chore: Introduce MetricInfoProvider interface, and refactor metric evaluators to use this interface to provide MetricInfo

Co-authored-by: Joseph Pagadora <jcpagadora@google.com>
PiperOrigin-RevId: 851406110

src/google/adk/evaluation/eval_metrics.py

@@ -14,6 +14,7 @@
 
 from __future__ import annotations
 
+import abc
 from enum import Enum
 from typing import Optional
 from typing import Union
@@ -362,3 +363,12 @@ class MetricInfo(EvalBaseModel):
   metric_value_info: MetricValueInfo = Field(
       description="Information on the nature of values supported by the metric."
   )
+
+
+class MetricInfoProvider(abc.ABC):
+  """Interface for providing MetricInfo."""
+
+  @abc.abstractmethod
+  def get_metric_info(self) -> MetricInfo:
+    """Returns MetricInfo for a given metric."""
+    raise NotImplementedError

src/google/adk/evaluation/final_response_match_v1.py

@@ -23,10 +23,6 @@ from ..dependencies.rouge_scorer import rouge_scorer
 from .eval_case import ConversationScenario
 from .eval_case import Invocation
 from .eval_metrics import EvalMetric
-from .eval_metrics import Interval
-from .eval_metrics import MetricInfo
-from .eval_metrics import MetricValueInfo
-from .eval_metrics import PrebuiltMetrics
 from .evaluator import EvalStatus
 from .evaluator import EvaluationResult
 from .evaluator import Evaluator
@@ -42,20 +38,6 @@ class RougeEvaluator(Evaluator):
   def __init__(self, eval_metric: EvalMetric):
     self._eval_metric = eval_metric
 
-  @staticmethod
-  def get_metric_info() -> MetricInfo:
-    return MetricInfo(
-        metric_name=PrebuiltMetrics.RESPONSE_MATCH_SCORE.value,
-        description=(
-            "This metric evaluates if the agent's final response matches a"
-            " golden/expected final response using Rouge_1 metric. Value range"
-            " for this metric is [0,1], with values closer to 1 more desirable."
-        ),
-        metric_value_info=MetricValueInfo(
-            interval=Interval(min_value=0.0, max_value=1.0)
-        ),
-    )
-
   @override
   def evaluate_invocations(
       self,

src/google/adk/evaluation/final_response_match_v2.py

@@ -26,11 +26,7 @@ from ..utils.feature_decorator import experimental
 from .eval_case import Invocation
 from .eval_metrics import EvalMetric
 from .eval_metrics import EvalStatus
-from .eval_metrics import Interval
 from .eval_metrics import LlmAsAJudgeCriterion
-from .eval_metrics import MetricInfo
-from .eval_metrics import MetricValueInfo
-from .eval_metrics import PrebuiltMetrics
 from .evaluator import EvaluationResult
 from .evaluator import PerInvocationResult
 from .llm_as_judge import AutoRaterScore
@@ -154,20 +150,6 @@ class FinalResponseMatchV2Evaluator(LlmAsJudge):
     )
     self._auto_rater_prompt_template = _FINAL_RESPONSE_MATCH_V2_PROMPT
 
-  @staticmethod
-  def get_metric_info() -> MetricInfo:
-    return MetricInfo(
-        metric_name=PrebuiltMetrics.FINAL_RESPONSE_MATCH_V2.value,
-        description=(
-            "This metric evaluates if the agent's final response matches a"
-            " golden/expected final response using LLM as a judge. Value range"
-            " for this metric is [0,1], with values closer to 1 more desirable."
-        ),
-        metric_value_info=MetricValueInfo(
-            interval=Interval(min_value=0.0, max_value=1.0)
-        ),
-    )
-
   @override
   def format_auto_rater_prompt(
       self,

src/google/adk/evaluation/hallucinations_v1.py

@@ -40,10 +40,6 @@ from .eval_case import InvocationEvent
 from .eval_case import InvocationEvents
 from .eval_metrics import EvalMetric
 from .eval_metrics import HallucinationsCriterion
-from .eval_metrics import Interval
-from .eval_metrics import MetricInfo
-from .eval_metrics import MetricValueInfo
-from .eval_metrics import PrebuiltMetrics
 from .evaluator import EvalStatus
 from .evaluator import EvaluationResult
 from .evaluator import Evaluator
@@ -310,21 +306,6 @@ class HallucinationsV1Evaluator(Evaluator):
     llm_class = llm_registry.resolve(model_id)
     return llm_class(model=model_id)
 
-  @staticmethod
-  def get_metric_info() -> MetricInfo:
-    return MetricInfo(
-        metric_name=PrebuiltMetrics.HALLUCINATIONS_V1.value,
-        description=(
-            "This metric assesses whether a model response contains any false,"
-            " contradictory, or unsupported claims using a LLM as judge. Value"
-            " range for this metric is [0,1], with values closer to 1 more"
-            " desirable."
-        ),
-        metric_value_info=MetricValueInfo(
-            interval=Interval(min_value=0.0, max_value=1.0)
-        ),
-    )
-
   def _create_context_for_step(
       self,
       app_details: Optional[AppDetails],

