Overview

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

Description

Intended Use

This validator ensures that a generated output responds to the given prompt.

Requirements

• Dependencies:

  • litellm
  • guardrails-ai>=0.4.0

• API keys: Set your LLM provider API key as an environment variable which will be used by litellm to authenticate with the LLM provider. For more information on supported LLM providers and how to set up the API key, refer to the LiteLLM documentation.

Installation

$ guardrails hub install hub://guardrails/responsiveness_check

Usage Examples

Validating string output via Python

In this example, we’ll test that a generated output actually answers the question posed in the prompt.

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

# Setup Guard
guard = Guard().use(
    ResponsivenessCheck,
    llm_callable="gpt-3.5-turbo",
    on_fail="exception",
)

res = guard.validate(
    "Jefferson City is the capital of Missouri.", 
    metadata={
        "original_prompt": "What is the capital of Missouri?",
        "pass_on_invalid": True
    }
)  # Validation passes
try:
    res = guard.validate(
        "Berlin is the capital of Germany.",
        metadata={
            "original_prompt": "What is the capital of Missouri?",
        }
    )  # Validation fails because this response isn't related to what we asked.
except Exception as e:
    print(e)

Output:

Validation failed for field with errors: The LLM says 'No'. The validation failed.

API Reference

__init__(self, prompt, llm_callable='gpt-3.5-turbo', on_fail="noop")

Initializes a new instance of the Validator class.

Parameters:

• prompt (str): The original prompt to the LLM.
• llm_callable (str): Model name to make the LiteLLM call. Defaults to gpt-3.5-turbo.
• 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:

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. Keys and values must match the expectations of this validator.

  Key	Type	Description	Default	Required
  original_prompt	String	The original prompt to the LLM	-	Yes
  pass_on_invalid	Boolean	Whether to pass the validation if the LLM returns an invalid response	False	No