🚀 Orbit AI Personal Assistant

Your Intelligent AI Companion with Memory, Conversations & Personalization

Built with Modern Full Stack Development + Generative AI Architecture

🌌 About Orbit AI

Orbit AI Personal Assistant is an intelligent AI-powered companion designed to provide personalized conversations, maintain deep user context, and evolve into a complete personal AI agent.

Unlike traditional stateless chatbots, Orbit AI focuses on:

• Conversational Intelligence: Natural, context-aware dialogues.
• User Personalization: Adapts to your preferences and style.
• Long-term Memory: Remembers important facts, goals, and technical stacks across sessions.
• Scalable Architecture: Built on a robust microservices-inspired foundation.

The Vision: To build a Jarvis-like personal AI assistant capable of deeply understanding users, autonomously managing information, and streamlining daily tasks.

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📑 Table of Contents

• Features
• System Architecture
• Tech Stack
• Project Structure
• Installation & Setup
• Environment Variables
• Roadmap & Vision
• Contributing
• License & Author

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✨ Features

🤖 AI Chat System

• Real-time AI conversations via Groq API (Llama 3.3 70B).
• Multiple persistent chat sessions.
• Rich message formatting with typing indicators and timestamps.
• Complete conversation management (Rename, Delete, Clear).

🔐 Authentication System

• Secure JWT-based registration and login.
• Support for both Email and Username identifiers.
• Persistent, protected sessions.

flowchart LR
    A([User]) -->|Register/Login| B(Backend API)
    B -->|Issues JWT| C{Protected App}
    C -->|Grants Access| D([Orbit AI Dashboard])

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👤 User Profile System

Orbit AI provides a personalized user profile management system that allows users to customize and manage their identity within the platform.

Features:

• Update personal information (Name, Username).
• Dynamic avatar management with automatic cloud storage integration.
• Secure image handling through Cloudinary.
• Real-time UI synchronization via React Context architecture (no page reload required).

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🧠 AI Memory System

Orbit AI includes an intelligent memory architecture designed to understand, store, and retrieve important user context for creating personalized AI conversations.

Memory Categories:

Category	Description
Personal	User identity and personal details
Education	Academic background and learning information
Goals	User objectives, ambitions, and future plans
Skills	Programming skills and technical abilities
Technology	Tools, frameworks, and technologies user works with
Projects	User projects and development activities
Preferences	User choices, interests, and interaction preferences

Memory Pipeline:

flowchart TD
    A[/User Message/] --> B[Memory Extractor]
    B --> C{Memory Validator}
    C -->|Valid| D[Importance Scoring]
    D --> E[Memory Categorization]
    C -->|Invalid| X((Discard))
    E --> F[(MongoDB Vector Storage)]
    F -.->|Context Retrieval| G[Personalized AI Response]

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🏗️ System Architecture

flowchart TD
    subgraph Client
        A[React Frontend<br/>Vite + Context API]
    end

    subgraph Backend Services
        B[Node.js Backend<br/>Express API + Multer]
        D[Python AI Service<br/>FastAPI + LangChain]
    end

    subgraph External Services
        C[(MongoDB Atlas)]
        E[Groq LLM<br/>Llama 3.3 70B]
        S[Cloudinary<br/>Storage]
    end

    A <-->|REST API / JWT| B
    B <-->|CRUD| C
    B -->|Uploads| S
    A <-->|Chat Prompts| D
    D <-->|Generates Output| E
    D <-->|Memory I/O| C

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🛠️ Tech Stack

Orbit AI is built using modern full-stack and AI technologies to provide a scalable, intelligent, and personalized AI assistant experience.

Domain	Technologies
🎨 Frontend	React, Vite, React Router, Context API, Bootstrap 5, Axios, CSS3
⚙️ Backend	Node.js, Express.js, JWT Authentication, Multer, Helmet, CORS
🧠 AI Service	Python, FastAPI, Groq API (Llama 3.3 70B), LangChain
🗄️ Storage	MongoDB Atlas, Mongoose, Cloudinary

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📂 Project Structure

Orbit-AI/
├── frontend/                 # React UI Client
│   ├── src/
│   │   ├── components/       # Reusable UI parts
│   │   ├── context/          # Global state
│   │   ├── pages/            # Main views
│   │   └── services/         # API integration
│   └── package.json
│
├── backend/                  # Node.js REST API
│   ├── src/
│   │   ├── controllers/      # Route logic
│   │   ├── middlewares/      # JWT, Multer
│   │   ├── models/           # Mongoose schemas
│   │   └── routes/           # API endpoints
│   └── package.json
│
└── ai_service/               # Python FastAPI Microservice
    ├── chains/               # LLM logic
    ├── memory/               # Memory extraction engine
    ├── routes/               # FastAPI routes
    └── requirements.txt

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🚀 Installation & Setup

Follow these steps to run Orbit AI locally.

