AlphaExtract - AI-powered PDF Summarizer

AlphaExtract is a cutting-edge PDF summarization tool that leverages state-of-the-art AI models to extract and synthesize information from PDF documents. Built with Meta's LLaMA 4 MOE Maverick model and powered by Groq's inference engine, it provides blazing-fast, high-precision summaries for any PDF document.

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Features

• Intelligent PDF Processing: Convert PDFs to images and extract detailed information
• Advanced Summarization: Generate comprehensive, well-structured summaries using LLaMA 4 MOE Maverick
• Professional PDF Export: Download summaries as beautifully formatted PDF documents
• Modern Web Interface: Clean, responsive UI built with Streamlit
• Parallel Processing: Multi-threaded extraction for improved performance
• Docker Support: Easy deployment with containerization
• CI/CD Integration: Automated Docker image builds and pushes

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Table of Contents

1. Architecture
2. Technical Stack
3. Requirements
4. Installation
5. Usage
6. Docker Deployment
7. Project Structure
8. Screenshots

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Architecture

AlphaExtract follows a pipeline architecture with three main components:

1. PDF Processing: Converts PDF documents to images for processing
2. Detail Extraction: Uses LLaMA 4 MOE Maverick to extract detailed information from each page
3. Summary Generation: Synthesizes extracted information into a coherent, analytical summary

The pipeline is optimized for parallel processing and handles documents of varying lengths efficiently.

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Technical Stack

• Language: Python 3.10
• Web Framework: Streamlit
• AI Models: Meta's LLaMA 4 MOE Maverick
• Inference Engine: Groq
• PDF Processing: pdf2image, ReportLab
• Package Management: uv
• Containerization: Docker
• CI/CD: GitHub Actions

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Requirements

• Python 3.10 or higher
• Dependencies listed in pyproject.toml
• Groq API key for inference
• Poppler utils for PDF processing

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Installation

1. Clone the repository:

   git clone https://github.com/yourusername/AlphaExtract.git
   cd AlphaExtract

2. Install dependencies using uv:

   curl -LsSf https://astral.sh/uv/install.sh | sh
   uv sync

3. Set up environment variables:

   export GROQ_API_KEY=your_api_key_here

4. Run the application:

   streamlit run main.py

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Usage

1. Access the web interface at http://localhost:7860
2. Upload your PDF document using the sidebar
3. Wait for the processing to complete
4. View the generated summary
5. Download the summary as a PDF document

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Docker Deployment

1. Build the Docker image:

   docker build -t alphaextract .

2. Run the container:

   docker run -p 7860:7860 -e GROQ_API_KEY=your_api_key_here alphaextract

The application will be available at http://localhost:7860.

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Project Structure

AlphaExtract/
├── .github/
│   └── workflows/
│       └── dockerhubPush.yaml
├── src/
│   ├── components/
│   │   ├── extractPdfDetails.py
│   │   └── summaryEngine.py
│   ├── pipelines/
│   │   └── pipeline.py
│   └── utils/
│       ├── functions.py
│       └── logger.py
├── config.ini
├── Dockerfile
├── main.py
├── prompts.yaml
└── pyproject.toml

Key Components

• main.py: Streamlit web application entry point
• src/components/: Core processing modules
• src/pipelines/: Pipeline orchestration
• config.ini: Configuration settings
• prompts.yaml: LLM system prompts
• Dockerfile: Container configuration
• .github/workflows/: CI/CD configuration

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Screenshots

• Project Demo Complete demonstration of PDF upload, processing, and summary generation

• Application Interface The clean and intuitive application interface

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License

This project is licensed under the MIT License.

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Author

Created with ❤️ by Rauhan Ahmed Siddiqui.

For questions or support, please open an issue on the GitHub repository.