CELLA_AI_2.0

RAG Based Document Chatbot 🤖📚

A modern, AI-powered document chatbot that lets you upload documents and chat with them using advanced RAG (Retrieval-Augmented Generation) technology. Built with React and FastAPI.

✨ Features

• 📤 Drag & Drop Upload - Easily upload PDF, DOCX, TXT, XLSX, and CSV files
• 💬 AI-Powered Chat - Ask questions about your documents in natural language
• 📚 Source Citations - See exactly where answers come from with relevance scores
• 🎨 Beautiful UI - Modern glassmorphism design with smooth animations
• ⚡ Fast Responses - Powered by Groq's LLaMA 3.1 model
• 🔒 Secure - Vector embeddings stored in Pinecone

🎥 Demo

Upload any document and start asking questions! The AI will provide detailed answers with source citations showing exactly where the information came from.

🛠️ Tech Stack

Frontend

• React 19 - Modern UI framework
• CSS3 - Glassmorphism design with custom animations
• Fetch API - RESTful backend communication

Backend

• FastAPI - High-performance Python web framework
• LangChain - RAG orchestration and document processing
• Pinecone - Vector database for semantic search
• Groq - Fast LLM inference (LLaMA 3.1 8B)
• HuggingFace - Sentence embeddings (all-MiniLM-L6-v2)

📁 Project Structure

CELLA_AI_2.0/
├── backend/
│   ├── routes/
│   │   ├── upload.py      # Document upload & processing
│   │   └── chat.py        # Chat & query handling
│   ├── service/
│   │   └── utils.py       # RAG services (chunking, embeddings, vector store)
│   └── main.py            # FastAPI application
├── frontend-new/
│   ├── src/
│   │   ├── components/    # React components
│   │   │   ├── DocumentUpload.js
│   │   │   ├── ChatInterface.js
│   │   │   └── MessageBubble.js
│   │   ├── App.js         # Main application
│   │   └── index.css      # Design system
│   └── public/
└── requirements.txt

🚀 Quick Start

Prerequisites

• Python 3.10+
• Node.js 16+
• Pinecone API Key (Get one here)
• Groq API Key (Get one here)

Installation

1. Clone the repository

git clone <your-repo-url>
cd CELLA_AI_2.0

2. Set up Backend

# Create and activate virtual environment
python -m venv .venv
.venv\Scripts\activate  # Windows
# source .venv/bin/activate  # Linux/Mac

# Install dependencies
pip install -r requirements.txt

3. Configure Environment Variables

Create a .env file in the backend folder:

GROQ_API_KEY=your_groq_api_key_here
PINECONE_API_KEY=your_pinecone_api_key_here

4. Set up Frontend

cd frontend-new
npm install

Running the Application

Terminal 1 - Start Backend:

.venv\Scripts\activate
python backend\main.py

Backend runs on: http://localhost:8000

Terminal 2 - Start Frontend:

cd frontend-new
npm start

Frontend runs on: http://localhost:3000

📖 How It Works

1. Document Upload - Files are processed and split into chunks
2. Embedding - Chunks are converted to vector embeddings
3. Storage - Vectors are stored in Pinecone for fast retrieval
4. Query - User questions are embedded and matched with relevant chunks
5. Generation - LLM generates answers based on retrieved context
6. Citation - Sources are displayed with relevance scores

🎨 UI Features

• Glassmorphism Design - Frosted glass effect with backdrop blur
• Smooth Animations - Fade-in effects and hover transitions
• Responsive Layout - Works on desktop, tablet, and mobile
• Real-time Feedback - Upload progress and typing indicators
• Source Expansion - Click to view full source content and metadata

🔐 Environment Variables

Variable	Description	Required
GROQ_API_KEY	Groq API key for LLM inference	Yes
PINECONE_API_KEY	Pinecone vector database key	Yes

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

📄 License

This project is open source and available under the MIT License.

🙏 Acknowledgments

• Built with LangChain
• Powered by Groq and Pinecone
• UI design inspired by modern glassmorphism trends
• Learned from Sunny Savita YouTube Channel⭐

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Made with ❤️ using React and FastAPI