We leverage the existing ecosystem for testing, so you don’t have to learn a new tool and can call your application logic directly.
Testing Dashboard

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Aggregation

We automatically aggregate all of your LLM and custom logs by test. This is helpful to get full visibility into each step of your workflow.

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Integrations

We provide plugins for the most popular testing frameworks. Check out the pytest and jest documentation to get started.

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Assertions

To validate the behavior of your code you can make assertions like you normally would to cause the test to pass or fail. In the following example, the test will fail if the string “Eiffel Tower” is not present in the LLM output.
Offline testing assertion
import openai

def test_paris_trip():
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        temperature=0.7,
        messages=[
            {
                "role": "user",
                "content": "What are three activities to do in Paris?"
            }
        ],
    )

    assert "Eiffel Tower" in response["choices"][0]["message"]["content"]

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Automatic evaluation

When working with LLMs, it is also helpful to gain more visibility into the outputs using a series of checks or evaluations. Evaluations capture information about the output but will not cause the test to fail by default. Eval results are then aggregated and displayed in your Baserun dashboard.
Offline testing assertion
In the following example, we add an eval to check if the output has the phrase “AI Language Model”. See the evaluation documentation for more information. 
import baserun
import openai

def test_paris_trip():
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        temperature=0.7,
        messages=[
            {
                "role": "user",
                "content": "What are three activities to do in Paris?"
            }
        ],
    )

    output = response["choices"][0]["message"]["content"]
    not_has_ai_language_model = baserun.evals.not_includes("AI Language Model", output, ["AI language model"])
    # Optional: will fail the test if "AI language model" is present in the output
    assert not_has_ai_language_model