Overview

Description

Intended Use

This validator checks to see if a given numerical output is within an expected range.

Requirements

Dependencies:
- guardrails-ai>=0.4.0

Installation

$ guardrails hub install hub://guardrails/valid_range

Usage Examples

Validating JSON output via Python

In this example, we’ll use the validator to check that a field of a JSON output is within an expected range.

# Import Guard and Validator
from pydantic import BaseModel, Field
from guardrails.hub import ValidRange
from guardrails import Guard

# Initialize Validator
val = ValidRange(min=0, max=10, on_fail="exception")


# Create Pydantic BaseModel
class PetInfo(BaseModel):
    pet_name: str
    pet_age: int = Field(validators=[val])


# Create a Guard to check for valid Pydantic output
guard = Guard.from_pydantic(output_class=PetInfo)

# Run LLM output generating JSON through guard
guard.parse(
    """
    {
        "pet_name": "Caesar",
        "pet_age": 5
    }
    """
)

try:
    # Run LLM output generating JSON through guard
    guard.parse(
        """
        {
            "pet_name": "Caesar",
            "pet_age": 15
        }
        """
    )
except Exception as e:
    print(e)

Output:

Validation failed for field with errors: Value 15 is greater than 10.

API Reference

__init__(self, min=None, max=None, on_fail="noop")

Initializes a new instance of the Validator class.

Parameters:

min (int): The inclusive minimum value of the range.
max (int): The inclusive maximum value of the range.
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:

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.