1e-3 ) assert evaluation_result.overall_eval_status == expected_status