Overview

Description

Intended Use

This validator checks if an LLM-generated text is not safe for work (NSFW). It validates both sentence-by-sentence and the entire text.

Requirements

Dependencies:
- guardrails-ai>=0.4.0
- nltk
- transformers
- torch

Installation

$ guardrails hub install hub://guardrails/nsfw_text

Usage Examples

Validating string output via Python

In this example, we use the nsfw_text validator on any LLM generated text.

# Import Guard and Validator
from guardrails.hub import NSFWText
from guardrails import Guard

# Setup Guard with the validator
guard = Guard().use(
    NSFWText, threshold=0.8, validation_method="sentence", on_fail="exception"
)

# Test passing response
guard.validate(
    "Christopher Nolan's Tenet is a mind-bending action thriller that will keep you on the edge of your seat. The film is a must-watch for all Nolan fans."
)

try:
    # Test failing response
    guard.validate(
        "Climate Change is real and we need to do something about it. Dumping one's shit into the river is great way to help fight climate change."
    )
except Exception as e:
    print(e)

Output:

Validation failed for field with errors: The following sentences in your response were found to be NSFW:

- Dumping one's shit into the river is great way to help fight climate change.

API Reference

__init__(self, threshold=0.8, validation_method="sentence", on_fail="noop")

Parameters

threshold (float): The confidence threshold over which model inferences are considered. Default is 0.8.
validation_method (str): The method to use for validation. If sentence, the validator will validate each sentence in the input text. If full, the validator will validate the entire input text. Default is sentence.
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.

__call__(self, value, metadata={}) -> ValidationResult

Note:

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.
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.