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| Developed by | Guardrails AI |
| Date of development | Feb 15, 2024 |
| Validator type | Format |
| Blog | |
| License | Apache 2 |
| Input/Output | Output |
This validator checks to see if a given numerical output is within an expected range.
$ guardrails hub install hub://guardrails/valid_range
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.
__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:
guard.parse(...) where this method will be called internally for each associated Validator.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. No additional metadata keys are needed for this validator.The validator playground is available to authenticated users. Please log in to use it.