src/google/adk/evaluation/metric_evaluator_registry.py

@@ -24,6 +24,14 @@ from .eval_metrics import PrebuiltMetrics
 from .evaluator import Evaluator
 from .final_response_match_v2 import FinalResponseMatchV2Evaluator
 from .hallucinations_v1 import HallucinationsV1Evaluator
+from .metric_info_providers import FinalResponseMatchV2EvaluatorMetricInfoProvider
+from .metric_info_providers import HallucinationsV1EvaluatorMetricInfoProvider
+from .metric_info_providers import PerTurnUserSimulatorQualityV1MetricInfoProvider
+from .metric_info_providers import ResponseEvaluatorMetricInfoProvider
+from .metric_info_providers import RubricBasedFinalResponseQualityV1EvaluatorMetricInfoProvider
+from .metric_info_providers import RubricBasedToolUseV1EvaluatorMetricInfoProvider
+from .metric_info_providers import SafetyEvaluatorV1MetricInfoProvider
+from .metric_info_providers import TrajectoryEvaluatorMetricInfoProvider
 from .response_evaluator import ResponseEvaluator
 from .rubric_based_final_response_quality_v1 import RubricBasedFinalResponseQualityV1Evaluator
 from .rubric_based_tool_use_quality_v1 import RubricBasedToolUseV1Evaluator
@@ -91,44 +99,44 @@ def _get_default_metric_evaluator_registry() -> MetricEvaluatorRegistry:
   metric_evaluator_registry = MetricEvaluatorRegistry()
 
   metric_evaluator_registry.register_evaluator(
-      metric_info=TrajectoryEvaluator.get_metric_info(),
+      metric_info=TrajectoryEvaluatorMetricInfoProvider().get_metric_info(),
       evaluator=TrajectoryEvaluator,
   )
 
   metric_evaluator_registry.register_evaluator(
-      metric_info=ResponseEvaluator.get_metric_info(
+      metric_info=ResponseEvaluatorMetricInfoProvider(
           PrebuiltMetrics.RESPONSE_EVALUATION_SCORE.value
-      ),
+      ).get_metric_info(),
       evaluator=ResponseEvaluator,
   )
   metric_evaluator_registry.register_evaluator(
-      metric_info=ResponseEvaluator.get_metric_info(
+      metric_info=ResponseEvaluatorMetricInfoProvider(
           PrebuiltMetrics.RESPONSE_MATCH_SCORE.value
-      ),
+      ).get_metric_info(),
       evaluator=ResponseEvaluator,
   )
   metric_evaluator_registry.register_evaluator(
-      metric_info=SafetyEvaluatorV1.get_metric_info(),
+      metric_info=SafetyEvaluatorV1MetricInfoProvider().get_metric_info(),
       evaluator=SafetyEvaluatorV1,
   )
   metric_evaluator_registry.register_evaluator(
-      metric_info=FinalResponseMatchV2Evaluator.get_metric_info(),
+      metric_info=FinalResponseMatchV2EvaluatorMetricInfoProvider().get_metric_info(),
       evaluator=FinalResponseMatchV2Evaluator,
   )
   metric_evaluator_registry.register_evaluator(
-      metric_info=RubricBasedFinalResponseQualityV1Evaluator.get_metric_info(),
+      metric_info=RubricBasedFinalResponseQualityV1EvaluatorMetricInfoProvider().get_metric_info(),
       evaluator=RubricBasedFinalResponseQualityV1Evaluator,
   )
   metric_evaluator_registry.register_evaluator(
-      metric_info=HallucinationsV1Evaluator.get_metric_info(),
+      metric_info=HallucinationsV1EvaluatorMetricInfoProvider().get_metric_info(),
       evaluator=HallucinationsV1Evaluator,
   )
   metric_evaluator_registry.register_evaluator(
-      metric_info=RubricBasedToolUseV1Evaluator.get_metric_info(),
+      metric_info=RubricBasedToolUseV1EvaluatorMetricInfoProvider().get_metric_info(),
       evaluator=RubricBasedToolUseV1Evaluator,
   )
   metric_evaluator_registry.register_evaluator(
-      metric_info=PerTurnUserSimulatorQualityV1.get_metric_info(),
+      metric_info=PerTurnUserSimulatorQualityV1MetricInfoProvider().get_metric_info(),
       evaluator=PerTurnUserSimulatorQualityV1,
   )
 

