Response Evaluator - Validator Details

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Response Evaluator

🏅Guardrails Certified

Last updated 2 months ago

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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 validates an LLM response based on a question provided by the user. The user-provided (rhetorical) question is expected to fact-check or ask the LLM whether the response is correct. If the LLM returns 'Yes' or 'No', the validator will pass or fail accordingly. If the LLM returns an invalid response, the validator will pass if the pass_on_inavlid flag is set to True in the metadata.

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/response_evaluator

Usage Examples

Validating string output via Python

In this example, we use the response_evaluator validator on any LLM generated text.

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

# Initialize The Guard with this validator
guard = Guard().use(
    ResponseEvaluator, llm_callable="gpt-3.5-turbo", on_fail="exception"
)

# Test passing response
guard.validate(
    "The capital of France is Paris",
    metadata={
        "validation_question": "Is Paris the capital of France?",
        "pass_on_invalid": True,
    },
)  # Pass

try:
    # Test failing response
    guard.validate(
        "The capital of France is London",
        metadata={
            "validation_question": "Is Paris the capital of France?",
        },
    )  # Fail
except Exception as e:
    print(e)

Output:

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

API Reference

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

Initializes a new instance of the Validator class.

Parameters:

llm_callable (str): The string name for the model used with LiteLLM. More info about available options here. Default is 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:

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.

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
validation_question String The question to ask the LLM N/A Yes
pass_on_invalid Boolean Whether to pass the validation if the LLM returns an invalid response False No

Key	Type	Description	Default	Required
`validation_question`	String	The question to ask the LLM	N/A	Yes
`pass_on_invalid`	Boolean	Whether to pass the validation if the LLM returns an invalid response	False	No

Guardrails API

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