fusero-app-boilerplate/frontend/src/components/FuseMind/README.md

FuseMind Memory System Architecture

Overview

FuseMind implements a sophisticated memory system that combines psychological memory models with modern LLM capabilities. The system is designed to be modular and extensible, allowing for gradual implementation and refinement.

Core Concepts

Memory Types

1. Episodic Memory

   • Stores specific events and experiences
   • Includes temporal and emotional context
   • Used for conversation history and user interactions

2. Semantic Memory

   • Stores general knowledge and facts
   • Includes relationships between concepts
   • Used for system knowledge and agent capabilities

3. Procedural Memory

   • Stores skills and procedures
   • Includes conditions and exceptions
   • Used for agent behaviors and capabilities

4. Life Event Memory

   • Stores significant life events
   • Tracks emotional impact and phases
   • Influences memory formation and retrieval

Emotional Context

• Tracks emotional state (mood, energy, stress, focus)
• Manages emotional triggers and biases
• Influences memory formation and retrieval
• Handles emotional volatility and stability

Temporal Context

• Tracks life events and their duration
• Manages memory decay and importance
• Handles period-specific biases
• Influences memory relevance

Technical Implementation

Memory Service

The MemoryService class handles all memory operations:

• Memory storage and retrieval
• Emotional state management
• Life event tracking
• Memory consolidation

Database Schema

Memories are stored with the following structure:

interface DatabaseMemory {
    id: string;
    type: MemoryType;
    content: string; // JSON stringified
    temporal: string; // JSON stringified
    emotional: string; // JSON stringified
    connections: string; // JSON stringified
    created_at: Date;
    updated_at: Date;
}

React Integration

The system provides a useFusemindMemory hook for React components:

const {
    activeMemories,
    emotionalState,
    storeMemory,
    retrieveMemories,
    updateEmotionalState
} = useFusemindMemory();

Implementation Roadmap

Phase 1: Core Memory System

• Basic memory types and interfaces
• Memory service implementation
• React integration
• Database integration

Phase 2: Emotional Context

• Emotional state tracking
• Trigger management
• Bias handling
• Emotional influence on memory

Phase 3: Life Events

• Life event tracking
• Event phase management
• Recovery and healing
• Temporal context influence

Phase 4: LLM Integration

• Context window management
• Embedding storage and retrieval
• Prompt template management
• Memory-augmented generation

Usage Examples

Storing a Memory

const memory: MemoryUnit = {
    id: generateId(),
    type: 'episodic',
    content: {
        data: { /* memory content */ },
        metadata: { /* additional info */ }
    },
    temporal: {
        created: new Date(),
        modified: new Date(),
        lastAccessed: new Date(),
        decayRate: 0.1,
        importance: 0.8
    },
    emotional: {
        valence: 0.5,
        arousal: 0.3,
        emotionalTags: ['positive', 'exciting']
    },
    connections: []
};

await memoryService.storeMemory(memory);

Retrieving Memories

const memories = await memoryService.retrieveMemory({
    query: 'search term',
    context: {
        currentTask: 'task description',
        emotionalState: 'current state'
    },
    filters: {
        type: 'episodic',
        timeRange: [startDate, endDate]
    }
});

Best Practices

1. Memory Formation

   • Always include temporal and emotional context
   • Consider current life events
   • Track memory importance and decay

2. Memory Retrieval

   • Use appropriate filters
   • Consider emotional context
   • Account for temporal relevance

3. Life Event Management

   • Track event phases
   • Monitor emotional impact
   • Handle event transitions

4. Emotional State

   • Update state gradually
   • Consider multiple factors
   • Handle state transitions

Future Enhancements

1. Advanced Memory Processing

   • Machine learning for memory importance
   • Automated memory consolidation
   • Dynamic decay rates

2. Enhanced Emotional Context

   • Multi-dimensional emotional states
   • Complex trigger patterns
   • Emotional memory networks

3. Improved LLM Integration

   • Context-aware prompting
   • Memory-augmented generation
   • Dynamic context management

4. Visualization Tools

   • Memory network visualization
   • Emotional state tracking
   • Life event timeline