Overview

Description

Intended Use

The validator ensures that a generated output is a single line based on whether the output has a newline character.

Requirements

Dependencies:
- guardrails-ai>=0.4.0

Installation

guardrails hub install hub://guardrails/one_line

Usage Examples

Validating string output via Python

In this example, we’ll test that a generated LLM sentence is a single line.

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

# Use the Guard with the validator
guard = Guard().use(OneLine, 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(
        "Christopher Nolan's Tenet is a mind-bending action thriller that will keep you on the edge of your seat\n. The film is a must-watch for all Nolan fans\n. Dunkirk was a great movie too."
    )
except Exception as e:
    print(e)

Output:

Validation failed for field with errors: Value 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
. Dunkirk was a great movie too. is not a single line.

Validating JSON output via Python

In this example, we verify that a summary of a product contains a single line.

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

# Initialize Validator
val = OneLine(on_fail="exception")

# Create Pydantic BaseModel
class ProductInfo(BaseModel):
    product_name: str = Field(description="Name of the product")
    product_summary: str = Field(
        description="A one line summary of the product", validators=[val]
    )

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

# Passing response
guard.parse(
    """
    {
        "product_name": "Hairspray",
        "product_summary": "This product helps your styled hair stay in place."
    }
    """
)

# Failing response
try:
    # Run LLM output generating JSON through guard
    guard.parse(
        """
        {
            "product_name": "Hairspray",
            "product_summary": "This product helps your styled hair stay in place\n. It is a very good product."
        }
        """
    )
except Exception as e:
    print(e)

Output:

Validation failed for field with errors: Value This product helps your styled hair stay in place
. It is a very good product. is not a single line.

API Reference

__init__(self, on_fail="noop")

Initializes a new instance of the Validator class.

Parameters:

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.