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| Developed by | Guardrails AI |
|---|---|
| Date of development | September 2, 2024 |
| Validator type | Format |
| Blog | |
| License | Apache 2 |
| Input/Output | Output |
This validator uses BespokeLabs.AI's minicheck API to evaluate LLM generated text against the provided context. It checks if the claims in the generated text are supported by the given context, using a configurable threshold for determining support. The validator can process the input as a whole or split it into individual sentences for more granular evaluation.
Dependencies:
Foundation model access keys:
$ guardrails hub install hub://bespokelabs/bespoke_minicheck
In this example, we apply the validator to a string output generated by an LLM.
# Import Guard and Validator
from guardrails.hub import BespokeMiniCheck
from guardrails import Guard
# Setup Guard
guard = Guard().use(
BespokeMiniCheck,
split_sentences=True,
threshold=0.5,
)
guard.validate("Alex likes cats.", metadata={"context": "Alex likes cats and dogs"}) # validation passes
guard.validate("Alex likes cats.", metadata={"context": "Alex likes dogs, but not cats."}) # validation fails
__init__(self, threshold: float = 0.5, split_sentences: bool = True, on_fail: Optional[Callable] = None)
Initializes a new instance of the BespokeMiniCheck class.
Parameters
threshold (float, optional): The minimum score for a claim to be considered supported. Defaults to 0.5.split_sentences (bool, optional): Whether to split the input into sentences for individual evaluation. Defaults to True.on_fail (Optional[Callable], optional): A callable to execute when the validation fails. Defaults to None.validate(self, value: Any, metadata: Dict = {}) -> 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:
guard.parse(...) where this method will be called internally for each associated Validator.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 |
|---|---|---|---|
threshold | float | The minimum score for a claim to be considered supported. | Value set during initialization |
split_sentences | bool | Whether to split the input into sentences for individual evaluation. | Value set during initialization |
contexts | List[str] | A list of context strings to validate claims against. | [] |
Returns
ValidationResult: A PassResult if all claims are supported, or a FailResult with an error message and a fix value containing only supported claims.Raises
ValueError: If the contexts provided are not a non-empty list of strings.