fix: fix inconsistent method signatures for evaluate_invocations

The evaluate_invocations method override in Evaluator subclasses was not consistent, leading to errors during calls, especially when using kwargs. Made the overrides and calls consistent to resolve this issue.

Co-authored-by: Keyur Joshi <keyurj@google.com>
PiperOrigin-RevId: 850462752

src/google/adk/evaluation/evaluator.py

@@ -62,8 +62,8 @@ class Evaluator(ABC):
   def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
+      expected_invocations: Optional[list[Invocation]] = None,
-      conversation_scenario: Optional[ConversationScenario],
+      conversation_scenario: Optional[ConversationScenario] = None,
   ) -> EvaluationResult:
     """Returns EvaluationResult after performing evaluations using actual and expected invocations.
 

src/google/adk/evaluation/final_response_match_v1.py

@@ -60,11 +60,12 @@ class RougeEvaluator(Evaluator):
   def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
+      expected_invocations: Optional[list[Invocation]] = None,
-      _: Optional[ConversationScenario] = None,
+      conversation_scenario: Optional[ConversationScenario] = None,
   ) -> EvaluationResult:
     if expected_invocations is None:
       raise ValueError("expected_invocations is required for this metric.")
+    del conversation_scenario  # not used by this metric.
 
     total_score = 0.0
     num_invocations = 0

src/google/adk/evaluation/hallucinations_v1.py

@@ -720,9 +720,11 @@ class HallucinationsV1Evaluator(Evaluator):
   async def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
+      expected_invocations: Optional[list[Invocation]] = None,
-      _: Optional[ConversationScenario] = None,
+      conversation_scenario: Optional[ConversationScenario] = None,
   ) -> EvaluationResult:
+    del conversation_scenario  # not used by this metric.
+
     # expected_invocations are not required by the metric and if they are not
     # supplied, we provide a list of None to rest of the code.
     expected_invocations = (
@@ -730,6 +732,7 @@ class HallucinationsV1Evaluator(Evaluator):
         if expected_invocations is None
         else expected_invocations
     )
+
     per_invocation_results = []
     for actual, expected in zip(actual_invocations, expected_invocations):
       step_evaluations = self._get_steps_to_evaluate(actual)

src/google/adk/evaluation/llm_as_judge.py

@@ -118,11 +118,12 @@ class LlmAsJudge(Evaluator):
   async def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
+      expected_invocations: Optional[list[Invocation]] = None,
-      _: Optional[ConversationScenario] = None,
+      conversation_scenario: Optional[ConversationScenario] = None,
   ) -> EvaluationResult:
     if self._expected_invocations_required and expected_invocations is None:
       raise ValueError("expected_invocations is needed by this metric.")
+    del conversation_scenario  # not supported for per-invocation evaluation.
 
     # If expected_invocation are not required by the metric and if they are not
     # supplied, we provide a list of None.

src/google/adk/evaluation/local_eval_service.py

@@ -370,6 +370,7 @@ class LocalEvalService(BaseEvalService):
       return metric_evaluator.evaluate_invocations(
           actual_invocations=actual_invocations,
           expected_invocations=expected_invocations,
+          conversation_scenario=conversation_scenario,
       )
 
   def _generate_final_eval_status(

src/google/adk/evaluation/response_evaluator.py

@@ -100,8 +100,8 @@ class ResponseEvaluator(Evaluator):
   def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
+      expected_invocations: Optional[list[Invocation]] = None,
-      _: Optional[ConversationScenario] = None,
+      conversation_scenario: Optional[ConversationScenario] = None,
   ) -> EvaluationResult:
     # If the metric is response_match_score, just use the RougeEvaluator.
     if self._metric_name == PrebuiltMetrics.RESPONSE_MATCH_SCORE.value:
@@ -109,11 +109,13 @@ class ResponseEvaluator(Evaluator):
           EvalMetric(metric_name=self._metric_name, threshold=self._threshold)
       )
       return rouge_evaluator.evaluate_invocations(
-          actual_invocations, expected_invocations
+          actual_invocations, expected_invocations, conversation_scenario
       )
 
     return _VertexAiEvalFacade(
         threshold=self._threshold,
         metric_name=self._metric_name,
         expected_invocations_required=True,
-    ).evaluate_invocations(actual_invocations, expected_invocations)
+    ).evaluate_invocations(
+        actual_invocations, expected_invocations, conversation_scenario
+    )

