🤖 AI RAG Chatbot with Modern UI

A sophisticated Retrieval Augmented Generation (RAG) chatbot built with Streamlit, LangChain, and OpenAI GPT-4. Features a modern, intuitive interface for document-based conversations.

✨ Features

• Modern UI Design: Clean, centered interface with sparkle animations and suggestion cards
• Multi-PDF Support: Upload and chat with multiple PDF documents simultaneously
• Smart Document Retrieval: FAISS vector search for accurate context retrieval
• Streaming Responses: Real-time GPT-4 responses with typing indicators
• Session Management: Persistent conversation history within sessions
• Responsive Design: Works seamlessly across different screen sizes
• Source Attribution: Responses include page numbers and filenames for verification

🏗️ Architecture

Core Components

• app.py: Streamlit frontend with modern UI and chat interface
• brain.py: RAG processing engine (PDF parsing, chunking, vector indexing)
• .env: Environment configuration (OpenAI API key)

Technology Stack

• Frontend: Streamlit with custom CSS styling
• Backend: Python with LangChain framework
• Vector Store: FAISS (Facebook AI Similarity Search)
• Embeddings: OpenAI text-embedding models
• LLM: OpenAI GPT-4 with streaming
• PDF Processing: PyPDF for text extraction

🚀 Quick Start

Prerequisites

• Python 3.8+
• OpenAI API key

Installation

1. Clone the repository

   git clone https://github.com/krishn1122/AI-Chatbot-RAG.git
   cd AI-Chatbot-RAG

2. Create virtual environment

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

3. Install dependencies

   pip install -r requirements.txt

4. Set up environment variables

   # Create .env file and add your OpenAI API key
   echo "OPENAI_API_KEY=your_api_key_here" > .env

5. Run the application

   streamlit run app.py

💡 How It Works

RAG Pipeline

1. Document Upload: Users upload PDF files through the web interface
2. Text Extraction: PyPDF extracts and cleans text content
3. Document Chunking: Text is split into 4000-character chunks with metadata
4. Vector Embedding: OpenAI creates vector representations of text chunks
5. Index Creation: FAISS builds a searchable vector database
6. Query Processing: User questions trigger similarity search (top-3 results)
7. Context Augmentation: Retrieved chunks are added to the GPT-4 prompt
8. Response Generation: GPT-4 generates contextual responses with source attribution

User Experience Flow

1. Landing Page: Modern interface with suggestion cards and PDF upload
2. Document Processing: Automatic vector database creation with progress indicators
3. Interactive Chat: Real-time conversations with document-aware responses
4. Source Tracking: All responses include page numbers and filenames

🎯 Usage Examples

Example Queries

• "What are the main topics discussed in the uploaded documents?"
• "Summarize the key findings from page 5 of document.pdf"
• "Compare the methodologies mentioned across different documents"
• "What recommendations are provided in the conclusion?"

🔧 Configuration

Environment Variables

Create a .env file in the root directory:

OPENAI_API_KEY=your_openai_api_key_here

Customization Options

• Chunk Size: Modify chunk_size in brain.py (default: 4000 characters)
• Similarity Search: Adjust k parameter in app.py (default: 3 results)
• Model Selection: Change GPT model in app.py (default: gpt-4)

📁 Project Structure

AI-Chatbot-RAG/
├── app.py              # Main Streamlit application
├── brain.py            # RAG processing logic
├── requirements.txt    # Python dependencies
├── .env               # Environment variables
├── .gitignore         # Git ignore rules
├── README.md          # Project documentation
├── thumbnail.webp     # Project thumbnail
└── venv/              # Virtual environment

🤝 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

📄 License

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

🙏 Acknowledgments

• LangChain for the RAG framework
• Streamlit for the web interface
• OpenAI for GPT-4 and embeddings
• FAISS for vector similarity search