Introduction

Quick Summary

Evaluation refers to the process of testing your LLM application outputs, and requires the following components:

• Test cases
• Metrics
• Evaluation dataset

Here's a diagram of what an ideal evaluation workflow looks like using deepeval:

Your test cases will typically be in a single python file, and executing them will be as easy as running deepeval test run:

deepeval test run test_example.py

tip

Click here for an end-to-end tutorial on how to evaluate an LLM medical chatbot using deepeval.

Metrics

deepeval offers 30+ evaluation metrics, most of which are evaluated using LLMs (visit the metrics section to learn why).

from deepeval.metrics import AnswerRelevancyMetric

answer_relevancy_metric = AnswerRelevancyMetric()

You'll need to create a test case to run deepeval's metrics.

Test Cases

In deepeval, a test case allows you to use evaluation metrics you have defined to unit test LLM applications.

from deepeval.test_case import LLMTestCase

test_case = LLMTestCase(
  input="Who is the current president of the United States of America?",
  actual_output="Joe Biden",
  retrieval_context=["Joe Biden serves as the current president of America."]
)

In this example, input mimics an user interaction with a RAG-based LLM application, where actual_output is the output of your LLM application and retrieval_context is the retrieved nodes in your RAG pipeline. Creating a test case allows you to evaluate using deepeval's default metrics:

from deepeval.test_case import LLMTestCase
from deepeval.metrics import AnswerRelevancyMetric

answer_relevancy_metric = AnswerRelevancyMetric()
test_case = LLMTestCase(
  input="Who is the current president of the United States of America?",
  actual_output="Joe Biden",
  retrieval_context=["Joe Biden serves as the current president of America."]
)

answer_relevancy_metric.measure(test_case)
print(answer_relevancy_metric.score)

Datasets

Datasets in deepeval is a collection of test cases. It provides a centralized interface for you to evaluate a collection of test cases using one or multiple metrics.

from deepeval.test_case import LLMTestCase
from deepeval.dataset import EvaluationDataset
from deepeval.metrics import AnswerRelevancyMetric

answer_relevancy_metric = AnswerRelevancyMetric()
test_case = LLMTestCase(
  input="Who is the current president of the United States of America?",
  actual_output="Joe Biden",
  retrieval_context=["Joe Biden serves as the current president of America."]
)

dataset = EvaluationDataset(test_cases=[test_case])
dataset.evaluate([answer_relevancy_metric])

note

You don't need to create an evaluation dataset to evaluate individual test cases. Visit the test cases section to learn how to assert inidividual test cases.

Synthesizer

In deepeval, the Synthesizer allows you to generate synthetic datasets. This is especially helpful if you don't have production data or you don't have a golden dataset to evaluate with.

from deepeval.synthesizer import Synthesizer
from deepeval.dataset import EvaluationDataset

synthesizer = Synthesizer()
goldens = synthesizer.generate_goldens_from_docs(
  document_paths=['example.txt', 'example.docx', 'example.pdf']
)

dataset = EvaluationDataset(goldens=goldens)

info

deepeval's Synthesizer is highly customizable, and you can learn more about it here.

Evaluating With Pytest

caution

Although deepeval integrates with Pytest, we highly recommend you to AVOID executing LLMTestCases directly via the pytest command to avoid any unexpected errors.

deepeval allows you to run evaluations as if you're using Pytest via our Pytest integration. Simply create a test file:

from deepeval import assert_test
from deepeval.test_case import LLMTestCase
from deepeval.metrics import AnswerRelevancyMetric

dataset = EvaluationDataset(test_cases=[...])

@pytest.mark.parametrize(
    "test_case",
    dataset,
)
def test_customer_chatbot(test_case: LLMTestCase):
    answer_relevancy_metric = AnswerRelevancyMetric()
    assert_test(test_case, [answer_relevancy_metric])

And run the test file in the CLI using deepeval test run:

deepeval test run test_example.py

There are TWO mandatory and ONE optional parameter when calling the assert_test() function:

• test_case: an LLMTestCase
• metrics: a list of metrics of type BaseMetric
• [Optional] run_async: a boolean which when set to True, enables concurrent evaluation of all metrics. Defaulted to True.

You can find the full documentation on deepeval test run, including how to use it with tracing, on the running LLM-evals page.

info

@pytest.mark.parametrize is a decorator offered by Pytest. It simply loops through your EvaluationDataset to evaluate each test case individually.

You can include the deepeval test run command as a step in a .yaml file in your CI/CD workflows to run pre-deployment checks on your LLM application.

Evaluating Without Pytest

Alternately, you can use deepeval's evaluate function. This approach avoids the CLI (if you're in a notebook environment), and allows for parallel test execution as well.

from deepeval import evaluate
from deepeval.metrics import AnswerRelevancyMetric
from deepeval.dataset import EvaluationDataset

dataset = EvaluationDataset(test_cases=[...])
answer_relevancy_metric = AnswerRelevancyMetric()

evaluate(dataset, [answer_relevancy_metric])

There are TWO mandatory and SIX optional parameters when calling the evaluate() function:

• test_cases: a list of LLMTestCases OR ConversationalTestCases, or an EvaluationDataset. You cannot evaluate LLMTestCase/MLLMTestCases and ConversationalTestCases in the same test run.
• metrics: a list of metrics of type BaseMetric.
• [Optional] hyperparameters: a dict of type dict[str, Union[str, int, float]]. You can log any arbitrary hyperparameter associated with this test run to pick the best hyperparameters for your LLM application on Confident AI.
• [Optional] identifier: a string that allows you to better identify your test run on Confident AI.
• [Optional] async_config: an instance of type AsyncConfig that allows you to customize the degree concurrency during evaluation. Defaulted to the default AsyncConfig values.
• [Optional] display_config:an instance of type DisplayConfig that allows you to customize what is displayed to the console during evaluation. Defaulted to the default DisplayConfig values.
• [Optional] error_config: an instance of type ErrorConfig that allows you to customize how to handle errors during evaluation. Defaulted to the default ErrorConfig values.
• [Optional] cache_config: an instance of type CacheConfig that allows you to customize the caching behavior during evaluation. Defaulted to the default CacheConfig values.

You can find the full documentation on evaluate(), including how to use it with tracing, on the running LLM-evals page.

tip

You can also replace dataset with a list of test cases, as shown in the test cases section.

Evaluating Nested Components

You can also run metrics on nested components by setting up tracing in deepeval, and requires under 10 lines of code:

from deepeval.test_case import LLMTestCase
from deepeval.metrics import AnswerRelevancyMetric
from deepeval.tracing import observe, update_current_span_test_case

@observe(metrics=[AnswerRelevancyMetric()])
def complete(query: str):
  response = openai.ChatCompletion.create(model="gpt-4o", messages=[{"role": "user", "content": query}]).choices[0].message["content"]

  update_current_span_test_case(
    test_case=LLMTestCase(input=query, output=response)
  )
  return response

This is very useful especially if you:

• Want to run a different set of metrics on differnet components
• Wish to evaluate multiple components at once
• Don't want to rewrite your codebase just to bubble up returned variables to create an LLMTestCase

You can also simply configure deepeval to not run any metrics when you're running your LLM application in production. For the full guide on evaluating with tracing, visit this page.