🤖 MongoDB AI Hub

A specialized data management system for AI applications with secure authentication and auto-launch capabilities for cloud environments.

🚀 Auto-Launch in Cloud Environments

This repository is pre-configured to automatically start in all major cloud development environments:

• ✅ GitHub Codespaces - Auto-starts on workspace open
• ✅ GitPod - Launches when workspace loads
• ✅ Replit - Starts when repl opens
• ✅ VS Code - Can auto-start on folder open

No more forgetting to launch your AI Hub! 🎉

Quick Health Check

curl http://localhost:3000/api/health
# Response: {"status":"ok","message":"MongoDB AI Data Hub API is running"}

📋 Features

🔐 Secure Authentication System

• JWT-based user authentication
• Password hashing with bcrypt
• Role-based access control (user/admin)
• Rate limiting and input validation
• Session management

🤖 AI Prompt Management

• Store and organize AI prompts
• Categorize by use case (coding, analysis, etc.)
• Tag and search functionality
• Model-specific prompts (GPT-4, Claude, etc.)
• Version control and metadata

🧠 Vector Store Management

• Store vector embeddings for semantic search
• Multiple vector stores with namespaces
• Support for different embedding dimensions
• Metadata and search capabilities
• Integration with AI/ML pipelines

🌐 Cloud-Native Features

• Auto-launch in cloud environments
• Environment detection and configuration
• Health monitoring and status checks
• Container and serverless ready

🔧 Quick Start

Cloud Environments (Auto-Start)

1. Open in any cloud environment (Codespaces, GitPod, Replit)
2. Server starts automatically - no action needed!
3. Access at: Check port 3000 forwarding in your environment

Local Development

# Clone and setup
git clone https://github.com/d33disc/mongodb-ai-hub.git
cd mongodb-ai-hub
npm install

# Start the server
npm run dev

# Or use auto-start script
npm run auto-start

📡 API Endpoints

Authentication

POST /api/auth/register    # Create user account
POST /api/auth/login       # User login
GET  /api/auth/profile     # Get user profile
PUT  /api/auth/profile     # Update profile
PUT  /api/auth/change-password  # Change password
GET  /api/auth/verify      # Verify token

AI Prompts

GET    /api/prompts        # List prompts (public)
POST   /api/prompts        # Create prompt (auth required)
GET    /api/prompts/:id    # Get specific prompt
PUT    /api/prompts/:id    # Update prompt (auth required)
DELETE /api/prompts/:id    # Delete prompt (auth required)
GET    /api/prompts/search/text?query=  # Search prompts

Vector Stores

GET    /api/vectorstores   # List vector stores
POST   /api/vectorstores   # Create store (auth required)
GET    /api/vectorstores/:id  # Get specific store
PUT    /api/vectorstores/:id  # Update store (auth required)
DELETE /api/vectorstores/:id  # Delete store (auth required)
POST   /api/vectorstores/:id/embeddings     # Add embeddings
DELETE /api/vectorstores/:id/embeddings/:embeddingId  # Remove embedding

🔑 Authentication Usage

Register User

curl -X POST http://localhost:3000/api/auth/register \
  -H "Content-Type: application/json" \
  -d '{
    "email": "user@example.com",
    "password": "SecurePass123!",
    "firstName": "John",
    "lastName": "Doe"
  }'

Login & Get Token

curl -X POST http://localhost:3000/api/auth/login \
  -H "Content-Type: application/json" \
  -d '{
    "email": "user@example.com", 
    "password": "SecurePass123!"
  }'

Use Token for API Calls

curl -X POST http://localhost:3000/api/prompts \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_JWT_TOKEN" \
  -d '{
    "title": "Code Review Prompt",
    "content": "Review this code for bugs and improvements:",
    "category": "coding",
    "model": "gpt-4"
  }'

🛠 Integration Examples

Python Integration

import requests

# Login and get token
response = requests.post('http://localhost:3000/api/auth/login', json={
    'email': 'user@example.com',
    'password': 'password'
})
token = response.json()['data']['tokens']['accessToken']

# Create a prompt
requests.post('http://localhost:3000/api/prompts', 
    headers={'Authorization': f'Bearer {token}'},
    json={
        'title': 'Data Analysis Prompt',
        'content': 'Analyze this dataset and provide insights:',
        'category': 'analysis'
    })

JavaScript/Node.js

const axios = require('axios');

// Login
const loginResponse = await axios.post('http://localhost:3000/api/auth/login', {
    email: 'user@example.com',
    password: 'password'
});

const token = loginResponse.data.data.tokens.accessToken;

// Create vector store
await axios.post('http://localhost:3000/api/vectorstores', {
    name: 'Product Embeddings',
    namespace: 'ecommerce', 
    vectorDimension: 1536,
    model: 'text-embedding-ada-002'
}, {
    headers: { Authorization: `Bearer ${token}` }
});

🌍 Cloud Environments

GitHub Codespaces

• Auto-starts: ✅ Enabled in .devcontainer/devcontainer.json
• MongoDB: Auto-configured
• Access: Port forwarding automatically configured

GitPod

• Auto-starts: ✅ Enabled in .gitpod.yml
• MongoDB: Local instance setup
• Access: https://3000-[workspace].gitpod.io

Replit

• Auto-starts: ✅ Enabled in .replit
• MongoDB: Configured for Replit environment
• Access: Repl web view

VS Code (Local/Remote)

• Auto-starts: ✅ Optional via tasks
• MongoDB: Local or remote connection
• Access: http://localhost:3000

📊 Monitoring & Status

Check Server Status

# Health check
npm run health-check

# Full status
npm run status

# View logs  
tail -f /tmp/ai-hub.log

Environment URLs

Each environment will show the appropriate URL for access. Look for port 3000 forwarding in your cloud environment.

🔧 Configuration

Environment Variables

# Required
MONGODB_URI=mongodb://localhost:27017/mongodb-ai-hub
JWT_SECRET=your-secret-key
JWT_REFRESH_SECRET=your-refresh-secret

# Optional  
PORT=3000
NODE_ENV=development
CORS_ORIGIN=http://localhost:3000

Auto-Start Configuration

The auto-start feature is configured in:

• .devcontainer/devcontainer.json - GitHub Codespaces
• .gitpod.yml - GitPod
• .replit - Replit
• .vscode/tasks.json - VS Code

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test in a cloud environment
5. Submit a pull request

📝 License

MIT License - see LICENSE file for details.

🆘 Support

• Documentation: cloud-deploy.md
• Issues: GitHub Issues
• Discussions: GitHub Discussions

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Ready to use in any cloud environment with automatic startup! 🚀