@alanhelmick/memorable

MemoRable 🧠

An advanced AI memory system enabling personalized, context-aware interactions through sophisticated memory management and emotional intelligence. Experience it live at mindfulmoments.io - your companion for mindfulness and personal growth through AI-powered emotional mirroring.

🌟 Features

• TaskHopper System

  • Intelligent task management and prioritization
  • Step-by-step progress tracking
  • AI task integration and automation
  • Task relationship mapping
  • Automated task archival

• Multi-modal Input Processing

  • Text, vision, audio, and video processing
  • AI response handling
  • File management
  • Extensible sensor framework

• Night Processing Intelligence

  • Automated pattern analysis (1 AM - 4 AM)
  • Model performance optimization
  • Cache warming strategies
  • Memory usage predictions
  • Task pattern analysis

• Contextual Indexing

  • Environmental data tracking
  • Temporal awareness
  • Task context management
  • Conversation history
  • Geospatial integration

• Advanced Emotional Intelligence

  • 83 distinct emotional vectors including:
    • Core emotions (joy, sadness, anger, etc.)
    • Complex emotions (nostalgia, contemplation, aesthetic appreciation)
    • Social emotions (empathic pain, adoration, triumph)
    • Cognitive states (concentration, confusion, realization)
  • Multi-modal emotion detection
  • Cross-referenced emotional context
  • Real-time emotional state analysis
  • Color-coded emotional visualization

• Three-tier Memory Architecture

  • Raw data storage (MongoDB)
  • Vector embeddings (Weaviate)
  • Active memory buffer (Redis)

• Custom Model Training

  • Personalized emotional pattern recognition
  • User-specific interaction learning
  • Adaptive response calibration
  • Continuous model improvement
  • Fine-tuning capabilities for:
    • Emotional recognition accuracy
    • Personal interaction style
    • Context sensitivity
    • Response generation

🏗️ Architecture

graph TD
    A[Multi-modal Input] --> B[Input Processor]
    B --> C[Contextual Indexer]
    B --> D[Emotional Processor]
    C --> E[Memory Manager]
    D --> E
    E --> F[MongoDB]
    E --> G[Weaviate]
    E --> H[Redis]
    E --> I[Attention System]
    I --> J[Predictive Behavior]

🛠️ Tech Stack

• Node.js/NPM
• MongoDB (time series)
• Weaviate (vector database)
• Redis (active memory)
• Docker
• Ollama (AI models)
• TensorFlow.js
• Hume.ai (emotion analysis)
• Custom embedding solutions

📋 Prerequisites

• Node.js >= 18.0.0
• Docker and Docker Compose
• MongoDB
• Redis
• Weaviate
• Ollama
• Hume.ai API key

🚀 Quick Start

1. Clone the repository

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

2. Install dependencies

npm install

3. Set up environment variables

cp .env.example .env
# Edit .env with your configuration

4. Start the services

npm run docker:up

5. Run the application

npm start

💻 Development

1. Start in development mode

npm run dev

2. Run tests

npm test

3. Lint code

npm run lint

🏛️ Project Structure

memorable/
├── src/
│   ├── config/           # Configuration files
│   ├── core/             # Core system components
│   ├── models/           # Data models
│   ├── services/         # Business logic
│   ├── utils/            # Utility functions
│   └── index.js          # Application entry point
├── tests/                # Test files
├── docker/               # Docker configuration
├── docs/                 # Documentation
└── scripts/             # Utility scripts

🔧 Configuration

The system can be configured through environment variables:

• MONGODB_URI: MongoDB connection string
• REDIS_URL: Redis connection URL
• WEAVIATE_URL: Weaviate instance URL
• OLLAMA_API_KEY: Ollama API key
• HUME_API_KEY: Hume.ai API key
• PORT: Application port (default: 3000)

📖 Documentation

Detailed documentation is available in the docs directory:

• Architecture Overview
• API Reference
• Development Guide
• Deployment Guide
• Emotion Processing Guide
• Custom Model Training

🤝 Contributing

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

📄 License

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

🌟 Try it Live

Experience MemoRable in action at mindfulmoments.io - a mindfulness and mirroring application that helps you understand how AI and the world perceive you, supporting your personal development and success journey.

🙏 Acknowledgments

• Hume.ai team for their incredible emotion AI technology
• TensorFlow.js team for machine learning capabilities
• Weaviate team for vector database functionality
• MongoDB team for time series database support
• Redis team for in-memory data store
• Ollama team for AI model support