📋 Prerequisites

Before starting, make sure you have installed:

• Node.js (v18+ recommended)
• npm
• Python (v3.10+ recommended)
• MongoDB Atlas account
• Cloudinary account
• Groq API Key

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1. Clone Repository

git clone [https://github.com/riturajlabs/Orbit-AI.git](https://github.com/riturajlabs/Orbit-AI.git)
cd Orbit-AI

2. 🎨 Frontend Setup

Open a terminal and navigate to the frontend directory:

cd frontend
npm install

Create a .env file in the frontend/ directory and add:

VITE_API_URL=http://localhost:5000
VITE_AI_SERVICE_URL=http://localhost:8000

Start the frontend development server:

npm run dev

Frontend runs on http://localhost:5173

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3. ⚙️ Backend Setup

Open a new terminal and navigate to the backend directory:

cd backend
npm install

Create a .env file in the backend/ directory and add:

PORT=5000
NODE_ENV=development
MONGODB_URI=your_mongodb_atlas_connection_string
JWT_SECRET=your_jwt_secret
JWT_EXPIRES_IN=7d
GROQ_API_KEY=your_groq_api_key
AI_SERVICE_URL=http://localhost:8000
CLOUDINARY_CLOUD_NAME=your_cloud_name
CLOUDINARY_API_KEY=your_cloud_api_key
CLOUDINARY_API_SECRET=your_cloud_api_secret

Start the backend server:

npm run dev

Backend runs on http://localhost:5000

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4. 🧠 AI Service Setup

Open another terminal and navigate to the AI service directory:

cd ai_service

Create and activate a virtual environment, then install dependencies:

# Create virtual environment
python -m venv venv

# Activate (Linux/macOS)
source venv/bin/activate
# Activate (Windows)
venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Create a .env file in the ai_service/ directory and add:

GROQ_API_KEY=your_groq_api_key
MONGODB_URI=your_mongodb_atlas_connection_string

Run the AI service:

python app.py

AI Service runs on http://localhost:8000

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✅ Verify Local Setup

After starting all three services, verify they are running successfully:

Service	Address / Status
Frontend	http://localhost:5173
Backend API	http://localhost:5000
AI Service	http://localhost:8000
Database	MongoDB Atlas Connected
File Storage	Cloudinary Connected

🚀 Orbit AI is now running locally!

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🛣️ Roadmap & Vision

🧪 Current Status: Orbit AI v1.0.0

Orbit AI v1.0 focuses on building a secure, personalized AI assistant foundation.

• Secure JWT Authentication
• Real-time AI Chat & Conversation History
• User Profile Management & Cloud Avatar Storage
• MongoDB Atlas Database Integration
• Core AI Memory Architecture

🚀 Upcoming Versions

🧠 Orbit AI v2.0 — Advanced Knowledge System

• RAG Pipeline & Document Uploads
• Personal Vector Knowledge Base
• Enhanced Semantic Context Retrieval

🤖 Orbit AI v3.0 — AI Agent System

• Tool Calling & Web Search Integration
• Task Automation & Calendar Synchronization
• Email Assistant Capabilities

🛸 Orbit AI v4.0 — The Jarvis Vision

• Multi-modal AI (Voice & Vision)
• Real-time Conversational Voice Interface
• Local Computer Control & Smart Home Integration

Evolution Path:

flowchart LR
    A(Chatbot) --> B(Personal Assistant)
    B --> C(AI Agent)
    C --> D{Jarvis-like AI System}
    style D fill:#23c58e,stroke:#fff,stroke-width:2px,color:#fff

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🤝 Contributing

Contributions, issues, and feature requests are welcome!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m "Add some AmazingFeature")
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📜 License

Distributed under the MIT License. See LICENSE file for more information.

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👨‍💻 Author

Ritu Raj
B.Sc Artificial Intelligence & Machine Learning Student | Full Stack Developer | AI Enthusiast

• GitHub: @riturajlabs
• Linkedin: @riturajlabs

⭐ If you like this project, consider dropping a star on the repository!
Built with ❤️ by riturajlabs