Overview

| Developed by | Guardrails AI | | Date of development | Feb 15, 2024 | | Validator type | Brand risk, QA, chatbots | | Blog | | | License | Apache 2 | | Input/Output | Output |

Description

Intended Use

This validator ensures that no competitors for an organization are being named. In order to use this validator, you need to provide a list of competitors that you don’t want to name.

Requirements

• Dependencies:
  • guardrails-ai>=0.4.0
  • nltk

Installation

guardrails hub install hub://guardrails/competitor_check

Usage Examples

Validating string output via Python

In this example, we apply the validator to a string output generated by an LLM.

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


# Setup Guard
guard = Guard().use(
    CompetitorCheck, ["Apple", "Samsung"], "exception"
)

response = guard.validate(
    "The apple doesn't fall far from the tree."
)  # Validator passes

try:
    response = guard.validate("Apple just released a new iPhone.")  # Validator fails
except Exception as e:
    print(e)

Output:

Validation failed for field with errors: Found the following competitors: [['Apple']]. Please avoid naming those competitors next time

Validating JSON output via Python

In this example, we apply the validator to a string that is a field within a Pydantic object.

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

# Initialize Validator
val = CompetitorCheck(competitors=["Apple", "Samsung"], on_fail="exception")


# Create Pydantic BaseModel
class MarketingCopy(BaseModel):
    product_name: str
    product_description: str = Field(
        description="Description about the product", validators=[val]
    )


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

# Run LLM output generating JSON through guard
try:
    guard.parse(
        """
        {
            "product_name": "Galaxy S23+",
            "product_description": "Samsung's latest flagship phone with 5G capabilities"
        }
        """
    )
except Exception as e:
    print(e)

Output:

Validation failed for field with errors: Found the following competitors: [['Samsung']]. Please avoid naming those competitors next time

API Reference

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

Initializes a new instance of the Validator class.

Parameters

• competitors (List[str]): List of names of competitors to avoid.
• 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:

1. 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.
2. 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.

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.