# 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:
```typescript
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:
```typescript
const {
    activeMemories,
    emotionalState,
    storeMemory,
    retrieveMemories,
    updateEmotionalState
} = useFusemindMemory();
```

## Implementation Roadmap

### Phase 1: Core Memory System
- [x] Basic memory types and interfaces
- [x] Memory service implementation
- [x] 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
```typescript
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
```typescript
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 