Response Language Classifier

The ResponseLanguageClassifier in deepeval is a categorical LLM-as-a-judge that determines whether your LLM application replied in the same language the user wrote in.

For applications that support more than one language, inputs across all of them belong in the dataset. This catches the common failure where a model drifts back to English, or to whatever language its system prompt was written in.

Labels

• matches_user: the response is written in the same language as the user's input.
• mismatch: the response is written in a different language from the user's input.

Usage

from deepeval.classifiers import ResponseLanguageClassifier
from deepeval.test_case import LLMTestCase
from deepeval import evaluate

classifier = ResponseLanguageClassifier()

test_case = LLMTestCase(
    input="¿Dónde está mi pedido?",
    actual_output="Su pedido fue enviado ayer y llegará el jueves.",
    expected_labels={classifier.name: "matches_user"},
)

evaluate(test_cases=[test_case], classifiers=[classifier])

There are FIVE optional parameters when creating a ResponseLanguageClassifier:

• [Optional] model: a string specifying which of OpenAI's GPT models to use, OR any custom LLM model of type DeepEvalBaseLLM. Defaulted to gpt-5.4.
• [Optional] include_reason: a boolean which when set to True, includes a reason for the chosen label. Defaulted to True.
• [Optional] allow_none: a boolean which when set to True, lets the classifier return no label when none of them fit (surfaced as label=None, with a reason). When False, the closest label is always chosen. Defaulted to False.
• [Optional] async_mode: a boolean which when set to True, enables concurrent execution within the classify() method. Defaulted to True.
• [Optional] classification_template: a subclass of ClassifierTemplate used to override the default prompts. Defaulted to ClassifierTemplate.

As a standalone

You can also run the ResponseLanguageClassifier on a single test case as a standalone, one-off execution. classify() returns the label and stores the result on the instance:

...

label = classifier.classify(test_case)
print(classifier.label, classifier.reason)

How Is It Calculated?

The ResponseLanguageClassifier is a one-shot LLM-as-a-judge: a single call to your evaluation model with the test case content and the labels above, returning the chosen label and a reason. The result is compared against the test case's expected_labels entry, if any, to decide pass or fail.