| Developed by | Guardrails AI | | Date of development | Feb 15, 2024 | | Validator type | Privacy, Security | | Blog | | | License | Apache 2 | | Input/Output | Input, Output |
This validator ensures that any given text does not contain PII. This validator uses Microsoft's Presidio (https://github.com/microsoft/presidio) to detect PII in the text. If PII is detected, the validator will fail with a programmatic fix that anonymizes the text. Otherwise, the validator will pass.
- Dependencies:
- guardrails-ai>=0.4.0
- presidio-analyzer
- presidio-anonymizer
$ guardrails hub install hub://guardrails/detect_pii# Import Guard and Validator
from guardrails.hub import DetectPII
from guardrails import Guard
# Setup Guard
guard = Guard().use(
DetectPII, ["EMAIL_ADDRESS", "PHONE_NUMBER"], "exception"
)
guard.validate("Good morning!") # Validator passes
try:
guard.validate(
"If interested, apply at not_a_real_email@guardrailsai.com"
) # Validator fails
except Exception as e:
print(e)Output:
Validation failed for field with errors: The following text in your response contains PII:
If interested, apply at not_a_real_email@guardrailsai.comIn 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 DetectPII
from guardrails import Guard
# Initialize Validator
val = DetectPII(pii_entities=["EMAIL_ADDRESS", "PHONE_NUMBER"], on_fail="exception")
# Create Pydantic BaseModel
class UserHistory(BaseModel):
name: str
last_msg: str = Field(description="Last message sent by user", validators=[val])
# Create a Guard to check for valid Pydantic output
guard = Guard.from_pydantic(output_class=UserHistory)
# Run LLM output generating JSON through guard
try:
guard.parse(
"""
{
"name": "John Smith",
"last_msg": "My account isn't working. My username is not_a_real_email@guardrailsai.com"
}
"""
)
except Exception as e:
print(e)Output:
Validation failed for field with errors: The following text in your response contains PII:
My account isn't working. My username is not_a_real_email@guardrailsai.com__init__(self, pii_entities, on_fail="noop")
-
Initializes a new instance of the Validator class.
pii_entities(Union[str, List(str)]): The types of PII entities to filter out. For a full list of entities look at https://microsoft.github.io/presidio/on_fail(str, Callable): The policy to enact when a validator fails. Ifstr, must be one ofreask,fix,filter,refrain,noop,exceptionorfix_reask. Otherwise, must be a function that is called when the validator fails.
Parameters
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.
- 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 appropriatemetadatadictionary that includes keys and values required by this validator. Ifguardis associated with multiple validators, combine all necessary metadata into a single dictionary. -
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 pii_entitiesUnion[str, list(str)] The types of PII entities to filter out. For a full list of entities look at https://microsoft.github.io/presidio/. When pii_entitiesare provided inmetadata, it overrides thepii_entitiesset during validator initialization.N/A
Note:
Parameters