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| Developed by | Hyperparam |
| Date of development | Feb 15, 2024 |
| Validator type | Format |
| Blog | |
| License | Apache 2 |
| Input/Output | Output |
A CSV validator for Guardrails AI.
This validator checks for various CSV issues such as mismatched column lengths, or mismatched quote delimiters.
$ guardrails hub install hub://hyparam/csv_validator
In this example, we apply the validator to a string output generated by an LLM.
# Import Guard and Validator
from guardrails.hub import CsvMatch
from guardrails import Guard
# Setup Guard
guard = Guard().use(
CsvMatch
)
guard.validate("name,email\njohn,john@example.com\njane,jane@example.com") # Validator passes
guard.validate("name,email\njohn\njane,jane@example.com") # Validator fails
In this example, we apply the validator to a string field of a JSON output generated by an LLM.
# Import Guard and Validator
from pydantic import BaseModel, Field
from guardrails.hub import CsvMatch
from guardrails import Guard
# Initialize Validator
val = CsvMatch()
# Create Pydantic BaseModel
class DbBackup(BaseModel):
db_name: str
data: str = Field(validators=[val])
# Create a Guard to check for valid Pydantic output
guard = Guard.from_pydantic(output_class=DbBackup)
# Run LLM output generating JSON through guard
guard.parse("""
{
"db_name": "USERS",
"data": "name,email\njohn,john@example.com\njane,jane@example.com"
}
""")
__init__(self, on_fail="noop")
Initializes a new instance of the CsvMatch class.
Parameters
delimiter (str): String delimiter for csv. Defaults to ,.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:
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.