Overview

Description

Intended Use

This validator ensures that any LLM generated text is readable within an expected reading time. The reading time estimation is done at 200 words / min.

Requirements

Dependencies:
- guardrails-ai>=0.4.0

Installation

$ guardrails hub install hub://guardrails/reading_time

Usage Examples

Validating string output via Python

In this example, we’ll use the validator to validate that an LLM description is under 5 seconds of reading time.

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

FIVE_SECONDS = 5 / 60
# Use the Guard with the validator
guard = Guard().use(ReadingTime, reading_time=FIVE_SECONDS, on_fail="exception")

# Test passing response
guard.validate("Azure is a cloud computing service created by Microsoft.")

try:
    # Test failing response
    guard.validate(
        """
        Azure is a cloud computing service created by Microsoft. It was first announced in 2008 and 
        released in 2010. It is a cloud computing service that provides a range of services, 
        including those for compute, analytics, storage, and networking. 
        It can be used to build, deploy, and manage applications and services.
        """
    )
except Exception as e:
    print(e)

Output:

Validation failed for field with errors: String should be readable within 0.083 min. but took 0.255 min. to read.

API Reference

__init__(self, reading_time, on_fail="noop")

Initializes a new instance of the Validator class.

Parameters:

reading_time (float): The maximum reading time in minutes.
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

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:

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.