DeepEval

➺ Core Features:

  • Automated testing suite
  • Performance benchmarking
  • Dataset evaluation
  • Quality metrics 

    • ➺ Technical Implementation:

    Key Components 
  • Metric collectors
  • Test orchestration
  • Results aggregation
  • Report generation 

    • ➺ Evaluation Types:

  • Unit tests
  • Integration tests
  • Regression testing
  • Stress testing 

    • ➺ Custom Metrics:

  • Response latency
  • Memory usage
  • Token accuracy
  • Semantic similarity 

    • ➺ Usage Guide:

    python

    # Example DeepEval Implementation from deepeval import MetricCollector, TestCase

    def test_llm_response(): test_case = TestCase(input=”What is RAG?”,
    actual_output=llm_response, expected_output=ground_truth,

    metrics=[
    AccuracyMetric(threshold=0.8),
    LatencyMetric(max_time=2.0)]
    )
    assert test_case.run()