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Architecture Overview

FlavumHive is built with a modular, extensible architecture that enables seamless integration of multiple platforms and AI personalities. This document explains the core architectural components and their interactions.

System Architecture​

graph TD
    A[Personality Manager] --> B[Platform Handlers]
    B --> C[Twitter Handler]
    B --> D[Reddit Handler]
    E[Configuration System] --> A
    E --> B
    F[Database] --> B
    G[Monitoring System] --> B
    H[OpenAI Integration] --> A
    I[Rate Limiter] --> B

Core Components​

1. Personality Manager​

  • Purpose: Central management of AI personalities and their behaviors
  • Responsibilities:
    • Loading personality configurations
    • Managing personality states
    • Coordinating with OpenAI for content generation
    • Ensuring consistency across platforms

2. Platform Handlers​

  • Base Handler:

    • Abstract interface for platform integration
    • Common utilities and shared functionality
    • Rate limiting and error handling

  • Platform-Specific Handlers:

    • Twitter: Selenium-based automation
    • Reddit: PRAW API integration
    • Future platforms: Extensible architecture

3. Configuration System​

  • Hierarchical Configuration:
    • Environment variables (.env)
    • Global settings (config.json)
    • Platform-specific configs
    • Personality configurations

4. Database Layer​

  • SQLite Integration:
    • Action history tracking
    • State persistence
    • Rate limit monitoring
    • Analytics data storage

5. Monitoring System​

  • Components:
    • Logging system
    • Health checks
    • Performance metrics
    • Alert management

Data Flow​

1. Content Generation Flow​

sequenceDiagram
    participant PM as Personality Manager
    participant OAI as OpenAI
    participant PH as Platform Handler
    participant DB as Database

    PM->>OAI: Request content generation
    OAI-->>PM: Generated content
    PM->>PH: Format for platform
    PH->>DB: Log action
    PH->>Platform: Post content

2. Interaction Flow​

sequenceDiagram
    participant Platform
    participant PH as Platform Handler
    participant PM as Personality Manager
    participant DB as Database

    Platform->>PH: New interaction
    PH->>PM: Request response
    PM->>OAI: Generate response
    OAI-->>PM: Response content
    PM->>PH: Format response
    PH->>Platform: Post response
    PH->>DB: Log interaction

Component Details​

Personality Manager​

class PersonalityManager:
    def __init__(self):
        self.personalities = {}
        self.active_personality = None
        self.openai_client = None

    def load_personality(self, name: str):
        # Load personality configuration
        # Initialize OpenAI client
        # Set active personality

Platform Handler Base​

class BasePlatformHandler:
    def __init__(self):
        self.rate_limiter = RateLimiter()
        self.config = None
        self.db = None

    @abstractmethod
    def post_content(self):
        pass

    @abstractmethod
    def handle_interaction(self):
        pass

Error Handling​

1. Platform-Level Errors​

  • Rate limit exceeded
  • Authentication failures
  • Network issues
  • Platform-specific errors

2. System-Level Errors​

  • Configuration errors
  • Database issues
  • Resource constraints
  • Integration failures

Error Recovery Flow​

graph TD
    A[Error Detected] --> B{Error Type}
    B -->|Rate Limit| C[Backoff & Retry]
    B -->|Auth Error| D[Reauth Flow]
    B -->|System Error| E[Alert & Log]
    C --> F[Resume Operations]
    D --> F
    E --> G[Manual Intervention]

Extensibility​

Adding New Platforms​

  1. Create new handler class
  2. Implement required interfaces
  3. Add platform-specific configuration
  4. Update personality configurations

Custom Personality Traits​

  1. Define trait schema
  2. Create personality configuration
  3. Implement behavior modifiers
  4. Add platform-specific adaptations

Performance Considerations​

1. Rate Limiting​

  • Platform-specific limits
  • Global rate limiting
  • Adaptive throttling
  • Backoff strategies

2. Resource Management​

  • Connection pooling
  • Cache management
  • Database optimization
  • Memory usage control

3. Scalability​

  • Modular design
  • Independent components
  • Stateless operations
  • Horizontal scaling support

Security Architecture​

1. Authentication​

  • Secure credential storage
  • Token management
  • Session handling
  • 2FA support

2. Data Protection​

  • Encrypted storage
  • Secure configurations
  • Access control
  • Audit logging

Next Steps​