Max Agent - Professional PDF Summarization Tool

A modern, production-ready Python package for intelligent PDF summarization with AI-powered text processing. Designed to condense large PDF documents while preserving critical information like code blocks and equations.

Features

• Smart Text Processing: Enhanced word separation and text cleaning algorithms
• AI-Powered Summarization: Uses Facebook BART model for high-quality summaries
• Professional CLI: Easy-to-use command-line interface with max-agent command
• Flexible Configuration: YAML-based settings for complete customization
• Quality Output: Generates clean, formatted PDF summaries
• Modern Packaging: Proper Python package with pip installation support
• Fully Tested: Comprehensive test suite with type hints throughout

Quick Start

# Clone the repository
git clone https://github.com/Tennisee-data/Max_Agent.git
cd Max_Agent

# Install the package
pip install -e .

# Run with your PDFs
max-agent --help
max-agent --log-level INFO

Perfect For

• ChatGPT Agent Optimization: Condense support documents to maximize token efficiency
• Document Analysis: Quickly extract key information from large PDFs
• Research Workflows: Summarize academic papers and technical documents
• Content Preparation: Process documents for AI agent configurations

Project Structure

src/max_agent/              # Main package
├── __init__.py             # Package initialization
├── cli.py                  # Command-line interface  
├── config.py               # Configuration management
├── pdf_processor.py        # Enhanced PDF processing
├── summariser.py           # AI summarization engine
├── pdf_generator.py        # PDF output generation
├── file_tracker.py         # Processing state management
└── config.yaml             # Default configuration

tests/                      # Test suite
├── test_pdf_processor.py   # Core functionality tests
└── ...

pyproject.toml             # Modern Python packaging
requirements.txt           # Dependencies
README.md                  # This file

Configuration

Customize processing via config.yaml:

# Input/Output directories
input_directory: "./pdfs/"
output_directory: "./summarised_pdfs/"
cleaned_text_directory: "./cleaned_texts/"

# AI Model settings
summarization_model_id: "facebook/bart-large-cnn"
max_tokens: 1024

# Summarization ratios
first_min_ratio: 0.25
first_max_ratio: 0.45
second_min_ratio: 0.60
second_max_ratio: 0.80

Usage Examples

# Basic usage
max-agent

# Custom configuration
max-agent --config my_config.yaml

# Debug mode
max-agent --log-level DEBUG

# Check version
max-agent --version

ChatGPT Agent Optimization

Originally designed to overcome ChatGPT's 20 PDF / 2M token limitations by intelligently condensing source documents:

• Token Efficiency: Reduce document size while preserving key information
• Smart Processing: Handles complex document structures
• Quality Preservation: Maintains code blocks, equations, and critical details
• Optimal Format: Works best with single-column text PDFs (OpenAI recommended)

Technical Details

• Text Extraction: Advanced PDF processing with pdfplumber
• AI Model: Facebook BART-large-CNN for summarization
• Word Segmentation: Intelligent text separation with wordsegment
• Output Format: Professional PDF generation with DejaVu fonts
• Code Quality: Black formatted, type-hinted, fully tested

Requirements

• Python 3.8+
• Dependencies automatically installed via pip
• Compatible with CPU and GPU processing

Development

# Install development dependencies
pip install -e .

# Run tests
python -m pytest tests/

# Code formatting
black src/max_agent/

# Linting
flake8 src/max_agent/

How It Works

1. PDF Ingestion: Extracts text while preserving document structure
2. Text Cleaning: Advanced word separation and content cleaning
3. Smart Chunking: Divides content into optimal token-sized segments
4. AI Summarization: Uses BART model for intelligent condensation
5. Content Preservation: Maintains code blocks and equations separately
6. PDF Generation: Creates professional formatted summary documents

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

François REEVES - francois@reevesnco.com

Acknowledgments

• Built with modern Python packaging standards
• Uses Facebook's BART model for summarization
• Powered by the transformers library
• Enhanced text processing with wordsegment

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Star this repo if Max Agent helped you optimize your ChatGPT agents!