src/google/adk/evaluation/metric_info_providers.py

@@ -0,0 +1,185 @@
+# Copyright 2025 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 .eval_metrics import Interval
+from .eval_metrics import MetricInfo
+from .eval_metrics import MetricInfoProvider
+from .eval_metrics import MetricValueInfo
+from .eval_metrics import PrebuiltMetrics
+
+
+class TrajectoryEvaluatorMetricInfoProvider(MetricInfoProvider):
+  """Metric info provider for TrajectoryEvaluator."""
+
+  def get_metric_info(self) -> MetricInfo:
+    return MetricInfo(
+        metric_name=PrebuiltMetrics.TOOL_TRAJECTORY_AVG_SCORE.value,
+        description=(
+            "This metric compares two tool call trajectories (expected vs."
+            " actual) for the same user interaction. It performs an exact match"
+            " on the tool name and arguments for each step in the trajectory."
+            " A score of 1.0 indicates a perfect match, while 0.0 indicates a"
+            " mismatch. Higher values are better."
+        ),
+        metric_value_info=MetricValueInfo(
+            interval=Interval(min_value=0.0, max_value=1.0)
+        ),
+    )
+
+
+class ResponseEvaluatorMetricInfoProvider(MetricInfoProvider):
+  """Metric info provider for ResponseEvaluator."""
+
+  def __init__(self, metric_name: str):
+    self._metric_name = metric_name
+
+  def get_metric_info(self) -> MetricInfo:
+    """Returns MetricInfo for the given metric name."""
+    if PrebuiltMetrics.RESPONSE_EVALUATION_SCORE.value == self._metric_name:
+      return MetricInfo(
+          metric_name=PrebuiltMetrics.RESPONSE_EVALUATION_SCORE.value,
+          description=(
+              "This metric evaluates how coherent agent's response was. Value"
+              " range of this metric is [1,5], with values closer to 5 more"
+              " desirable."
+          ),
+          metric_value_info=MetricValueInfo(
+              interval=Interval(min_value=1.0, max_value=5.0)
+          ),
+      )
+    elif PrebuiltMetrics.RESPONSE_MATCH_SCORE.value == self._metric_name:
+      return MetricInfo(
+          metric_name=PrebuiltMetrics.RESPONSE_MATCH_SCORE.value,
+          description=(
+              "This metric evaluates if the agent's final response matches a"
+              " golden/expected final response using Rouge_1 metric. Value"
+              " range for this metric is [0,1], with values closer to 1 more"
+              " desirable."
+          ),
+          metric_value_info=MetricValueInfo(
+              interval=Interval(min_value=0.0, max_value=1.0)
+          ),
+      )
+    else:
+      raise ValueError(f"`{self._metric_name}` is not supported.")
+
+
+class SafetyEvaluatorV1MetricInfoProvider(MetricInfoProvider):
+  """Metric info provider for SafetyEvaluatorV1."""
+
+  def get_metric_info(self) -> MetricInfo:
+    return MetricInfo(
+        metric_name=PrebuiltMetrics.SAFETY_V1.value,
+        description=(
+            "This metric evaluates the safety (harmlessness) of an Agent's"
+            " Response. Value range of the metric is [0, 1], with values closer"
+            " to 1 to be more desirable (safe)."
+        ),
+        metric_value_info=MetricValueInfo(
+            interval=Interval(min_value=0.0, max_value=1.0)
+        ),
+    )
+
+
+class FinalResponseMatchV2EvaluatorMetricInfoProvider(MetricInfoProvider):
+  """Metric info provider for FinalResponseMatchV2Evaluator."""
+
+  def get_metric_info(self) -> MetricInfo:
+    return MetricInfo(
+        metric_name=PrebuiltMetrics.FINAL_RESPONSE_MATCH_V2.value,
+        description=(
+            "This metric evaluates if the agent's final response matches a"
+            " golden/expected final response using LLM as a judge. Value range"
+            " for this metric is [0,1], with values closer to 1 more desirable."
+        ),
+        metric_value_info=MetricValueInfo(
+            interval=Interval(min_value=0.0, max_value=1.0)
+        ),
+    )
+
+
+class RubricBasedFinalResponseQualityV1EvaluatorMetricInfoProvider(
+    MetricInfoProvider
+):
+  """Metric info provider for RubricBasedFinalResponseQualityV1Evaluator."""
+
+  def get_metric_info(self) -> MetricInfo:
+    return MetricInfo(
+        metric_name=PrebuiltMetrics.RUBRIC_BASED_FINAL_RESPONSE_QUALITY_V1.value,
+        description=(
+            "This metric assess if the agent's final response against a set of"
+            " rubrics using LLM as a judge. Value range for this metric is"
+            " [0,1], with values closer to 1 more desirable."
+        ),
+        metric_value_info=MetricValueInfo(
+            interval=Interval(min_value=0.0, max_value=1.0)
+        ),
+    )
+
+
+class HallucinationsV1EvaluatorMetricInfoProvider(MetricInfoProvider):
+  """Metric info provider for HallucinationsV1Evaluator."""
+
+  def get_metric_info(self) -> MetricInfo:
+    return MetricInfo(
+        metric_name=PrebuiltMetrics.HALLUCINATIONS_V1.value,
+        description=(
+            "This metric assesses whether a model response contains any false,"
+            " contradictory, or unsupported claims using a LLM as judge. Value"
+            " range for this metric is [0,1], with values closer to 1 more"
+            " desirable."
+        ),
+        metric_value_info=MetricValueInfo(
+            interval=Interval(min_value=0.0, max_value=1.0)
+        ),
+    )
+
+
+class RubricBasedToolUseV1EvaluatorMetricInfoProvider(MetricInfoProvider):
+  """Metric info provider for RubricBasedToolUseV1Evaluator."""
+
+  def get_metric_info(self) -> MetricInfo:
+    return MetricInfo(
+        metric_name=PrebuiltMetrics.RUBRIC_BASED_TOOL_USE_QUALITY_V1.value,
+        description=(
+            "This metric assess if the agent's usage of tools against a set of"
+            " rubrics using LLM as a judge. Value range for this metric is"
+            " [0,1], with values closer to 1 more desirable."
+        ),
+        metric_value_info=MetricValueInfo(
+            interval=Interval(min_value=0.0, max_value=1.0)
+        ),
+    )
+
+
+class PerTurnUserSimulatorQualityV1MetricInfoProvider(MetricInfoProvider):
+  """Metric info provider for PerTurnUserSimulatorQualityV1."""
+
+  def get_metric_info(self) -> MetricInfo:
+    return MetricInfo(
+        metric_name=PrebuiltMetrics.PER_TURN_USER_SIMULATOR_QUALITY_V1,
+        description=(
+            "This metric evaluates if the user messages generated by a "
+            "user simulator follow the given conversation scenario. It "
+            "validates each message separately. The resulting metric "
+            "computes the percentage of user messages that we mark as "
+            "valid. The value range for this metric is [0,1], with values "
+            "closer to 1 more desirable. "
+        ),
+        metric_value_info=MetricValueInfo(
+            interval=Interval(min_value=0.0, max_value=1.0)
+        ),
+    )

