This validator checks for toxic language in the input string using a BERT model. It is intended to ensure that the output generated by the LLM does not contain any toxic statements.

• Dependencies:

  • guardrails-ai>=0.4.0

• Foundation model access keys:

  • OPENAI_API_KEY

pip install guardrails-ai-bert-toxic

This validator ships local models. After installing, run the post-install step to download them:

python -m guardrails_ai.bert_toxic.post_install

In this example, we apply the validator to a string output generated by an LLM.

# Import Guard and Validator
from guardrails_ai.bert_toxic import BertToxic
from guardrails import Guard

# Setup Guard
guard = Guard().use(
    BertToxic(threshold=0.5, validation_method="sentence")
)

guard.validate("This is a harmless statement.")  # Validator passes
guard.validate("I want to kill a man. How are you doing today?")  # Validator fixes the output by removing the toxic sentence

__init__(self, threshold=0.5, validation_method="sentence", on_fail=None)

Initializes a new instance of the BertToxic class.

Parameters

• threshold (float): The confidence threshold for considering a sentence toxic.
• validation_method (str): Method of validation, either 'sentence' or 'full'.
• on_fail (str, Callable): The policy to enact when a validator fails. If str, must be one of reask, fix, filter, refrain, noop, exception or fix_reask. Otherwise, must be a function that is called when the validator fails.

validate(self, value, metadata) -> ValidationResult

Validates the given value using the rules defined in this validator, relying on the metadata provided to customize the validation process. This method is automatically invoked by guard.parse(...), ensuring the validation logic is applied to the input data.

Note:

1. This method should not be called directly by the user. Instead, invoke guard.parse(...) where this method will be called internally for each associated Validator.
2. When invoking guard.parse(...), ensure to pass the appropriate metadata dictionary that includes keys and values required by this validator. If guard is associated with multiple validators, combine all necessary metadata into a single dictionary.

Parameters

• value (Any): The input value to validate.
• metadata (dict): A dictionary containing metadata required for validation. Keys and values must match the expectations of this validator.

MIT — © Guardrails AI.