☕ Coffee AI — E-commerce + AI Chatbot

An AI-powered full-stack coffee shop application featuring a FastAPI backend, RAG-based intelligent chatbot, and a modern React.js frontend.

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🚀 Features

• 🛒 Product Catalog — Browse and search coffee products with filters
• 🛍️ Cart & Order Management — Add to cart, place and track orders
• 🔐 JWT Authentication — Secure user registration, login, and protected routes
• 🤖 AI Chatbot — RAG-based chatbot with vector embeddings for semantic product search and personalized recommendations
• 📱 Responsive UI — Built with React.js and Tailwind CSS

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🏗️ Project Structure

coffeeAI/
├── chatbot_rag-main/        # Python FastAPI backend + RAG chatbot
│   ├── core/                # RAG logic, embeddings, vector search
│   ├── data/                # Vector store and data files
│   ├── database/            # Database schema and service
│   ├── main.py              # FastAPI entry point
│   └── requirements.txt     # Python dependencies
├── src/                     # React.js frontend
│   ├── components/          # Reusable UI components
│   ├── pages/               # Application pages
│   ├── services/            # API service calls (Axios)
│   └── App.tsx              # Main React app
├── public/                  # Static assets
├── .gitignore
├── package.json
└── README.md

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⚙️ Setup Instructions

Prerequisites

• Python 3.8+
• Node.js v16+
• Git

1. Clone the repository

git clone https://github.com/kbsubramanyasharma/coffeeAI.git
cd coffeeAI

2. Backend Setup

cd chatbot_rag-main
pip install -r requirements.txt
python setup_database.py
python main.py

Backend runs at: http://localhost:8000 API docs available at: http://localhost:8000/docs

3. Frontend Setup

cd ..
npm install
npm run dev

Frontend runs at: http://localhost:5173

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🔌 API Endpoints

Method	Endpoint	Description
POST	/auth/register	Register new user
POST	/auth/login	Login and get JWT token
GET	/products	Get all products
GET	/products/{id}	Get product by ID
POST	/cart	Add item to cart
GET	/orders	Get user orders
POST	/orders	Place new order
POST	/chat	Chat with AI chatbot

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🧠 How the RAG Chatbot Works

1. Product data is converted into vector embeddings at setup
2. User query is embedded and compared using cosine similarity
3. Most relevant products are retrieved from the vector store
4. Retrieved context is passed to the AI model to generate a response
5. User gets a personalized, context-aware recommendation

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🛠️ Tech Stack

Layer	Technology
Backend	Python, FastAPI
AI / Chatbot	RAG, Vector Embeddings, Generative AI
Database	SQLite (migrating to MySQL)
Frontend	React.js, TypeScript, Tailwind CSS, Vite
Auth	JWT (JSON Web Tokens)
Tools	Git, Postman, VS Code

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📸 Screenshots

Coming soon — will add after deployment

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🔮 Future Improvements

• Migrate database from SQLite to MySQL
• Deploy backend on AWS EC2 with RDS
• Store product images in AWS S3
• Add payment gateway integration
• Write unit tests with pytest

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👤 Author

Subrahmanya Sharma K B

• GitHub: @kbsubramanyasharma
• LinkedIn: subrahmanyasharma-kb
• Email: subrahmanyasharmakb@gmail.com

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📄 License

This project is licensed under the MIT License.