mirror of
https://github.com/encounter/adk-python.git
synced 2026-07-09 18:19:28 -07:00
Ankur SharmaandCopybara-Service
0bd05df471
We add a new metric for evaluating safety of Agent's response to ADK Eval. We delegate the actual implementation to Vertex Gen AI Eval SDK, so using this metric will require GCP project. As a part of this change, we created (refactored) a simple Facade for vertex gen ai eval sdk. PiperOrigin-RevId: 778580406
148 lines
4.6 KiB
Python
148 lines
4.6 KiB
Python
# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import os
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from typing import Optional
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from google.genai import types as genai_types
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import pandas as pd
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from typing_extensions import override
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from vertexai import Client as VertexAiClient
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from vertexai import types as vertexai_types
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from .eval_case import Invocation
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from .evaluator import EvalStatus
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from .evaluator import EvaluationResult
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from .evaluator import Evaluator
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from .evaluator import PerInvocationResult
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_ERROR_MESSAGE_SUFFIX = """
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You should specify both project id and location. This metric uses Vertex Gen AI
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Eval SDK, and it requires google cloud credentials.
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If using an .env file add the values there, or explicitly set in the code using
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the template below:
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os.environ['GOOGLE_CLOUD_LOCATION'] = <LOCATION>
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os.environ['GOOGLE_CLOUD_PROJECT'] = <PROJECT ID>
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"""
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class _VertexAiEvalFacade(Evaluator):
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"""Simple facade for Vertex Gen AI Eval SDK.
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Vertex Gen AI Eval SDK exposes quite a few metrics that are valuable for
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agentic evals. This class helps us to access those metrics.
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Using this class requires a GCP project. Please set GOOGLE_CLOUD_PROJECT and
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GOOGLE_CLOUD_LOCATION in your .env file.
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"""
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def __init__(
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self, threshold: float, metric_name: vertexai_types.PrebuiltMetric
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):
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self._threshold = threshold
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self._metric_name = metric_name
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@override
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def evaluate_invocations(
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self,
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actual_invocations: list[Invocation],
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expected_invocations: list[Invocation],
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) -> EvaluationResult:
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total_score = 0.0
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num_invocations = 0
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per_invocation_results = []
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for actual, expected in zip(actual_invocations, expected_invocations):
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prompt = self._get_text(expected.user_content)
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reference = self._get_text(expected.final_response)
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response = self._get_text(actual.final_response)
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eval_case = {
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"prompt": prompt,
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"reference": reference,
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"response": response,
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}
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eval_case_result = _VertexAiEvalFacade._perform_eval(
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dataset=pd.DataFrame([eval_case]), metrics=[self._metric_name]
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)
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score = self._get_score(eval_case_result)
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per_invocation_results.append(
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PerInvocationResult(
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actual_invocation=actual,
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expected_invocation=expected,
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score=score,
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eval_status=self._get_eval_status(score),
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)
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)
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if score:
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total_score += score
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num_invocations += 1
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if per_invocation_results:
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overall_score = (
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total_score / num_invocations if num_invocations > 0 else None
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)
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return EvaluationResult(
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overall_score=overall_score,
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overall_eval_status=self._get_eval_status(overall_score),
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per_invocation_results=per_invocation_results,
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)
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return EvaluationResult()
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def _get_text(self, content: Optional[genai_types.Content]) -> str:
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if content and content.parts:
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return "\n".join([p.text for p in content.parts if p.text])
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return ""
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def _get_score(self, eval_result) -> Optional[float]:
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if eval_result and eval_result.summary_metrics:
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return eval_result.summary_metrics[0].mean_score
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return None
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def _get_eval_status(self, score: Optional[float]):
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if score:
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return (
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EvalStatus.PASSED if score >= self._threshold else EvalStatus.FAILED
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)
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return EvalStatus.NOT_EVALUATED
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@staticmethod
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def _perform_eval(dataset, metrics):
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"""This method hides away the call to external service.
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Primarily helps with unit testing.
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"""
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project_id = os.environ.get("GOOGLE_CLOUD_PROJECT", None)
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location = os.environ.get("GOOGLE_CLOUD_LOCATION", None)
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if not project_id:
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raise ValueError("Missing project id." + _ERROR_MESSAGE_SUFFIX)
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if not location:
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raise ValueError("Missing location." + _ERROR_MESSAGE_SUFFIX)
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client = VertexAiClient(project=project_id, location=location)
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return client.evals.evaluate(
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dataset=vertexai_types.EvaluationDataset(eval_dataset_df=dataset),
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metrics=metrics,
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)
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