pytest-evals

pytest-evals 🚀

Test your LLM outputs against examples - no more manual checking! A (minimalistic) pytest plugin that helps you to evaluate that your LLM is giving good answers.

🧐 Why pytest-evals?

Building LLM applications is exciting, but how do you know they're actually working well? pytest-evals helps you:

• 🎯 Test & Evaluate: Run your LLM prompt against many cases
• 📈 Track & Measure: Collect metrics and analyze the overall performance
• 🔄 Integrate Easily: Works with pytest, Jupyter notebooks, and CI/CD pipelines
• ✨ Scale Up: Run tests in parallel with pytest-xdist and asynchronously with pytest-asyncio.

🚀 Getting Started

To get started, install pytest-evals and write your tests:

pip install pytest-evals

⚡️ Quick Example

For example, say you're building a support ticket classifier. You want to test cases like:

Input Text	Expected Classification
My login isn't working and I need to access my account	account_access
Can I get a refund for my last order?	billing
How do I change my notification settings?	settings

pytest-evals helps you automatically test how your LLM perform against these cases, track accuracy, and ensure it keeps working as expected over time.

# Predict the LLM performance for each case
@pytest.mark.eval(name="my_classifier")
@pytest.mark.parametrize("case", TEST_DATA)
def test_classifier(case: dict, eval_bag, classifier):
    # Run predictions and store results
    eval_bag.prediction = classifier(case["Input Text"])
    eval_bag.expected = case["Expected Classification"]
    eval_bag.accuracy = eval_bag.prediction == eval_bag.expected


# Now let's see how our app performing across all cases...
@pytest.mark.eval_analysis(name="my_classifier")
def test_analysis(eval_results):
    accuracy = sum([result.accuracy for result in eval_results]) / len(eval_results)
    print(f"Accuracy: {accuracy:.2%}")
    assert accuracy >= 0.7  # Ensure our performance is not degrading 🫢

Then, run your evaluation tests:

# Run test cases
pytest --run-eval

# Analyze results
pytest --run-eval-analysis

😵‍💫 Why Another Eval Tool?

Evaluations are just tests. No need for complex frameworks or DSLs. pytest-evals is minimalistic by design:

• Use pytest - the tool you already know
• Keep tests and evaluations together
• Focus on logic, not infrastructure

It just collects your results and lets you analyze them as a whole. Nothing more, nothing less.

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📚 User Guide

Check out our detailed guides and examples:

• Basic evaluation
• Basic of LLM as a judge evaluation
• Notebook example
• Advanced notebook example

🤔 How It Works

Built on top of pytest-harvest, pytest-evals splits evaluation into two phases:

1. Evaluation Phase: Run all test cases, collecting results and metrics in eval_bag. The results are saved in a temporary file to allow the analysis phase to access them.
2. Analysis Phase: Process all results at once through eval_results to calculate final metrics

This split allows you to:

• Run evaluations in parallel (since the analysis test MUST run after all cases are done, we must run them separately)
• Make pass/fail decisions on the overall evaluation results instead of individual test failures (by passing the --supress-failed-exit-code --run-eval flags)
• Collect comprehensive metrics

Note: When running evaluation tests, the rest of your test suite will not run. This is by design to keep the results clean and focused.

💾 Saving case results

By default, pytest-evals saves the results of each case in a json file to allow the analysis phase to access them. However, this might not be a friendly format for deeper analysis. To save the results in a more friendly format, as a CSV file, use the --save-evals-csv flag:

pytest --run-eval --save-evals-csv

📝 Working with a notebook

It's also possible to run evaluations from a notebook. To do that, simply install ipytest, and load the extension:

%load_ext pytest_evals

Then, use the magic commands %%ipytest_eval in your cell to run evaluations. This will run the evaluation phase and then the analysis phase. By default, using this magic will run both --run-eval and --run-eval-analysis, but you can specify your own flags by passing arguments right after the magic command (e.g., %%ipytest_eval --run-eval).

%%ipytest_eval
import pytest


@pytest.mark.eval(name="my_eval")
def test_agent(eval_bag):
    eval_bag.prediction = agent.run(case["input"])


@pytest.mark.eval_analysis(name="my_eval")
def test_analysis(eval_results):
    print(f"F1 Score: {calculate_f1(eval_results):.2%}")

You can see an example of this in the example/example_notebook.ipynb notebook. Or look at the advanced example for a more complex example that tracks multiple experiments.

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🏗️ Production Use

📚 Managing Test Data (Evaluation Set)

It's recommended to use a CSV file to store test data. This makes it easier to manage large datasets and allows you to communicate with non-technical stakeholders.

To do this, you can use pandas to read the CSV file and pass the test cases as parameters to your tests using @pytest.mark.parametrize 🙃 :

import pandas as pd
import pytest

test_data = pd.read_csv("tests/testdata.csv")


@pytest.mark.eval(name="my_eval")
@pytest.mark.parametrize("case", test_data.to_dict(orient="records"))
def test_agent(case, eval_bag, agent):
    eval_bag.prediction = agent.run(case["input"])

In case you need to select a subset of the test data (e.g., a golden set), you can simply define an environment variable to indicate that, and filter the data with pandas.

🔀 CI Integration

Run tests and analysis as separate steps:

evaluate:
  steps:
    - run: pytest --run-eval -n auto --supress-failed-exit-code  # Run cases in parallel
    - run: pytest --run-eval-analysis  # Analyze results

Use --supress-failed-exit-code with --run-eval - let the analysis phase determine success/failure. If all your cases pass, your evaluation set is probably too small!

⚡️ Parallel Testing

As your evaluation set grows, you may want to run your test cases in parallel. To do this, install pytest-xdist. pytest-evals will support that out of the box 🚀.

run: pytest --run-eval -n auto

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👷 Contributing

Contributions make the open-source community a fantastic place to learn, inspire, and create. Any contributions you make are greatly appreciated (not only code! but also documenting, blogging, or giving us feedback) 😍.

Please fork the repo and create a pull request if you have a suggestion. You can also simply open an issue to give us some feedback.

Don't forget to give the project a star! ⭐️

For more information about contributing code to the project, read the CONTRIBUTING.md guide.

📃 License

This project is licensed under the MIT License - see the LICENSE file for details.

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