src/google/adk/evaluation/response_evaluator.py

@@ -21,9 +21,6 @@ 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
-from .eval_metrics import MetricInfo
-from .eval_metrics import MetricValueInfo
 from .eval_metrics import PrebuiltMetrics
 from .evaluator import EvaluationResult
 from .evaluator import Evaluator
@@ -76,26 +73,6 @@ class ResponseEvaluator(Evaluator):
 
     self._threshold = threshold
 
-  @staticmethod
-  def get_metric_info(metric_name: str) -> MetricInfo:
-    """Returns MetricInfo for the given metric name."""
-    if PrebuiltMetrics.RESPONSE_EVALUATION_SCORE.value == metric_name:
-      return MetricInfo(
-          metric_name=PrebuiltMetrics.RESPONSE_EVALUATION_SCORE.value,
-          description=(
-              "This metric evaluates how coherent agent's response was. Value"
-              " range of this metric is [1,5], with values closer to 5 more"
-              " desirable."
-          ),
-          metric_value_info=MetricValueInfo(
-              interval=Interval(min_value=1.0, max_value=5.0)
-          ),
-      )
-    elif PrebuiltMetrics.RESPONSE_MATCH_SCORE.value == metric_name:
-      return RougeEvaluator.get_metric_info()
-    else:
-      raise ValueError(f"`{metric_name}` is not supported.")
-
   @override
   def evaluate_invocations(
       self,

src/google/adk/evaluation/rubric_based_final_response_quality_v1.py

@@ -24,10 +24,6 @@ from ..utils.feature_decorator import experimental
 from .eval_case import Invocation
 from .eval_case import InvocationEvents
 from .eval_metrics import EvalMetric
-from .eval_metrics import Interval
-from .eval_metrics import MetricInfo
-from .eval_metrics import MetricValueInfo
-from .eval_metrics import PrebuiltMetrics
 from .eval_metrics import RubricsBasedCriterion
 from .llm_as_judge_utils import get_text_from_content
 from .llm_as_judge_utils import get_tool_calls_and_responses_as_json_str
@@ -266,20 +262,6 @@ class RubricBasedFinalResponseQualityV1Evaluator(RubricBasedEvaluator):
         _RUBRIC_BASED_FINAL_RESPONSE_QUALITY_V1_PROMPT
     )
 
-  @staticmethod
-  def get_metric_info() -> MetricInfo:
-    return MetricInfo(
-        metric_name=PrebuiltMetrics.RUBRIC_BASED_FINAL_RESPONSE_QUALITY_V1.value,
-        description=(
-            "This metric assess if the agent's final response against a set of"
-            " rubrics using LLM as a judge. Value range for this metric is"
-            " [0,1], with values closer to 1 more desirable."
-        ),
-        metric_value_info=MetricValueInfo(
-            interval=Interval(min_value=0.0, max_value=1.0)
-        ),
-    )
-
   @override
   def format_auto_rater_prompt(
       self, actual_invocation: Invocation, _: Optional[Invocation]

src/google/adk/evaluation/rubric_based_tool_use_quality_v1.py

@@ -23,10 +23,6 @@ from typing_extensions import override
 from ..utils.feature_decorator import experimental
 from .eval_case import Invocation
 from .eval_metrics import EvalMetric
-from .eval_metrics import Interval
-from .eval_metrics import MetricInfo
-from .eval_metrics import MetricValueInfo
-from .eval_metrics import PrebuiltMetrics
 from .eval_metrics import RubricsBasedCriterion
 from .llm_as_judge_utils import get_text_from_content
 from .llm_as_judge_utils import get_tool_calls_and_responses_as_json_str
@@ -166,20 +162,6 @@ class RubricBasedToolUseV1Evaluator(RubricBasedEvaluator):
     )
     self._auto_rater_prompt_template = _RUBRIC_BASED_TOOL_USE_QUALITY_V1_PROMPT
 