src/google/adk/evaluation/safety_evaluator.py

@@ -18,6 +18,7 @@ from typing import Optional
 
 from typing_extensions import override
 
+from .eval_case import ConversationScenario
 from .eval_case import Invocation
 from .eval_metrics import EvalMetric
 from .eval_metrics import Interval
@@ -65,11 +66,14 @@ class SafetyEvaluatorV1(Evaluator):
   def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
+      expected_invocations: Optional[list[Invocation]] = None,
+      conversation_scenario: Optional[ConversationScenario] = None,
   ) -> EvaluationResult:
     from ..dependencies.vertexai import vertexai
 
     return _VertexAiEvalFacade(
         threshold=self._eval_metric.threshold,
         metric_name=vertexai.types.PrebuiltMetric.SAFETY,
-    ).evaluate_invocations(actual_invocations, expected_invocations)
+    ).evaluate_invocations(
+        actual_invocations, expected_invocations, conversation_scenario
+    )

src/google/adk/evaluation/simulation/per_turn_user_simulator_quality_v1.py

@@ -290,10 +290,12 @@ class PerTurnUserSimulatorQualityV1(Evaluator):
   async def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
+      expected_invocations: Optional[list[Invocation]] = None,
-      conversation_scenario: Optional[ConversationScenario],
+      conversation_scenario: Optional[ConversationScenario] = None,
   ) -> EvaluationResult:
-    del expected_invocations
+    del expected_invocations  # not used by this metric.
+    if conversation_scenario is None:
+      raise ValueError("conversation_scenario is needed by this metric.")
 
     # Evaluate the first invocation contains the given starting prompt.
     results = [

src/google/adk/evaluation/trajectory_evaluator.py

@@ -22,6 +22,7 @@ from google.genai import types as genai_types
 from pydantic import ValidationError
 from typing_extensions import override
 
+from .eval_case import ConversationScenario
 from .eval_case import get_all_tool_calls
 from .eval_case import Invocation
 from .eval_metrics import EvalMetric
@@ -118,11 +119,13 @@ class TrajectoryEvaluator(Evaluator):
   def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
+      expected_invocations: Optional[list[Invocation]] = None,
+      conversation_scenario: Optional[ConversationScenario] = None,
   ) -> EvaluationResult:
     """Returns EvaluationResult after performing evaluations using actual and expected invocations."""
     if expected_invocations is None:
       raise ValueError("expected_invocations is needed by this metric.")
+    del conversation_scenario  # not supported for per-invocation evaluation.
 
     total_tool_use_accuracy = 0.0
     num_invocations = 0

src/google/adk/evaluation/vertex_ai_eval_facade.py

@@ -69,11 +69,12 @@ class _VertexAiEvalFacade(Evaluator):
   def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
+      expected_invocations: Optional[list[Invocation]] = None,
-      _: Optional[ConversationScenario] = None,
+      conversation_scenario: Optional[ConversationScenario] = None,
   ) -> EvaluationResult:
     if self._expected_invocations_required and expected_invocations is None:
       raise ValueError("expected_invocations is needed by this metric.")
+    del conversation_scenario  # not supported for per-invocation evaluation.
 
     # If expected_invocation are not required by the metric and if they are not
     # supplied, we provide a list of None.

tests/unittests/evaluation/test_local_eval_service.py

@@ -26,6 +26,7 @@ from google.adk.evaluation.base_eval_service import InferenceConfig
 from google.adk.evaluation.base_eval_service import InferenceRequest
 from google.adk.evaluation.base_eval_service import InferenceResult
 from google.adk.evaluation.base_eval_service import InferenceStatus
+from google.adk.evaluation.conversation_scenarios import ConversationScenario
 from google.adk.evaluation.eval_case import Invocation
 from google.adk.evaluation.eval_metrics import EvalMetric
 from google.adk.evaluation.eval_metrics import EvalMetricResult
@@ -46,6 +47,7 @@ from google.adk.evaluation.metric_evaluator_registry import DEFAULT_METRIC_EVALU
 from google.adk.models.registry import LLMRegistry
 from google.genai import types as genai_types
 import pytest
+from typing_extensions import override
 
 
 @pytest.fixture
@@ -97,11 +99,13 @@ class FakeEvaluator(Evaluator):
         ),
     )
 
+  @override
   def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
+      expected_invocations: Optional[list[Invocation]] = None,
-  ):
+      conversation_scenario: Optional[ConversationScenario] = None,
+  ) -> EvaluationResult:
     if expected_invocations is None:
       raise ValueError("expected_invocations is required for this metric.")
     per_invocation_results = []
@@ -136,11 +140,13 @@ class FakeSingleSidedEvaluator(Evaluator):
         ),
     )
 
+  @override
   def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
+      expected_invocations: Optional[list[Invocation]] = None,
-  ):
+      conversation_scenario: Optional[ConversationScenario] = None,
+  ) -> EvaluationResult:
     per_invocation_results = []
     for actual in actual_invocations:
       per_invocation_results.append(