mirror of
https://github.com/encounter/adk-python.git
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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
227 lines
7.9 KiB
Python
227 lines
7.9 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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"""Tests for the Response Evaluator."""
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import random
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from unittest.mock import patch
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from google.adk.evaluation.eval_case import Invocation
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from google.adk.evaluation.evaluator import EvalStatus
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from google.adk.evaluation.vertex_ai_eval_facade import _VertexAiEvalFacade
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from google.genai import types as genai_types
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import pytest
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from vertexai import types as vertexai_types
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@patch(
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"google.adk.evaluation.vertex_ai_eval_facade._VertexAiEvalFacade._perform_eval"
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)
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class TestVertexAiEvalFacade:
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"""A class to help organize "patch" that are applicable to all tests."""
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def test_evaluate_invocations_metric_passed(self, mock_perform_eval):
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"""Test evaluate_invocations function for a metric."""
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actual_invocations = [
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Invocation(
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user_content=genai_types.Content(
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parts=[genai_types.Part(text="This is a test query.")]
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),
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final_response=genai_types.Content(
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parts=[
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genai_types.Part(text="This is a test candidate response.")
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]
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),
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)
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]
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expected_invocations = [
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Invocation(
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user_content=genai_types.Content(
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parts=[genai_types.Part(text="This is a test query.")]
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),
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final_response=genai_types.Content(
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parts=[genai_types.Part(text="This is a test reference.")]
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),
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)
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]
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evaluator = _VertexAiEvalFacade(
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threshold=0.8, metric_name=vertexai_types.PrebuiltMetric.COHERENCE
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)
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# Mock the return value of _perform_eval
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mock_perform_eval.return_value = vertexai_types.EvaluationResult(
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summary_metrics=[vertexai_types.AggregatedMetricResult(mean_score=0.9)],
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eval_case_results=[],
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)
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evaluation_result = evaluator.evaluate_invocations(
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actual_invocations, expected_invocations
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)
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assert evaluation_result.overall_score == 0.9
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assert evaluation_result.overall_eval_status == EvalStatus.PASSED
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mock_perform_eval.assert_called_once()
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_, mock_kwargs = mock_perform_eval.call_args
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# Compare the names of the metrics.
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assert [m.name for m in mock_kwargs["metrics"]] == [
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vertexai_types.PrebuiltMetric.COHERENCE.name
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]
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def test_evaluate_invocations_metric_failed(self, mock_perform_eval):
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"""Test evaluate_invocations function for a metric."""
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actual_invocations = [
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Invocation(
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user_content=genai_types.Content(
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parts=[genai_types.Part(text="This is a test query.")]
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),
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final_response=genai_types.Content(
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parts=[
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genai_types.Part(text="This is a test candidate response.")
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]
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),
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)
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]
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expected_invocations = [
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Invocation(
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user_content=genai_types.Content(
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parts=[genai_types.Part(text="This is a test query.")]
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),
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final_response=genai_types.Content(
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parts=[genai_types.Part(text="This is a test reference.")]
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),
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)
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]
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evaluator = _VertexAiEvalFacade(
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threshold=0.8, metric_name=vertexai_types.PrebuiltMetric.COHERENCE
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)
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# Mock the return value of _perform_eval
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mock_perform_eval.return_value = vertexai_types.EvaluationResult(
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summary_metrics=[vertexai_types.AggregatedMetricResult(mean_score=0.7)],
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eval_case_results=[],
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)
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evaluation_result = evaluator.evaluate_invocations(
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actual_invocations, expected_invocations
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)
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assert evaluation_result.overall_score == 0.7
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assert evaluation_result.overall_eval_status == EvalStatus.FAILED
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mock_perform_eval.assert_called_once()
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_, mock_kwargs = mock_perform_eval.call_args
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# Compare the names of the metrics.
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assert [m.name for m in mock_kwargs["metrics"]] == [
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vertexai_types.PrebuiltMetric.COHERENCE.name
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]
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def test_evaluate_invocations_metric_no_score(self, mock_perform_eval):
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"""Test evaluate_invocations function for a metric."""
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actual_invocations = [
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Invocation(
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user_content=genai_types.Content(
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parts=[genai_types.Part(text="This is a test query.")]
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),
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final_response=genai_types.Content(
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parts=[
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genai_types.Part(text="This is a test candidate response.")
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]
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),
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)
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]
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expected_invocations = [
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Invocation(
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user_content=genai_types.Content(
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parts=[genai_types.Part(text="This is a test query.")]
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),
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final_response=genai_types.Content(
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parts=[genai_types.Part(text="This is a test reference.")]
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),
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)
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]
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evaluator = _VertexAiEvalFacade(
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threshold=0.8, metric_name=vertexai_types.PrebuiltMetric.COHERENCE
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)
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# Mock the return value of _perform_eval
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mock_perform_eval.return_value = vertexai_types.EvaluationResult(
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summary_metrics=[],
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eval_case_results=[],
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)
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evaluation_result = evaluator.evaluate_invocations(
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actual_invocations, expected_invocations
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)
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assert evaluation_result.overall_score is None
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assert evaluation_result.overall_eval_status == EvalStatus.NOT_EVALUATED
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mock_perform_eval.assert_called_once()
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_, mock_kwargs = mock_perform_eval.call_args
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# Compare the names of the metrics.
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assert [m.name for m in mock_kwargs["metrics"]] == [
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vertexai_types.PrebuiltMetric.COHERENCE.name
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]
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def test_evaluate_invocations_metric_multiple_invocations(
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self, mock_perform_eval
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):
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"""Test evaluate_invocations function for a metric with multiple invocations."""
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num_invocations = 6
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actual_invocations = []
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expected_invocations = []
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mock_eval_results = []
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random.seed(61553)
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scores = [random.random() for _ in range(num_invocations)]
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for i in range(num_invocations):
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actual_invocations.append(
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Invocation(
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user_content=genai_types.Content(
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parts=[genai_types.Part(text=f"Query {i+1}")]
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),
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final_response=genai_types.Content(
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parts=[genai_types.Part(text=f"Response {i+1}")]
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),
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)
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)
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expected_invocations.append(
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Invocation(
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user_content=genai_types.Content(
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parts=[genai_types.Part(text=f"Query {i+1}")]
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),
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final_response=genai_types.Content(
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parts=[genai_types.Part(text=f"Reference {i+1}")]
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),
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)
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)
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mock_eval_results.append(
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vertexai_types.EvaluationResult(
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summary_metrics=[
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vertexai_types.AggregatedMetricResult(mean_score=scores[i])
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],
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eval_case_results=[],
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)
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)
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evaluator = _VertexAiEvalFacade(
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threshold=0.8, metric_name=vertexai_types.PrebuiltMetric.COHERENCE
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)
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# Mock the return value of _perform_eval
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mock_perform_eval.side_effect = mock_eval_results
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evaluation_result = evaluator.evaluate_invocations(
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actual_invocations, expected_invocations
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)
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assert evaluation_result.overall_score == pytest.approx(
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sum(scores) / num_invocations
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)
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assert evaluation_result.overall_eval_status == EvalStatus.FAILED
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assert mock_perform_eval.call_count == num_invocations
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