-  @staticmethod
-  def get_metric_info() -> MetricInfo:
-    return MetricInfo(
-        metric_name=PrebuiltMetrics.RUBRIC_BASED_TOOL_USE_QUALITY_V1.value,
-        description=(
-            "This metric assess if the agent's usage of tools against a set of"
-            " rubrics using LLM as a judge. Value range for this metric is"
-            " [0,1], with values closer to 1 more desirable."
-        ),
-        metric_value_info=MetricValueInfo(
-            interval=Interval(min_value=0.0, max_value=1.0)
-        ),
-    )
-
   @override
   def format_auto_rater_prompt(
       self, actual_invocation: Invocation, _: Optional[Invocation]

src/google/adk/evaluation/safety_evaluator.py

@@ -21,10 +21,6 @@ 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
-from .eval_metrics import MetricInfo
-from .eval_metrics import MetricValueInfo
-from .eval_metrics import PrebuiltMetrics
 from .evaluator import EvaluationResult
 from .evaluator import Evaluator
 from .vertex_ai_eval_facade import _VertexAiEvalFacade
@@ -48,20 +44,6 @@ class SafetyEvaluatorV1(Evaluator):
   def __init__(self, eval_metric: EvalMetric):
     self._eval_metric = eval_metric
 
-  @staticmethod
-  def get_metric_info() -> MetricInfo:
-    return MetricInfo(
-        metric_name=PrebuiltMetrics.SAFETY_V1.value,
-        description=(
-            "This metric evaluates the safety (harmlessness) of an Agent's"
-            " Response. Value range of the metric is [0, 1], with values closer"
-            " to 1 to be more desirable (safe)."
-        ),
-        metric_value_info=MetricValueInfo(
-            interval=Interval(min_value=0.0, max_value=1.0)
-        ),
-    )
-
   @override
   def evaluate_invocations(
       self,

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

@@ -35,11 +35,7 @@ from ..eval_case import Invocation
 from ..eval_metrics import BaseCriterion
 from ..eval_metrics import EvalMetric
 from ..eval_metrics import EvalStatus
-from ..eval_metrics import Interval
 from ..eval_metrics import LlmBackedUserSimulatorCriterion
-from ..eval_metrics import MetricInfo
-from ..eval_metrics import MetricValueInfo
-from ..eval_metrics import PrebuiltMetrics
 from ..evaluator import EvaluationResult
 from ..evaluator import Evaluator
 from ..evaluator import PerInvocationResult
@@ -269,23 +265,6 @@ class PerTurnUserSimulatorQualityV1(Evaluator):
     except ValidationError as e:
       raise expected_criterion_type_error from e
 
-  @staticmethod
-  def get_metric_info() -> MetricInfo:
-    return MetricInfo(
-        metric_name=PrebuiltMetrics.PER_TURN_USER_SIMULATOR_QUALITY_V1,
-        description=(
-            "This metric evaluates if the user messages generated by a "
-            "user simulator follow the given conversation scenario. It "
-            "validates each message separately. The resulting metric "
-            "computes the percentage of user messages that we mark as "
-            "valid. The value range for this metric is [0,1], with values "
-            "closer to 1 more desirable. "
-        ),
-        metric_value_info=MetricValueInfo(
-            interval=Interval(min_value=0.0, max_value=1.0)
-        ),
-    )
-
   @override
   async def evaluate_invocations(
       self,

src/google/adk/evaluation/trajectory_evaluator.py

@@ -26,10 +26,6 @@ from .eval_case import ConversationScenario
 from .eval_case import get_all_tool_calls
 from .eval_case import Invocation
 from .eval_metrics import EvalMetric
-from .eval_metrics import Interval
-from .eval_metrics import MetricInfo
-from .eval_metrics import MetricValueInfo
-from .eval_metrics import PrebuiltMetrics
 from .eval_metrics import ToolTrajectoryCriterion
 from .evaluator import EvalStatus
 from .evaluator import EvaluationResult
@@ -99,22 +95,6 @@ class TrajectoryEvaluator(Evaluator):
       self._threshold = threshold
       self._match_type = ToolTrajectoryCriterion.MatchType.EXACT
 
-  @staticmethod
-  def get_metric_info() -> MetricInfo:
-    return MetricInfo(
-        metric_name=PrebuiltMetrics.TOOL_TRAJECTORY_AVG_SCORE.value,
-        description=(
-            "This metric compares two tool call trajectories (expected vs."
-            " actual) for the same user interaction. It performs an exact match"
-            " on the tool name and arguments for each step in the trajectory."
-            " A score of 1.0 indicates a perfect match, while 0.0 indicates a"
-            " mismatch. Higher values are better."
-        ),
-        metric_value_info=MetricValueInfo(
-            interval=Interval(min_value=0.0, max_value=1.0)
-        ),
-    )
-
   @override
   def evaluate_invocations(
       self,

tests/unittests/evaluation/test_final_response_match_v1.py

@@ -139,11 +139,3 @@ def test_rouge_evaluator_multiple_invocations(
       expected_score, rel=1e-3
   )
   assert evaluation_result.overall_eval_status == expected_status
-
-
-def test_get_metric_info():
-  """Test get_metric_info function for response match metric."""
-  metric_info = RougeEvaluator.get_metric_info()
-  assert metric_info.metric_name == PrebuiltMetrics.RESPONSE_MATCH_SCORE.value
-  assert metric_info.metric_value_info.interval.min_value == 0.0
-  assert metric_info.metric_value_info.interval.max_value == 1.0

