Update Eval Run and TrajectoryEvaluator to use the new schema.

PiperOrigin-RevId: 758927160

src/google/adk/evaluation/trajectory_evaluator.py

@@ -12,18 +12,98 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any
+from typing import Any, cast
 
+from deprecated import deprecated
+from google.genai import types as genai_types
 import pandas as pd
 from tabulate import tabulate
+from typing_extensions import override
 
+from .eval_case import Invocation
 from .evaluation_constants import EvalConstants
+from .evaluator import EvalStatus
+from .evaluator import EvaluationResult
+from .evaluator import Evaluator
+from .evaluator import PerInvocationResult
 
 
-class TrajectoryEvaluator:
+class TrajectoryEvaluator(Evaluator):
   """Evaluates tool use trajectories for accuracy."""
 
+  def __init__(self, threshold: float):
+    self._threshold = threshold
+
+  @override
+  def evaluate_invocations(
+      self,
+      actual_invocations: list[Invocation],
+      expected_invocations: list[Invocation],
+  ) -> EvaluationResult:
+    """Returns EvaluationResult after performing evaluations using actual and expected invocations."""
+    total_tool_use_accuracy = 0.0
+    num_invocations = 0
+    per_invocation_results = []
+
+    for actual, expected in zip(actual_invocations, expected_invocations):
+      actual_tool_uses = (
+          actual.intermediate_data.tool_uses if actual.intermediate_data else []
+      )
+      expected_tool_uses = (
+          expected.intermediate_data.tool_uses
+          if expected.intermediate_data
+          else []
+      )
+      tool_use_accuracy = (
+          1.0
+          if self._are_tool_calls_equal(actual_tool_uses, expected_tool_uses)
+          else 0.0
+      )
+      per_invocation_results.append(
+          PerInvocationResult(
+              actual_invocation=actual,
+              expected_invocation=expected,
+              score=tool_use_accuracy,
+              eval_status=self._get_eval_status(tool_use_accuracy),
+          )
+      )
+      total_tool_use_accuracy += tool_use_accuracy
+      num_invocations += 1
+
+    if per_invocation_results:
+      overall_score = total_tool_use_accuracy / num_invocations
+      return EvaluationResult(
+          overall_score=overall_score,
+          overall_eval_status=self._get_eval_status(overall_score),
+          per_invocation_results=per_invocation_results,
+      )
+
+    return EvaluationResult()
+
+  def _are_tool_calls_equal(
+      self,
+      actual_tool_calls: list[genai_types.FunctionCall],
+      expected_tool_calls: list[genai_types.FunctionCall],
+  ) -> bool:
+    if len(actual_tool_calls) != len(expected_tool_calls):
+      return False
+
+    for actual, expected in zip(actual_tool_calls, expected_tool_calls):
+      if actual.name != expected.name or actual.args != expected.args:
+        return False
+
+    return True
+
+  def _get_eval_status(self, score: float):
+    return EvalStatus.PASSED if score >= self._threshold else EvalStatus.FAILED
+
   @staticmethod
+  @deprecated(
+      reason=(
+          "This method has been deprecated and will be removed soon. Please use"
+          " evaluate_invocations instead."
+      )
+  )
   def evaluate(
       eval_dataset: list[list[dict[str, Any]]],
       *,
@@ -137,6 +217,7 @@ class TrajectoryEvaluator:
     return new_row, failure
 
   @staticmethod
+  @deprecated()
   def are_tools_equal(list_a_original, list_b_original):
     # Remove other entries that we don't want to evaluate
     list_a = [