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Using Datasets for Evaluation

To start using your datasets for evaluation, you’ll need to:

  1. Pull your dataset from Confident AI.
  2. Compute the actual outputs and retrieval contexts, and convert your goldens into test cases.
  3. Begin running evaluations.

In this tutorial, we’ll pull the synthetic dataset generated in the previous section and run evaluations on the dataset against the three metrics we’ve defined: Answer Relevancy, Faithfulness, and Professionalism.

Pulling Your Dataset​

To pull a dataset from Confident AI, simply call the pull method from an EvaluationDataset and specify the dataset alias you wish to retrieve. By default, auto_convert_goldens_to_test_cases is set to True, but we'll set it to False for this tutorial since the actual output is a required parameter in an LLMTestCase, and we haven't generated them yet.

from deepeval import EvaluationDataset

dataset = EvaluationDataset()
dataset.pull(alias="Patients Seeking Diagnosis", auto_convert_goldens_to_test_cases=False)

Converting Goldens to Test Cases​

Next, we'll convert the goldens in the dataset we pulled into LLMTestCases and add them to our evaluation dataset. Although our goldens have contexts and expected outputs, we won’t need them for our current set of metrics.

from deepeval.test_case import LLMTestCase

for golden in dataset.goldens:
    # Compute actual output and retrieval context
    actual_output = "..."  # Replace with logic to compute actual output
    retrieval_context = "..."  # Replace with logic to compute retrieval context
    
    dataset.add_test_case(
        LLMTestCase(
            input=golden.input,
            actual_output=actual_output,
            retrieval_context=retrieval_context
        )
    )

Run Evaluations on your Dataset​

Finally, we'll redefine our three metrics and use the evaluate function to run evaluations on our synthetic dataset.

from deepeval.metrics import AnswerRelevancy, Faithfulness, GEval
from deepeval import evaluate

# Metrics Definition
answer_relevancy_metric = AnswerRelevancy()
faithfulness_metric = Faithfulness()
professionalism_metric = GEval(
    name="Professionalism",
    criteria=criteria,
    evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT]
)

evaluate(
  dataset, 
  metrics = [answer_relevancy_metric, faithfulness_metric, professionalism_metric],
  hyperparameters={
    "model": "gpt-4o"
    "prompt template": "You are a..."
    "temperature": 0.8
  }
)

Here are the final evaluation results:

Datasets 1

You can see that although we passed all 5 test cases previously, it's important to test on a larger dataset, as 4 out of the 15 test cases we generated are still failing. To learn more about iterating on your hyperparameters, you can revisit this section.