tests/unittests/evaluation/test_final_response_match_v2.py

@@ -486,13 +486,3 @@ def test_aggregate_invocation_results():
   # Only 4 / 8 invocations are evaluated, and 2 / 4 are valid.
   assert aggregated_result.overall_score == 0.5
   assert aggregated_result.overall_eval_status == EvalStatus.PASSED
-
-
-def test_get_metric_info():
-  """Test get_metric_info function for Final Response Match V2 metric."""
-  metric_info = FinalResponseMatchV2Evaluator.get_metric_info()
-  assert (
-      metric_info.metric_name == PrebuiltMetrics.FINAL_RESPONSE_MATCH_V2.value
-  )
-  assert metric_info.metric_value_info.interval.min_value == 0.0
-  assert metric_info.metric_value_info.interval.max_value == 1.0

tests/unittests/evaluation/test_metric_evaluator_registry.py

@@ -19,11 +19,52 @@ from google.adk.evaluation.eval_metrics import EvalMetric
 from google.adk.evaluation.eval_metrics import Interval
 from google.adk.evaluation.eval_metrics import MetricInfo
 from google.adk.evaluation.eval_metrics import MetricValueInfo
+from google.adk.evaluation.eval_metrics import PrebuiltMetrics
 from google.adk.evaluation.evaluator import Evaluator
+from google.adk.evaluation.metric_evaluator_registry import FinalResponseMatchV2EvaluatorMetricInfoProvider
+from google.adk.evaluation.metric_evaluator_registry import HallucinationsV1EvaluatorMetricInfoProvider
 from google.adk.evaluation.metric_evaluator_registry import MetricEvaluatorRegistry
+from google.adk.evaluation.metric_evaluator_registry import PerTurnUserSimulatorQualityV1MetricInfoProvider
+from google.adk.evaluation.metric_evaluator_registry import ResponseEvaluatorMetricInfoProvider
+from google.adk.evaluation.metric_evaluator_registry import RubricBasedFinalResponseQualityV1EvaluatorMetricInfoProvider
+from google.adk.evaluation.metric_evaluator_registry import RubricBasedToolUseV1EvaluatorMetricInfoProvider
+from google.adk.evaluation.metric_evaluator_registry import SafetyEvaluatorV1MetricInfoProvider
+from google.adk.evaluation.metric_evaluator_registry import TrajectoryEvaluatorMetricInfoProvider
 import pytest
 
 _DUMMY_METRIC_NAME = "dummy_metric_name"
+_DUMMY_METRIC_INFO = MetricInfo(
+    metric_name=_DUMMY_METRIC_NAME,
+    description="Dummy metric description",
+    metric_value_info=MetricValueInfo(
+        interval=Interval(min_value=0.0, max_value=1.0)
+    ),
+)
+_ANOTHER_DUMMY_METRIC_INFO = MetricInfo(
+    metric_name=_DUMMY_METRIC_NAME,
+    description="Another dummy metric description",
+    metric_value_info=MetricValueInfo(
+        interval=Interval(min_value=0.0, max_value=1.0)
+    ),
+)
+
+
+class DummyEvaluator(Evaluator):
+
+  def __init__(self, eval_metric: EvalMetric):
+    self._eval_metric = eval_metric
+
+  def evaluate_invocations(self, actual_invocations, expected_invocations):
+    return "dummy_result"
+
+
+class AnotherDummyEvaluator(Evaluator):
+
+  def __init__(self, eval_metric: EvalMetric):
+    self._eval_metric = eval_metric
+
+  def evaluate_invocations(self, actual_invocations, expected_invocations):
+    return "another_dummy_result"
 
 
 class TestMetricEvaluatorRegistry:
@@ -33,88 +74,137 @@ class TestMetricEvaluatorRegistry:
   def registry(self):
     return MetricEvaluatorRegistry()
 
-  class DummyEvaluator(Evaluator):
-
-    def __init__(self, eval_metric: EvalMetric):
-      self._eval_metric = eval_metric
-
-    def evaluate_invocations(self, actual_invocations, expected_invocations):
-      return "dummy_result"
-
-    @staticmethod
-    def get_metric_info() -> MetricInfo:
-      return MetricInfo(
-          metric_name=_DUMMY_METRIC_NAME,
-          description="Dummy metric description",
-          metric_value_info=MetricValueInfo(
-              interval=Interval(min_value=0.0, max_value=1.0)
-          ),
-      )
-
-  class AnotherDummyEvaluator(Evaluator):
-
-    def __init__(self, eval_metric: EvalMetric):
-      self._eval_metric = eval_metric
-
-    def evaluate_invocations(self, actual_invocations, expected_invocations):
-      return "another_dummy_result"
-
-    @staticmethod
-    def get_metric_info() -> MetricInfo:
-      return MetricInfo(
-          metric_name=_DUMMY_METRIC_NAME,
-          description="Another dummy metric description",
-          metric_value_info=MetricValueInfo(
-              interval=Interval(min_value=0.0, max_value=1.0)
-          ),
-      )
-
   def test_register_evaluator(self, registry):
-    metric_info = TestMetricEvaluatorRegistry.DummyEvaluator.get_metric_info()
     registry.register_evaluator(
-        metric_info,
-        TestMetricEvaluatorRegistry.DummyEvaluator,
+        _DUMMY_METRIC_INFO,
+        DummyEvaluator,
     )
     assert _DUMMY_METRIC_NAME in registry._registry
     assert registry._registry[_DUMMY_METRIC_NAME] == (
-        TestMetricEvaluatorRegistry.DummyEvaluator,
-        metric_info,
+        DummyEvaluator,
+        _DUMMY_METRIC_INFO,
     )
 
   def test_register_evaluator_updates_existing(self, registry):
-    metric_info = TestMetricEvaluatorRegistry.DummyEvaluator.get_metric_info()
     registry.register_evaluator(
-        metric_info,
-        TestMetricEvaluatorRegistry.DummyEvaluator,
+        _DUMMY_METRIC_INFO,
+        DummyEvaluator,
     )
 
     assert registry._registry[_DUMMY_METRIC_NAME] == (
-        TestMetricEvaluatorRegistry.DummyEvaluator,
-        metric_info,
+        DummyEvaluator,
+        _DUMMY_METRIC_INFO,
     )
 
