Validate that an LLM-generated text is in the expected language. If the text is not in the expected language, the validator will attempt to translate it to the expected language.

Use fast-langdetect library to detect the language of the input text, and iso-language-codes library to get the language names from the ISO codes.

Utilize Meta's facebook/nllb-200-distilled-600M translation model (available on Huggingface) to translate the text from the detected language to the expected language.

• Primary intended uses: This validator is useful when you’re using multiple languages in an LLM application.
• Out-of-scope use cases: N/A

• Dependencies:
  • fast_langdetect
  • iso_language_codes
  • transformers HuggingFace library
  • facebook/nllb-200-distilled-600M translation model
• Foundation model access keys: HuggingFace

$ guardrails hub install hub://guardrails/correct_language

from guardrails.hub import CorrectLanguage

# Create validator
language_validator = CorrectLanguage(expected_language_iso="en", threshold=0.75)

# Create Guard with Validator
guard = Guard.from_string(
    validators=[language_validator, ...],
    num_reasks=2,
)

guard.parse("Translated_text")

guard("Translate this to english: Danke")

N/A

• Dependencies:
  • fast_langdetect
  • iso_language_codes
  • transformers HuggingFace library
  • facebook/nllb-200-distilled-600M translation model
• Foundation model access keys: Huggingface auth
• Compute: Yes

N/A

0.7