Agent Development Kit(ADK)

An easy-to-use and powerful framework to build AI agents.

src/google/adk/evaluation/trajectory_evaluator.py

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+# 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 typing import Any
+
+import pandas as pd
+from tabulate import tabulate
+
+from .evaluation_constants import EvalConstants
+
+
+class TrajectoryEvaluator:
+  """Evaluates tool use trajectories for accuracy."""
+
+  @staticmethod
+  def evaluate(
+      eval_dataset: list[list[dict[str, Any]]],
+      *,
+      print_detailed_results: bool = False,
+  ):
+    r"""Returns the mean tool use accuracy of the eval dataset.
+
+    Tool use accuracy is calculated by comparing the expected and actuall tool
+    use trajectories. An exact match scores a 1, 0 otherwise. The final number
+    is an
+    average of these individual scores.
+
+    Value range: [0, 1], where 0 is means none of the too use entries aligned,
+    and 1 would mean all of them aligned. Higher value is good.
+
+    Args:
+      eval_dataset: The dataset that will be evaluated.
+      print_detailed_results: Prints detailed results on the console. This is
+        usually helpful during debugging.
+
+    A note on eval_dataset:
+      The dataset should be a list session, where each sesssion is represented
+      as a list of interaction that need evaluation. Each evaluation is
+      represented as a dictionary that is expected to have values for the
+      following keys:
+        1) query
+        2) response
+        3) acutal_tool_use
+        4) expected_tool_use
+
+      Here is a sample eval_dataset value with one entry:
+
+      [
+        [
+          {
+            "query": "Roll a 16 sided dice for me",
+            "response": "I rolled a 16 sided die and got 13.\n",
+            "expected_tool_use": [
+              {
+                "tool_name": "roll_die",
+                "tool_input": {
+                  "sides": 16
+                }
+              }
+            ],
+            "acutal_tool_use": [
+              {
+                "tool_name": "roll_die",
+                "tool_input": {
+                  "sides": 16
+                }
+              }
+            ]
+          }
+        ]
+      ]
+    """
+    if not eval_dataset:
+      raise ValueError("The evaluation dataset is empty.")
+
+    results_df = pd.DataFrame(
+        columns=[
+            "query",
+            "response",
+            "actual_tool_use",
+            "expected_tool_use",
+            "tool_use_accuracy",
+        ]
+    )
+    failures = []
+
+    for conversation in eval_dataset:
+      for index, row in enumerate(conversation):
+        new_row, failure = TrajectoryEvaluator._evaluate_row(row)
+        results_df = pd.concat(
+            [results_df, pd.DataFrame([new_row])], ignore_index=True
+        )
+        if failure:
+          failure["turn"] = index + 1
+          failures.append(failure)
+
+    TrajectoryEvaluator._report_failures(failures)
+
+    if print_detailed_results:
+      TrajectoryEvaluator._print_results(results_df)
+
+    return results_df["tool_use_accuracy"].mean()
+
+  @staticmethod
+  def _evaluate_row(row):
+    # We don't evaluate the mock tool outputs.
+    expected = TrajectoryEvaluator._remove_tool_outputs(
+        row["expected_tool_use"]
+    )
+    actual = row["actual_tool_use"]
+    tool_use_accuracy = (
+        1.0 if TrajectoryEvaluator.are_tools_equal(actual, expected) else 0.0
+    )
+
+    new_row = {
+        "query": row["query"],
+        "response": row["response"],
+        "actual_tool_use": actual,
+        "expected_tool_use": expected,
+        "tool_use_accuracy": tool_use_accuracy,
+    }
+    failure = (
+        None
+        if tool_use_accuracy == 1.0
+        else {"query": row["query"], "actual": actual, "expected": expected}
+    )
+    return new_row, failure
+
+  @staticmethod
+  def are_tools_equal(list_a_original, list_b_original):
+    # Remove other entries that we don't want to evaluate
+    list_a = [
+        {"tool_name": tool["tool_name"], "tool_input": tool["tool_input"]}
+        for tool in list_a_original
+    ]
+
+    list_b = [
+        {"tool_name": tool["tool_name"], "tool_input": tool["tool_input"]}
+        for tool in list_b_original
+    ]
+
+    return list_a == list_b
+
+  @staticmethod
+  def _remove_tool_outputs(tool_use_list):
+    """Removes 'mock_tool_output' from each dictionary in the list."""
+    result = []
+    for tool_use in tool_use_list:
+      new_tool_use = (
+          tool_use.copy()
+      )  # Create a copy to avoid modifying the original
+      new_tool_use.pop(
+          EvalConstants.MOCK_TOOL_OUTPUT, None
+      )  # Remove 'tool_output' if it exists
+      result.append(new_tool_use)
+    return result
+
+  @staticmethod
+  def _report_failures(failures):
+    if failures:
+      print("Failures:")
+      for failure in failures:
+        print(f"""{{
+  "turn": {failure["turn"]},
+  "query": '{failure["query"]}',
+  "actual": {failure["actual"]},
+  "expected_tool_use": {failure["expected"]},
+}}
+""")
+
+  @staticmethod
+  def _print_results(results_df):
+    print(tabulate(results_df, headers="keys", tablefmt="grid"))