Quanta - AI-Powered Customer Support Chatbot

Quanta is an advanced AI-powered customer support chatbot designed to provide seamless and responsive support 24/7. Built with cutting-edge technologies like Next.js, Firebase, LangChain, Tailwind CSS, AWS Amplify, TypeScript, and React, Quanta ensures secure and personalized user experiences while delivering contextually relevant responses.

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🚀 Features

• 🔐 User Authentication: Secure and personalized user access.
• 💬 Advanced Language Understanding: Powered by LangChain for smooth and intuitive interactions.
• 🕒 24/7 Availability: Always ready to assist with instant support.
• 🖥️ User-Friendly Interface: Easy integration into existing support channels.
• 💡 Feedback System: Collects user feedback after every 5 interactions, and then after every 30 responses.

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🛠️ Tech Stack

• Frontend: Next.js, React, Tailwind CSS, TypeScript
• Backend: Firebase, AWS Amplify
• AI: LangChain
• Deployment: AWS Amplify, Vercel

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📦 Installation

To get started with Quanta locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/quanta.git
   cd quanta

2. Install dependencies:

   npm install

3. Set up environment variables: Create a .env.local file in the root directory and add your Firebase, AWS, and other necessary API keys.

4. Run the development server:

   npm run dev

5. Open your browser: Visit http://localhost:3000 to view the application.

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🎯 Usage

After setting up and running the development server, you can:

• Sign Up/Sign In to experience the authentication system.
• Interact with Quanta: Test the chatbot’s response to various queries.
• Provide Feedback: Use the feedback system to help us improve the service.

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🗂️ Project Structure

├── /api               # Backend API routes
├── /components        # Reusable React components
├── /pages             # Next.js pages
├── /public            # Static assets
├── /styles            # Tailwind CSS styles
├── /utils             # Utility functions
└── /vercel.json       # Vercel deployment configuration

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🌟 Future Plans

In upcoming updates, we plan to integrate Retrieval-Augmented Generation (RAG) to enhance Quanta’s capabilities, enabling even more accurate and context-aware responses. Stay tuned for these exciting new features!

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🤝 Contributing

We welcome contributions! Please follow these steps if you wish to contribute:

1. Fork the repository.
2. Create a new branch with your feature or bug fix:

   git checkout -b feature/your-feature-name

3. Commit your changes:

   git commit -m "Add new feature"

4. Push to your branch:

   git push origin feature/your-feature-name

5. Submit a pull request.

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📜 License

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