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https://github.com/crewAIInc/crewAI.git
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1b6b2b36d9
* feat: add exchanged messages in LLMCallCompletedEvent * feat: add GoalAlignment metric for Agent evaluation * feat: add SemanticQuality metric for Agent evaluation * feat: add Tool Metrics for Agent evaluation * feat: add Reasoning Metrics for Agent evaluation, still in progress * feat: add AgentEvaluator class This class will evaluate Agent' results and report to user * fix: do not evaluate Agent by default This is a experimental feature we still need refine it further * test: add Agent eval tests * fix: render all feedback per iteration * style: resolve linter issues * style: fix mypy issues * fix: allow messages be empty on LLMCallCompletedEvent * feat: add Experiment evaluation framework with baseline comparison * fix: reset evaluator for each experiement iteraction * fix: fix track of new test cases * chore: split Experimental evaluation classes * refactor: remove unused method * refactor: isolate Console print in a dedicated class * fix: make crew required to run an experiment * fix: use time-aware to define experiment result * test: add tests for Evaluator Experiment * style: fix linter issues * fix: encode string before hashing * style: resolve linter issues * feat: add experimental folder for beta features (#3141) * test: move tests to experimental folder
82 lines
3.0 KiB
Python
82 lines
3.0 KiB
Python
from unittest.mock import patch, MagicMock
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from crewai.experimental.evaluation.base_evaluator import EvaluationScore
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from crewai.experimental.evaluation.metrics.semantic_quality_metrics import SemanticQualityEvaluator
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from tests.experimental.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
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from crewai.utilities.llm_utils import LLM
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class TestSemanticQualityEvaluator(BaseEvaluationMetricsTest):
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@patch("crewai.utilities.llm_utils.create_llm")
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def test_evaluate_success(self, mock_create_llm, mock_agent, mock_task, execution_trace):
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mock_llm = MagicMock(spec=LLM)
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mock_llm.call.return_value = """
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{
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"score": 8.5,
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"feedback": "The output is clear, coherent, and logically structured."
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}
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"""
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mock_create_llm.return_value = mock_llm
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evaluator = SemanticQualityEvaluator(llm=mock_llm)
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result = evaluator.evaluate(
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agent=mock_agent,
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task=mock_task,
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execution_trace=execution_trace,
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final_output="This is a well-structured analysis of the data."
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)
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assert isinstance(result, EvaluationScore)
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assert result.score == 8.5
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assert "clear, coherent" in result.feedback
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mock_llm.call.assert_called_once()
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prompt = mock_llm.call.call_args[0][0]
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assert len(prompt) >= 2
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assert "system" in prompt[0]["role"]
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assert "user" in prompt[1]["role"]
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assert mock_agent.role in prompt[1]["content"]
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assert mock_task.description in prompt[1]["content"]
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@patch("crewai.utilities.llm_utils.create_llm")
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def test_evaluate_with_empty_output(self, mock_create_llm, mock_agent, mock_task, execution_trace):
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mock_llm = MagicMock(spec=LLM)
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mock_llm.call.return_value = """
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{
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"score": 2.0,
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"feedback": "The output is empty or minimal, lacking substance."
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}
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"""
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mock_create_llm.return_value = mock_llm
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evaluator = SemanticQualityEvaluator(llm=mock_llm)
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result = evaluator.evaluate(
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agent=mock_agent,
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task=mock_task,
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execution_trace=execution_trace,
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final_output=""
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)
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assert isinstance(result, EvaluationScore)
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assert result.score == 2.0
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assert "empty or minimal" in result.feedback
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@patch("crewai.utilities.llm_utils.create_llm")
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def test_evaluate_error_handling(self, mock_create_llm, mock_agent, mock_task, execution_trace):
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mock_llm = MagicMock(spec=LLM)
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mock_llm.call.return_value = "Invalid JSON response"
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mock_create_llm.return_value = mock_llm
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evaluator = SemanticQualityEvaluator(llm=mock_llm)
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result = evaluator.evaluate(
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agent=mock_agent,
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task=mock_task,
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execution_trace=execution_trace,
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final_output="This is the output."
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)
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assert isinstance(result, EvaluationScore)
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assert result.score is None
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assert "Failed to parse" in result.feedback |