langchain_experimental.data_anonymizer.deanonymizer_mapping.create_anonymizer_mapping¶

langchain_experimental.data_anonymizer.deanonymizer_mapping.create_anonymizer_mapping(original_text: str, analyzer_results: List[RecognizerResult], anonymizer_results: EngineResult, is_reversed: bool = False) → Dict[str, Dict[str, str]][source]¶
Create or update the mapping used to anonymize and/or

deanonymize a text.

This method exploits the results returned by the analysis and anonymization processes.

If is_reversed is True, it constructs a mapping from each original entity to its anonymized value.

If is_reversed is False, it constructs a mapping from each anonymized entity back to its original text value.

If there are multiple entities of the same type, the mapping will include a count to differentiate them. For example, if there are two names in the input text, the mapping will include NAME_1 and NAME_2.

Example of mapping: {

“PERSON”: {

“<original>”: “<anonymized>”, “John Doe”: “Slim Shady”

}, “PHONE_NUMBER”: {

“111-111-1111”: “555-555-5555”

}

Parameters

  • original_text (str) –

  • analyzer_results (List[RecognizerResult]) –

  • anonymizer_results (EngineResult) –

  • is_reversed (bool) –

Return type

Dict[str, Dict[str, str]]