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

Author: KPJoshi

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]],
-      conversation_scenario: Optional[ConversationScenario],
+      expected_invocations: Optional[list[Invocation]] = None,
+      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 @@ def get_metric_info() -> MetricInfo:
   def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
-      _: Optional[ConversationScenario] = None,
+      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.")
+    del conversation_scenario  # not used by this metric.
 
     total_score = 0.0
     num_invocations = 0

src/google/adk/evaluation/hallucinations_v1.py

@@ -720,16 +720,19 @@ def _aggregate_invocation_results(
   async def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
-      _: Optional[ConversationScenario] = None,
+      expected_invocations: Optional[list[Invocation]] = 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 = (
         [None] * len(actual_invocations)
         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 @@ def aggregate_invocation_results(
   async def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
-      _: Optional[ConversationScenario] = None,
+      expected_invocations: Optional[list[Invocation]] = 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 @@ async def _evaluate_metric(
       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

@@ -100,20 +100,22 @@ def get_metric_info(metric_name: str) -> MetricInfo:
   def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
-      _: Optional[ConversationScenario] = None,
+      expected_invocations: Optional[list[Invocation]] = 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:
       rouge_evaluator = RougeEvaluator(
           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_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 @@ def get_metric_info() -> MetricInfo:
   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 @@ def get_metric_info() -> MetricInfo:
   async def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
-      conversation_scenario: Optional[ConversationScenario],
+      expected_invocations: Optional[list[Invocation]] = None,
+      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 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 @@ def get_metric_info() -> MetricInfo:
   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 @@ def __init__(
   def evaluate_invocations(
       self,
       actual_invocations: list[Invocation],
-      expected_invocations: Optional[list[Invocation]],
-      _: Optional[ConversationScenario] = None,
+      expected_invocations: Optional[list[Invocation]] = 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.