Source code for langchain_experimental.data_anonymizer.base
from abc import ABC, abstractmethod
from typing import Callable, List, Optional
from langchain_experimental.data_anonymizer.deanonymizer_mapping import MappingDataType
from langchain_experimental.data_anonymizer.deanonymizer_matching_strategies import (
    exact_matching_strategy,
)
DEFAULT_DEANONYMIZER_MATCHING_STRATEGY = exact_matching_strategy
[docs]class AnonymizerBase(ABC):
    """
    Base abstract class for anonymizers.
    It is public and non-virtual because it allows
        wrapping the behavior for all methods in a base class.
    """
[docs]    def anonymize(
        self,
        text: str,
        language: Optional[str] = None,
        allow_list: Optional[List[str]] = None,
    ) -> str:
        """Anonymize text"""
        return self._anonymize(text, language, allow_list)
    @abstractmethod
    def _anonymize(
        self, text: str, language: Optional[str], allow_list: Optional[List[str]] = None
    ) -> str:
        """Abstract method to anonymize text"""
[docs]class ReversibleAnonymizerBase(AnonymizerBase):
    """
    Base abstract class for reversible anonymizers.
    """
[docs]    def deanonymize(
        self,
        text_to_deanonymize: str,
        deanonymizer_matching_strategy: Callable[
            [str, MappingDataType], str
        ] = DEFAULT_DEANONYMIZER_MATCHING_STRATEGY,
    ) -> str:
        """Deanonymize text"""
        return self._deanonymize(text_to_deanonymize, deanonymizer_matching_strategy)
    @abstractmethod