-    metric_info = (
-        TestMetricEvaluatorRegistry.AnotherDummyEvaluator.get_metric_info()
-    )
     registry.register_evaluator(
-        metric_info, TestMetricEvaluatorRegistry.AnotherDummyEvaluator
+        _ANOTHER_DUMMY_METRIC_INFO, AnotherDummyEvaluator
     )
     assert registry._registry[_DUMMY_METRIC_NAME] == (
-        TestMetricEvaluatorRegistry.AnotherDummyEvaluator,
-        metric_info,
+        AnotherDummyEvaluator,
+        _ANOTHER_DUMMY_METRIC_INFO,
     )
 
   def test_get_evaluator(self, registry):
-    metric_info = TestMetricEvaluatorRegistry.DummyEvaluator.get_metric_info()
     registry.register_evaluator(
-        metric_info,
-        TestMetricEvaluatorRegistry.DummyEvaluator,
+        _DUMMY_METRIC_INFO,
+        DummyEvaluator,
     )
     eval_metric = EvalMetric(metric_name=_DUMMY_METRIC_NAME, threshold=0.5)
     evaluator = registry.get_evaluator(eval_metric)
-    assert isinstance(evaluator, TestMetricEvaluatorRegistry.DummyEvaluator)
+    assert isinstance(evaluator, DummyEvaluator)
 
   def test_get_evaluator_not_found(self, registry):
     eval_metric = EvalMetric(metric_name="non_existent_metric", threshold=0.5)
     with pytest.raises(NotFoundError):
       registry.get_evaluator(eval_metric)
+
+
+class TestMetricInfoProviders:
+  """Test cases for MetricInfoProviders."""
+
+  def test_trajectory_evaluator_metric_info_provider(self):
+    metric_info = TrajectoryEvaluatorMetricInfoProvider().get_metric_info()
+    assert (
+        metric_info.metric_name
+        == PrebuiltMetrics.TOOL_TRAJECTORY_AVG_SCORE.value
+    )
+    assert metric_info.metric_value_info.interval.min_value == 0.0
+    assert metric_info.metric_value_info.interval.max_value == 1.0
+
+  def test_response_evaluator_metric_info_provider_eval_score(self):
+    metric_info = ResponseEvaluatorMetricInfoProvider(
+        PrebuiltMetrics.RESPONSE_EVALUATION_SCORE.value
+    ).get_metric_info()
+    assert (
+        metric_info.metric_name
+        == PrebuiltMetrics.RESPONSE_EVALUATION_SCORE.value
+    )
+    assert metric_info.metric_value_info.interval.min_value == 1.0
+    assert metric_info.metric_value_info.interval.max_value == 5.0
+
+  def test_response_evaluator_metric_info_provider_match_score(self):
+    metric_info = ResponseEvaluatorMetricInfoProvider(
+        PrebuiltMetrics.RESPONSE_MATCH_SCORE.value
+    ).get_metric_info()
+    assert metric_info.metric_name == PrebuiltMetrics.RESPONSE_MATCH_SCORE.value
+    assert metric_info.metric_value_info.interval.min_value == 0.0
+    assert metric_info.metric_value_info.interval.max_value == 1.0
+
+  def test_safety_evaluator_v1_metric_info_provider(self):
+    metric_info = SafetyEvaluatorV1MetricInfoProvider().get_metric_info()
+    assert metric_info.metric_name == PrebuiltMetrics.SAFETY_V1.value
+    assert metric_info.metric_value_info.interval.min_value == 0.0
+    assert metric_info.metric_value_info.interval.max_value == 1.0
+
+  def test_final_response_match_v2_evaluator_metric_info_provider(self):
+    metric_info = (
+        FinalResponseMatchV2EvaluatorMetricInfoProvider().get_metric_info()
+    )
+    assert (
+        metric_info.metric_name == PrebuiltMetrics.FINAL_RESPONSE_MATCH_V2.value
+    )
+    assert metric_info.metric_value_info.interval.min_value == 0.0
+    assert metric_info.metric_value_info.interval.max_value == 1.0
+
+  def test_rubric_based_final_response_quality_v1_evaluator_metric_info_provider(
+      self,
+  ):
+    metric_info = (
+        RubricBasedFinalResponseQualityV1EvaluatorMetricInfoProvider().get_metric_info()
+    )
+    assert (
+        metric_info.metric_name
+        == PrebuiltMetrics.RUBRIC_BASED_FINAL_RESPONSE_QUALITY_V1.value
+    )
+    assert metric_info.metric_value_info.interval.min_value == 0.0
+    assert metric_info.metric_value_info.interval.max_value == 1.0
+
+  def test_hallucinations_v1_evaluator_metric_info_provider(self):
+    metric_info = (
+        HallucinationsV1EvaluatorMetricInfoProvider().get_metric_info()
+    )
+    assert metric_info.metric_name == PrebuiltMetrics.HALLUCINATIONS_V1.value
+    assert metric_info.metric_value_info.interval.min_value == 0.0
+    assert metric_info.metric_value_info.interval.max_value == 1.0
+
+  def test_rubric_based_tool_use_v1_evaluator_metric_info_provider(self):
+    metric_info = (
+        RubricBasedToolUseV1EvaluatorMetricInfoProvider().get_metric_info()
+    )
+    assert (
+        metric_info.metric_name
+        == PrebuiltMetrics.RUBRIC_BASED_TOOL_USE_QUALITY_V1.value
+    )
+    assert metric_info.metric_value_info.interval.min_value == 0.0
+    assert metric_info.metric_value_info.interval.max_value == 1.0
+
+  def test_per_turn_user_simulator_quality_v1_metric_info_provider(self):
+    metric_info = (
+        PerTurnUserSimulatorQualityV1MetricInfoProvider().get_metric_info()
+    )
+    assert (
+        metric_info.metric_name
+        == PrebuiltMetrics.PER_TURN_USER_SIMULATOR_QUALITY_V1.value
+    )
+    assert metric_info.metric_value_info.interval.min_value == 0.0
+    assert metric_info.metric_value_info.interval.max_value == 1.0

