Metadata-Version: 2.1
Name: anonLLM
Version: 0.1.9
Summary: Anonymizes personally identifiable information for Large Language Model APIs
Home-page: https://github.com/fsndzomga/anonLLM
Author: FS Ndzomga
Author-email: ndzomgafs@gmail.com
Project-URL: Bug Tracker, https://github.com/fsndzomga/anonLLM/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: Faker
Requires-Dist: openai
Requires-Dist: pydantic>=2
Requires-Dist: spacy>=3
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: flake8; extra == "dev"

# anonLLM: Anonymize Personally Identifiable Information (PII) for Large Language Model APIs

![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)

anonLLM is a Python package designed to anonymize personally identifiable information (PII) in text data before it's sent to Language Model APIs like GPT-3. The goal is to protect user privacy by ensuring that sensitive data such as names, email addresses, and phone numbers are anonymized.

# Features

Anonymize names
Anonymize email addresses
Anonymize phone numbers
Support for multiple country-specific phone number formats
Reversible anonymization (de-anonymization)
Installation

To install anonLLM, run:

```bash
pip install anonLLM
```

# Quick Start

Here's how to get started with anonLLM:

```python
from anonLLM.llm import OpenaiLanguageModel
from dotenv import load_dotenv

load_dotenv()

# Anonymize a text
text = "Write a CV for me: My name is Alice Johnson, "\
    "email: alice.johnson@example.com, phone: +1 234-567-8910."\
    "I am a machine learning engineer."

# Anonymization is handled under the hood
llm = OpenaiLanguageModel()

response = llm.generate(text)

print(response)
```
In this example, the response will contain the correct name provided.
At the same time, no PII will be sent to OpenAI.

# Contributing

We welcome contributions!

# License

This project is licensed under the MIT License.
