Testing

testing

LangSmith pytest testing module.

FUNCTION	DESCRIPTION
log_feedback	Log run feedback from within a pytest test run.
log_inputs	Log run inputs from within a pytest test run.
log_outputs	Log run outputs from within a pytest test run.
log_reference_outputs	Log example reference outputs from within a pytest test run.
trace_feedback	Trace the computation of a pytest run feedback as its own run.

log_feedback

log_feedback(
    feedback: dict | list[dict] | None = None,
    /,
    *,
    key: str,
    score: int | bool | float | None = None,
    value: str | int | float | bool | None = None,
    **kwargs: Any,
) -> None

Log run feedback from within a pytest test run.

Warning

This API is in beta and might change in future versions.

Should only be used in pytest tests decorated with @pytest.mark.langsmith.

PARAMETER	DESCRIPTION
key	Feedback name. TYPE: str
score	Numerical feedback value. TYPE: int | bool | float | None DEFAULT: None
value	Categorical feedback value TYPE: str | int | float | bool | None DEFAULT: None
kwargs	Any other Client.create_feedback args. TYPE: Any DEFAULT: {}

Example

import pytest
from langsmith import testing as t


@pytest.mark.langsmith
def test_foo() -> None:
    x = 0
    y = 1
    expected = 2
    result = foo(x, y)
    t.log_feedback(key="right_type", score=isinstance(result, int))
    assert result == expected

log_inputs

log_inputs(inputs: dict) -> None

Log run inputs from within a pytest test run.

Warning

This API is in beta and might change in future versions.

Should only be used in pytest tests decorated with @pytest.mark.langsmith.

PARAMETER	DESCRIPTION
inputs	Inputs to log. TYPE: dict

Example

from langsmith import testing as t


@pytest.mark.langsmith
def test_foo() -> None:
    x = 0
    y = 1
    t.log_inputs({"x": x, "y": y})
    assert foo(x, y) == 2

log_outputs

log_outputs(outputs: dict) -> None

Log run outputs from within a pytest test run.

Warning

This API is in beta and might change in future versions.

Should only be used in pytest tests decorated with @pytest.mark.langsmith.

PARAMETER	DESCRIPTION
outputs	Outputs to log. TYPE: dict

Example

from langsmith import testing as t


@pytest.mark.langsmith
def test_foo() -> None:
    x = 0
    y = 1
    result = foo(x, y)
    t.log_outputs({"foo": result})
    assert result == 2

log_reference_outputs

log_reference_outputs(reference_outputs: dict) -> None

Log example reference outputs from within a pytest test run.

Warning

This API is in beta and might change in future versions.

Should only be used in pytest tests decorated with @pytest.mark.langsmith.

PARAMETER	DESCRIPTION
reference_outputs	Reference outputs to log. TYPE: dict

Example

from langsmith import testing


@pytest.mark.langsmith
def test_foo() -> None:
    x = 0
    y = 1
    expected = 2
    testing.log_reference_outputs({"foo": expected})
    assert foo(x, y) == expected

trace_feedback

trace_feedback(*, name: str = 'Feedback') -> Generator[RunTree | None, None, None]

Trace the computation of a pytest run feedback as its own run.

Warning

This API is in beta and might change in future versions.

PARAMETER	DESCRIPTION
name	Feedback run name. Defaults to "Feedback". TYPE: str DEFAULT: 'Feedback'

Example

import openai
import pytest

from langsmith import testing as t
from langsmith import wrappers

oai_client = wrappers.wrap_openai(openai.Client())


@pytest.mark.langsmith
def test_openai_says_hello():
    # Traced code will be included in the test case
    text = "Say hello!"
    response = oai_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": text},
        ],
    )
    t.log_inputs({"text": text})
    t.log_outputs({"response": response.choices[0].message.content})
    t.log_reference_outputs({"response": "hello!"})

    # Use this context manager to trace any steps used for generating evaluation
    # feedback separately from the main application logic
    with t.trace_feedback():
        grade = oai_client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {
                    "role": "system",
                    "content": "Return 1 if 'hello' is in the user message and 0 otherwise.",
                },
                {
                    "role": "user",
                    "content": response.choices[0].message.content,
                },
            ],
        )
        # Make sure to log relevant feedback within the context for the
        # trace to be associated with this feedback.
        t.log_feedback(
            key="llm_judge", score=float(grade.choices[0].message.content)
        )

    assert "hello" in response.choices[0].message.content.lower()