The presidio-analyzer is a Python based service which does the actual detection of PII entities.
The main modules in presidio-analyzer are the AnalyzerEngine and the RecognizerRegistry. The AnalyzerEngine is in charge of calling each requested recognizer. the RecognizerRegistry is in charge of providing the list of predefined and custom recognizers for analysis.
Code based recognizers are written in Python and are a part of the presidio-analyzer module.
The main modules in presidio-analyzer are the AnalyzerEngine and the RecognizerRegistry.
The AnalyzerEngine is in charge of calling each requested recognizer.
The RecognizerRegistry is in charge of providing the list of predefined and custom recognizers for analysis.
In order to implement a new recognizer by code, follow these two steps:
a. Implement the abstract recognizer class:
Create a new Python class which implements LocalRecognizer.
LocalRecognizer implements the base EntityRecognizer class.
All local recognizers run locally together with all other predefined recognizers as a part of the presidio-analyzer Python process. In contrast, RemoteRecognizer is an abstract class for recognizers that are external to the presidio-analyzer service, for example on a different microservice.
The EntityRecognizer abstract class requires the implementation the following methods:
i. initializing a model. Occurs when the presidio-analyzer process starts:
def load(self)ii. analyze: The main function to be called for getting entities out of the new recognizer:
def analyze(self, text, entities, nlp_artifacts):The analyze method should return a list of RecognizerResult. Refer to the code documentation for more information.
b. Reference and add the new class to the RecognizerRegistry module, in the load_predefined_recognizers method, which registers all code based recognizers.