piigpt 0.13

Provides a mechanism to anonymize PII and PHI before sending to a LLM API

piigpt

Provides a mechanism to anonymize PII and PHI before sending to a LLM API

Basic Setup:

from Analyzers.AnalyzerType import AnalyzerType
from PIIScrubber import PIIScrubber

def main():
    from dotenv import load_dotenv
    load_dotenv("sample.env")
    pii = PIIScrubber(AnalyzerType.AZURE)
    text = "My phone number is 555-555-5555"
    print(pii.scrub([text]))
    print(pii.get_entities([text]))
    print(pii.anonymize(pii.get_entities([text]), text))
    print(pii.deanonymize(pii.anonymize(pii.get_entities([text]), text)))

if __name__ == "__main__":
    main()

Output: ['My phone number is ************'] [Text: 555-555-5555, Category: PhoneNumber, Subcategory: None, Offset: 19, Length: 12] My phone number is :yudDNDuGJG: My phone number is 555-555-5555

Extensible

Analyzers:

Currently AzureAIAnalyzer is supported. Contribute by adding additional analyzers following the BaseAnalyzer interface. Configure your parameters for your analyzer in the analyzer_config.toml file

Anonymizers:

Plug in your own anonymizer or use the built in Anonymizer. Change how the built in anonymizer anonymizes entities by changing the anonymizer_config.toml file or use the defaults.

CacheProviders:

Use the built in dictionary cache provider or insert your own CacheProvider following the BaseCache interface. Cache ensures all PII is deleted in a timely manner