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.