README_FOR_HF.md

Physical AI & Humanoid Robotics Book

This is an AI-powered technical publication about Physical AI and Humanoid Robotics, featuring a RAG-based chatbot that answers questions using book content.

About This Project

This project combines a Docusaurus-based book platform with AI capabilities, featuring:

• Interactive book platform built with Docusaurus
• RAG-based chatbot that answers questions using book content
• Robotic/AI themed UI with dark mode and modern styling
• User authentication system for personalized experiences
• Support for multiple languages (English and Urdu)

Architecture

The application consists of:

• Frontend: Docusaurus-based static site deployed on Hugging Face Spaces
• Backend: FastAPI server with RAG capabilities
• AI Services: Google Gemini for response generation
• Embeddings: Qwen or Hugging Face models for content indexing
• Vector Database: Qdrant for similarity search
• Database: SQLite for user data (PostgreSQL in production)

Deployment on Hugging Face Spaces

This project is designed to run on Hugging Face Spaces using Docker. The application exposes a FastAPI backend that serves AI-powered features.

Environment Variables Required

To run this application on Hugging Face Spaces, you need to configure the following environment variables:

• GEMINI_API_KEY: Your Google Gemini API key
• QWEN_API_KEY: Your Qwen API key (optional, for embeddings)
• HF_TOKEN: Your Hugging Face API token (for model access)
• SECRET_KEY: A secret key for JWT tokens
• TRANSLATION_API_KEY: Google Translate API key (optional)

Local Setup

If you want to run this locally:

1. Clone this repository
2. Install backend dependencies:

   cd backend
   pip install -r requirements.txt
   

3. Set up environment variables in a .env file
4. Run the backend:

   cd backend
   uvicorn main:app --reload
   

5. In another terminal, install and run the frontend:

   npm install
   npm start
   

Features

• AI Chatbot: Ask questions about Physical AI and Humanoid Robotics and get answers based on book content
• Content Search: Search through the book content with semantic search
• Multilingual Support: Content available in English and Urdu
• User Authentication: Personalized experience with account creation
• Responsive Design: Works on desktop and mobile devices

Technology Stack

Backend

• FastAPI: Modern Python web framework
• SQLAlchemy: Python SQL toolkit and ORM
• Google Generative AI SDK: For Gemini integration
• Qwen: For embedding generation
• QDrant: Vector database for similarity search

Frontend

• Docusaurus: Static site generator for documentation
• React: Component-based UI library
• MDX: JSX in Markdown files

Contributing

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

License

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