AuraAI 🌌

All-in-one fullstack AI application — your intelligent companion at the intersection of front-end, back-end, and brainy models.

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🚀 Table of Contents

1. About
2. Features
3. Architecture
4. Getting Started
   • Prerequisites
   • Installation
   • Running Locally
5. Environment Variables / Configuration
6. Usage
7. Folder Structure
8. Roadmap & Future Plans
9. Contributing
10. License
11. Acknowledgements

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🧠 About

AuraAI is a fullstack platform built to unify front-end UI, back-end services, and AI capabilities (e.g. LLMs, embeddings, agents). It’s designed as a modular, extensible foundation so you can plug & play AI features while maintaining sanity in your codebase.

Live demo / deployment: aura-ai-server-one.vercel.app (if maintained) :contentReference[oaicite:0]{index=0}

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✨ Features

Here’s what AuraAI aims to offer (or already offers):

• Modular architecture: front end + back end + AI models all in one
• API routing, authentication, user session logic
• Integration with language models (OpenAI, etc.)
• Embedding store / vector DB (optional)
• Agent / tool framework support
• Logging, error handling, monitoring hooks
• Real-time / websocket support (future)
• Extensibility: plugin your own modules or models

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🏗 Architecture

[ Client / Frontend ]  ↔  [ Server / API Layer ]  ↔  [ AI / Model / DB Layer ]

• Client (in client/) — UI, state management, request orchestration
• Server (in server/) — API endpoints, orchestration, model calls, business logic
• AI / Model Layer — embedding store, calling LLMs, agent logic, etc.

You can swap or upgrade any part without rewriting the rest.

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🛠 Getting Started

Prerequisites

Make sure you have:

• Node.js (>= 16.x or your chosen version)
• npm or yarn
• (Optional) A cloud hosting / deployment setup
• API keys for LLM / AI provider (e.g. OpenAI)
• (If using embeddings vector DB) credentials or local instance

Installation

# Clone the repo
git clone https://github.com/SP4567/AuraAI.git
cd AuraAI

# Install dependencies in both server & client
cd server && npm install
cd ../client && npm install

Running Locally

Open two terminal tabs/windows:

# In server folder
cd server
npm run dev   # or whatever your start script is

# In client folder
cd client
npm run dev

Then open your browser to http://localhost:5000 (or your configured port).

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⚙ Environment Variables / Configuration

Create a .env (or .env.local) file (in server and/or client as needed). Example variables:

In server/.env

OPENAI_API_KEY=your_openai_key MODEL_PROVIDER_URL=... DB_URL=... EMBEDDING_KEY=...

In client/.env

NEXT_PUBLIC_API_ENDPOINT=http://localhost:3000/api

Adjust according to your setup.

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🎯 Usage

Once up and running, you can:

• Authenticate / login (if built)
• Send prompts / queries to the AI
• Use embedding-based retrieval & reasoning
• Extend with new tools or modules
• Monitor logs, errors, performance
• Deploy to production (Vercel, AWS, etc.)

You might want to include screenshots or GIFs here in future.

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📁 Folder Structure

Here’s a sample (adjust to your actual) layout:

AuraAI/ ├── client/ # Frontend app (React / Next.js / etc.) │ ├── pages/ │ ├── components/ │ └── ... ├── server/ # Backend / API / business logic │ ├── api/ │ ├── services/ │ ├── models/ │ └── ... ├── README.md
└── .gitignore

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🚧 Roadmap & Future Plans

• 🔧 Add WebSocket / real-time features
• 🧩 Plugin system (extension marketplace)
• 🔐 Full auth & user permissions
• 📊 Dashboard / analytics
• ☁️ One-click deploy / CI/CD
• 🧠 Fine-tuning & model switching support

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🤝 Contributing

You’re welcome to contribute — code, docs, tests, ideas. Here’s how:

1. Fork the repo
2. Create a branch: feature/your-thing
3. Make changes & add tests
4. Submit a pull request with a clear description
5. We’ll review, discuss, iterate

Please abide by the repository’s code style, tests, and commit conventions.

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📜 License

This project is under the MIT License (or whichever you prefer). See LICENSE file.

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🙏 Acknowledgements

• Inspiration / code snippets from various open-source AI fullstack projects
• Thanks to the open source community & model providers