> ## Documentation Index
> Fetch the complete documentation index at: https://docs.neuraltrust.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Language

The Language Evaluators are specialized tools designed to validate the language of text responses. There are two types of language evaluators:

1. **Expected Language Evaluator**: Checks if the response is in a specific expected language
2. **Equal Language Evaluator**: Checks if the response is in the same language as the question

## Purpose

The Language Evaluators are particularly useful when you need to:

* Ensure responses are in the correct language
* Verify language consistency between questions and answers
* Validate multilingual content
* Check language requirements compliance
* Monitor language-specific responses

## How It Works

Both evaluators use a binary scoring system based on language detection:

### Expected Language Evaluator

* **Score: 1**: The response is in the expected language
* **Score: 0**: The response is not in the expected language

### Equal Language Evaluator

* **Score: 1**: The response is in the same language as the question
* **Score: 0**: The response is in a different language than the question

The evaluation uses the `langdetect` library to detect the language of the text, with special handling for Spanish and Portuguese languages in the Equal Language Evaluator.

## Usage Examples

### Expected Language Evaluator

```python theme={null}
import asyncio

from trusttest.evaluation_contexts import Context
from trusttest.evaluators import ExpectedLanguageEvaluator


async def evaluate():
    evaluator = ExpectedLanguageEvaluator(
        expected_language="es"
    )
    result = await evaluator.evaluate(
        response="Hola, ¿cómo estás?",
        context=Context()
    )
    print(result)

if __name__ == "__main__":
    asyncio.run(evaluate())
```

### Equal Language Evaluator

```python theme={null}
import asyncio

from trusttest.evaluation_contexts import QuestionContext
from trusttest.evaluators import EqualLanguageEvaluator


async def evaluate():
    evaluator = EqualLanguageEvaluator()

    result = await evaluator.evaluate(
        response="Hola, ¿cómo estás?",
        context=QuestionContext(
            question="¿Qué tal estás?"
        )
    )
    print(result)

if __name__ == "__main__":
    asyncio.run(evaluate())
```

The evaluators return a tuple containing:

* A binary score (0 or 1) indicating language match status
* A list of explanations including:
  * Success message with the detected language if matched
  * Failure message with both detected and expected languages if not matched

## When to Use

Use the Language Evaluators when you need to:

* Ensure responses are in the correct language
* Verify language consistency in conversations
* Validate multilingual content
* Check language requirements
* Monitor language-specific responses
* Ensure proper language handling in chatbots
* Validate language-specific content generation