README.md

metadata

title: PDF Explainer Using RAG
emoji: 📚
colorFrom: blue
colorTo: green
sdk: docker
pinned: false
license: mit
short_description: A pdf explainer using retrieval-augmented generation (RAG)

📄 PDF Explainer Using RAG

A powerful AI-powered chatbot that allows you to upload PDF documents and ask intelligent questions about their content using Retrieval-Augmented Generation (RAG) technology.

🚀 Features

• 🤖 Smart AI Assistant: Works as a general-purpose chatbot even without uploaded documents
• 📤 PDF Upload & Processing: Upload single or multiple PDF documents with automatic text extraction
• 🎯 RAG-Powered Responses: Uses advanced embedding models to find relevant document content
• 💬 Streaming Responses: Real-time streaming chat interface for smooth conversations
• 🔄 Multiple Uploads: Add more PDFs during conversations to expand the knowledge base
• 📊 Table Support: Enhanced extraction of tables and structured content from PDFs
• 🏷️ Source Citations: Responses include filename and page number references
• 🐳 Docker Ready: Easy deployment with Docker containerization

🛠️ Technologies Used

• Frontend: Gradio - Interactive web interface
• LLM: Groq with Llama 3.1 8B Instant model
• PDF Processing: PyMuPDF4LLM - Optimized for LLM workflows
• Vector Database: ChromaDB - Efficient similarity search
• Embeddings: BGE-small-en-v1.5 - High-quality text embeddings
• Text Chunking: LangChain Text Splitters - Intelligent text segmentation

📋 Prerequisites

• Python 3.8+
• Groq API key (free at console.groq.com)

🔧 Installation

Local Setup

1. Clone the repository:

   git clone https://github.com/your-username/pdf-explainer-using-rag.git
   cd pdf-explainer-using-rag
   

2. Create virtual environment:

   python -m venv proj_env
   source proj_env/bin/activate  # On Windows: proj_env\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Set up environment variables:

   # Create .env file
   echo "GROQ_API_KEY=your_groq_api_key_here" > .env
   

5. Run the application:

   cd app
   python app.py
   

6. Access the application: Open your browser and go to http://localhost:7860

Docker Setup

1. Build the Docker image:

   docker build -t pdf-explainer .
   

2. Run the container:

   docker run -p 7860:7860 -e GROQ_API_KEY=your_groq_api_key_here pdf-explainer
   

3. Access the application: Open your browser and go to http://localhost:7860

🎯 Usage

Getting Started

1. Open the application in your web browser
2. Start chatting immediately - the AI works as a general assistant without any uploads
3. Upload PDFs (optional) using the file upload section
4. Ask questions about your documents - the AI will automatically find and use relevant content

Example Workflows

General Chat (No PDFs needed):

User: "What are the benefits of renewable energy?"
AI: [Provides general knowledge response]

Document-Specific Questions (After uploading PDFs):

User: "What is the main conclusion of the research paper?"
AI: "According to the research paper (research_paper.pdf, Page 15), 
     the main conclusion is that renewable energy adoption..."

Multi-Document Analysis:

User: "Compare the methodologies mentioned in both documents"
AI: "Comparing the methodologies:
     
     From methodology_paper.pdf (Page 3): [methodology A details]
     From comparison_study.pdf (Page 7): [methodology B details]..."

📁 Project Structure

pdf-explainer-using-rag/
├── app/
│   ├── app.py              # Main Gradio application
│   ├── llm.py              # LLM integration with RAG
│   ├── retrieval.py        # PDF processing and vector operations
├── Dockerfile              # Docker configuration
├── .dockerignore           # Docker ignore rules
├── .gitignore              # Git ignore rules
└── requirements.txt        # Python dependencies
└── README.md               # This file

⚙️ Configuration

Environment Variables

Variable	Description	Required
GROQ_API_KEY	Your Groq API key for LLM access	Yes

Customizable Parameters

In retrieval.py:

• chunk_size: Text chunk size (default: 500)
• chunk_overlap: Overlap between chunks (default: 150)
• top_k: Number of retrieved documents (default: 5)

In llm.py:

• model: Groq model name (default: "llama-3.1-8b-instant")
• temperature: Response creativity (default: 0.7)

🔍 How It Works

1. PDF Upload: Documents are parsed using PyMuPDF4LLM with markdown formatting
2. Text Processing: Content is cleaned and split into semantic chunks
3. Embedding: Text chunks are converted to vectors using BGE embeddings
4. Storage: Vectors and metadata are stored in ChromaDB
5. Retrieval: User questions trigger similarity search for relevant chunks
6. Generation: LLM generates responses using retrieved context and chat history

🚀 Deployment Options

Local Development

• Run directly with Python for development and testing

Docker Container

• Production-ready containerized deployment
• Includes pre-downloaded embedding models for faster startup

Cloud Deployment

• Compatible with any cloud platform supporting Docker
• Requires Groq API key as environment variable

🤝 Contributing

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