valid_length: A Python repository from jonasferoz

Overview

Developed by	Guardrails AI
Date of development	Feb 15, 2024
Validator type	Format
Blog	-
License	Apache 2
Input/Output	Output

Description

This validator can perform the following checks:

If applying this validator on a string: ensures that a generated string is of an expected length
If applying this validator on a generated JSON object: ensures that a generated list is of an expected length

Installation

guardrails hub install hub://guardrails/valid_length

Usage Examples

Validating string output via Python

In this example, we verify that an LLM generated response contains anywhere from 3-6 characters.

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

# Setup Guard
guard = Guard().use(ValidLength, min=3, max=6, on_fail="exception")
response = guard.validate("hello")  # Validator passes

try:
    response = guard.validate("hello world!")  # Validator fails
except Exception as e:
    print(e)

Output:

Validation failed for field with errors: Value has length greater than 6. Please return a shorter output, that is shorter than 6 characters.

Validating the length of a list within a generated JSON

This example applies the validator to a list of a JSON object, and ensures that the length of the list is within an expected range.

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

val = ValidLength(min=1, max=2, on_fail="exception")


# Create Pydantic BaseModels
class ProductInfo(BaseModel):
    """Information about a single product."""

    product_name: str = Field(description="Name of the product")
    product_summary: str = Field(description="A summary of the product")


class ProductCategory(BaseModel):
    """List of products."""

    category_name: str = Field(description="Name of product category")
    products: list[ProductInfo] = Field(
        description="List of products", validators=[val]
    )


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

# Run LLM output generating JSON through guard
guard.parse(
    """
    {
        "category_name": "Hair care",
        "products": [
            {
                "product_name": "Hair spray",
                "product_summary": "Helps your styled hair stay in place."
            },
            {
                "product_name": "Shampoo",
                "product_summary": "Helps clean your hair."
            }
        ]
    """
)

try:
    # Run LLM output generating JSON through guard
    guard.parse(
        """
        {
            "category_name": "Hair care",
            "products": [
                {
                    "product_name": "Hair spray",
                    "product_summary": "Helps your styled hair stay in place."
                },
                {
                    "product_name": "Shampoo",
                    "product_summary": "Helps clean your hair."
                },
                {
                    "product_name": "Conditioner",
                    "product_summary": "Helps condition your hair."
                }
            ]
        }
        """
    )
except Exception as e:
    print(e)

Output:

Validation failed for field with errors: Value has length greater than 2. Please return a shorter output, that is shorter than 2 characters.

API Reference

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

Initializes a new instance of the Validator class.

Parameters:

min (int): Min expected length of the object (str, list).
max (int): Max expected length of the object (str, list).
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.