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| Developed by | Guardrails AI |
| Date of development | Feb 15, 2024 |
| Validator type | Format |
| Blog | |
| License | Apache 2 |
| Input/Output | Output |
This validator enforces that an LLM generated output belongs to a subset of acceptable choices.
$ guardrails hub install hub://guardrails/valid_choices
In this example, we’ll use the validator to check if the output belongs to a set of choices: OpenAI, Anthropic, Cohere.
# Import Guard and Validator
from guardrails.hub import ValidChoices
from guardrails import Guard
# Use the Guard with the validator
guard = Guard().use(
ValidChoices, choices=["OpenAI", "Anthropic", "Cohere"], on_fail="exception"
)
# Test passing response
guard.validate("OpenAI")
try:
# Test failing response
guard.validate("Google")
except Exception as e:
print(e)
Output:
Validation failed for field with errors: Value Google is not in choices ['OpenAI', 'Anthropic', 'Cohere'].
We can use the same validator to confirm that a field in a JSON output belongs to a set of categories. We will use the validator to check for allowed pet types: cat, dog, bird.
# Import Guard and Validator
from pydantic import BaseModel, Field
from guardrails.hub import ValidChoices
from guardrails import Guard
val = ValidChoices(choices=["cat", "dog", "bird"], on_fail="exception")
# Create Pydantic BaseModel
class PetInfo(BaseModel):
pet_name: str
pet_type: str = Field(description="Type of pet", validators=[val])
# Create a Guard to check for valid Pydantic output
guard = Guard.from_pydantic(output_class=PetInfo)
# Run LLM output generating JSON through guard
guard.parse(
"""
{
"pet_name": "Caesar",
"pet_type": "dog"
}
"""
)
try:
# Run LLM output generating JSON through guard
guard.parse(
"""
{
"pet_name": "Caesar",
"pet_type": "fish"
}
"""
)
except Exception as e:
print(e)
Output:
Validation failed for field with errors: Value fish is not in choices ['cat', 'dog', 'bird'].
__init__(self, on_fail="noop")
Initializes a new instance of the Validator class.
Parameters
choices (List[str]): The list of acceptable choices.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:
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. No additional metadata keys are needed for this validator.The validator playground is available to authenticated users. Please log in to use it.