Pytector Version 0.0.9 Release Notes

I am happy to announce the release of Pytector Version 0.0.9, the initial version for practical application and wider user. This release embodies my commitment to providing a easy-to-use solution for detecting prompt injection in text inputs, leveraging the latest advancements in machine learning and the transformers library.

Highlights of Version 0.0.9:

• First Production-Ready Release: After rigorous development and testing, Version 0.0.9 is the first release that is fully prepared for use in production environments, offering an advanced level of reliability and stability.
• Comprehensive Documentation: Documentation has been prepared to ensure a smooth user experience, covering installation, usage examples, and API references. Access the documentation here.
• Enhanced Model Support: This version introduces support for additional machine learning models including DeBERTa and DistilBERT, alongside optimizations for ONNX versions for improved performance and efficiency.
• Easy-to-use: Simple backend with only one API key from HuggingFace makes it easier than ever to integrate Pytector into your projects, customize settings, and detect potential prompt injections with confidence.

Installation:

To get started with Pytector Version 0.0.9, you can install it via pip:

pip install pytector==0.0.9

Or, clone the repository and install directly from the source:

git clone https://github.com/MaxMLang/pytector.git
cd pytector
pip install .

Getting Started:

To begin using Pytector, import the PromptInjectionDetector class and initiate it with a pre-defined or custom model. For more detailed instructions, refer to my Getting Started Guide.

import pytector

# Initialize the detector
detector = pytector.PromptInjectionDetector(model_name_or_url="deberta")

# Evaluate your text input for prompt injection
is_injection, probability = detector.detect_injection("Your text input here")
print(f"Is injection: {is_injection}, Probability: {probability}")