tests/unittests/evaluation/test_response_evaluator.py

@@ -118,29 +118,3 @@ class TestResponseEvaluator:
     assert [m.name for m in mock_kwargs["metrics"]] == [
         vertexai_types.PrebuiltMetric.COHERENCE.name
     ]
-
-  def test_get_metric_info_response_evaluation_score(self):
-    """Test get_metric_info function for response evaluation metric."""
-    metric_info = ResponseEvaluator.get_metric_info(
-        PrebuiltMetrics.RESPONSE_EVALUATION_SCORE.value
-    )
-    assert (
-        metric_info.metric_name
-        == PrebuiltMetrics.RESPONSE_EVALUATION_SCORE.value
-    )
-    assert metric_info.metric_value_info.interval.min_value == 1.0
-    assert metric_info.metric_value_info.interval.max_value == 5.0
-
-  def test_get_metric_info_response_match_score(self):
-    """Test get_metric_info function for response match metric."""
-    metric_info = ResponseEvaluator.get_metric_info(
-        PrebuiltMetrics.RESPONSE_MATCH_SCORE.value
-    )
-    assert metric_info.metric_name == PrebuiltMetrics.RESPONSE_MATCH_SCORE.value
-    assert metric_info.metric_value_info.interval.min_value == 0.0
-    assert metric_info.metric_value_info.interval.max_value == 1.0
-
-  def test_get_metric_info_invalid(self):
-    """Test get_metric_info function for invalid metric."""
-    with pytest.raises(ValueError):
-      ResponseEvaluator.get_metric_info("invalid_metric")

tests/unittests/evaluation/test_rubric_based_tool_use_quality_v1.py

@@ -136,15 +136,3 @@ def test_format_auto_rater_prompt_with_intermediate_data(
   assert '"name": "test_func"' in prompt
   assert '"tool_response":' in prompt
   assert '"result": "ok"' in prompt
-
-
-def test_get_metric_info(evaluator: RubricBasedToolUseV1Evaluator):
-  """Tests the get_metric_info method."""
-  metric_info = evaluator.get_metric_info()
-  assert (
-      metric_info.metric_name
-      == PrebuiltMetrics.RUBRIC_BASED_TOOL_USE_QUALITY_V1.value
-  )
-  assert "agent's usage of tools" in metric_info.description
-  assert metric_info.metric_value_info.interval.min_value == 0.0
-  assert metric_info.metric_value_info.interval.max_value == 1.0

tests/unittests/evaluation/test_safety_evaluator.py

@@ -76,10 +76,3 @@ class TestSafetyEvaluatorV1:
     assert [m.name for m in mock_kwargs["metrics"]] == [
         vertexai_types.PrebuiltMetric.SAFETY.name
     ]
-
-  def test_get_metric_info(self):
-    """Test get_metric_info function for Safety metric."""
-    metric_info = SafetyEvaluatorV1.get_metric_info()
-    assert metric_info.metric_name == PrebuiltMetrics.SAFETY_V1.value
-    assert metric_info.metric_value_info.interval.min_value == 0.0
-    assert metric_info.metric_value_info.interval.max_value == 1.0

tests/unittests/evaluation/test_trajectory_evaluator.py

@@ -30,16 +30,6 @@ _USER_CONTENT = genai_types.Content(
 )
 
 
-def test_get_metric_info():
-  """Test get_metric_info function for tool trajectory avg metric."""
-  metric_info = TrajectoryEvaluator.get_metric_info()
-  assert (
-      metric_info.metric_name == PrebuiltMetrics.TOOL_TRAJECTORY_AVG_SCORE.value
-  )
-  assert metric_info.metric_value_info.interval.min_value == 0.0
-  assert metric_info.metric_value_info.interval.max_value == 1.0
-
-
 @pytest.fixture
 def evaluator() -> TrajectoryEvaluator:
   """Returns a TrajectoryEvaluator."""