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AI Integration

Overview

Deep dive into AI integration capabilities of CASYS RPG, including custom models, advanced processing, and optimization techniques.

Language Models

Model Configuration

class ModelConfig:
    """Language model configuration."""
    model_name: str = "gpt-4o-mini"
    temperature: float = 0.7
    max_tokens: int = 2048
    stop_sequences: List[str] = []

    class Prompts:
        system: str = "You are a game master..."
        context: str = "Current game state..."
        format: str = "Response format..."

  • Model Selection

    • Model capabilities
    • Performance characteristics
    • Resource requirements

  • Parameter Tuning

    • Temperature
    • Token limits
    • Response formatting

Prompt Engineering

%%{init: {'theme': 'default', 'themeVariables': { 'fontFamily': 'Roboto' }}}%%
flowchart TD
    subgraph PC[Prompt Components]
        SY[System Prompt]
        CO[Context]
        HI[History]
        FO[Format]
    end

    subgraph PR[Processing]
        TO[Tokenization]
        OP[Optimization]
        VA[Validation]
    end

    PC --> TO
    PC --> OP
    PC --> VA

    style PC fill:#f9f9f9,stroke:#333,stroke-width:2px
    style PR fill:#f9f9f9,stroke:#333,stroke-width:2px
    style SY fill:#6200ea,stroke:#6200ea,color:#fff
    style CO fill:#6200ea,stroke:#6200ea,color:#fff
    style HI fill:#6200ea,stroke:#6200ea,color:#fff
    style FO fill:#6200ea,stroke:#6200ea,color:#fff
    style TO fill:#6200ea,stroke:#6200ea,color:#fff
    style OP fill:#6200ea,stroke:#6200ea,color:#fff
    style VA fill:#6200ea,stroke:#6200ea,color:#fff

  • Prompt Structure

    • System prompts
    • Context injection
    • Response formatting

  • Optimization

    • Token efficiency
    • Context management
    • Response quality

Advanced Processing

Context Management

class ContextManager:
    """Manages AI context and history."""
    def __init__(self, max_tokens: int = 4096):
        self.history: List[Message] = []
        self.max_tokens = max_tokens

    def add_message(self, message: Message):
        """Add message while managing context window."""
        while self.total_tokens > self.max_tokens:
            self.history.pop(0)
        self.history.append(message)

  • Context Window

    • Size management
    • History pruning
    • Relevance scoring

  • Memory Management

    • Short-term memory
    • Long-term storage
    • Context retrieval

Response Generation

%%{init: {'theme': 'default', 'themeVariables': { 'fontFamily': 'Roboto' }}}%%
flowchart LR
    subgraph IN[Input]
        PR[Prompt]
        CO[Context]
    end

    subgraph PR[Processing]
        TO[Token Processing]
        GE[Generation]
    end

    subgraph OUT[Output]
        RE[Response]
        ME[Metadata]
    end

    IN --> TO
    TO --> GE
    GE --> OUT

    style IN fill:#f9f9f9,stroke:#333,stroke-width:2px
    style PR fill:#f9f9f9,stroke:#333,stroke-width:2px
    style OUT fill:#f9f9f9,stroke:#333,stroke-width:2px
    style PR fill:#6200ea,stroke:#6200ea,color:#fff
    style CO fill:#6200ea,stroke:#6200ea,color:#fff
    style TO fill:#6200ea,stroke:#6200ea,color:#fff
    style GE fill:#6200ea,stroke:#6200ea,color:#fff
    style RE fill:#6200ea,stroke:#6200ea,color:#fff
    style ME fill:#6200ea,stroke:#6200ea,color:#fff

  • Generation Pipeline

    • Input processing
    • Response generation
    • Output formatting

  • Quality Control

    • Response validation
    • Format checking
    • Error handling

Custom Agents

Agent Configuration

class AgentConfig:
    """AI agent configuration."""
    name: str
    role: str
    capabilities: List[str]
    model_config: ModelConfig
    prompt_templates: Dict[str, str]

    def get_prompt(self, context: Dict) -> str:
        """Generate contextualized prompt."""
        template = self.prompt_templates[context["type"]]
        return template.format(**context)

  • Agent Types

    • Specialized roles
    • Custom behaviors
    • Integration points

  • Configuration

    • Model settings
    • Prompt templates
    • Processing rules

Integration Points

%%{init: {'theme': 'default', 'themeVariables': { 'fontFamily': 'Roboto' }}}%%
flowchart TD
    subgraph AG[Agents]
        SA[Story Agent]
        RA[Rules Agent]
        DA[Decision Agent]
        NA[Narrator Agent]
    end

    subgraph AI[AI Integration]
        PR[Prompt Engine]
        LL[LLM Interface]
        CA[Cache]
    end

    AG <--> PR
    PR <--> LL
    LL <--> CA

    style AG fill:#f9f9f9,stroke:#333,stroke-width:2px
    style AI fill:#f9f9f9,stroke:#333,stroke-width:2px
    style SA fill:#6200ea,stroke:#6200ea,color:#fff
    style RA fill:#6200ea,stroke:#6200ea,color:#fff
    style DA fill:#6200ea,stroke:#6200ea,color:#fff
    style NA fill:#6200ea,stroke:#6200ea,color:#fff
    style PR fill:#6200ea,stroke:#6200ea,color:#fff
    style LL fill:#6200ea,stroke:#6200ea,color:#fff
    style CA fill:#6200ea,stroke:#6200ea,color:#fff

  • Communication

    • Inter-agent messaging
    • State sharing
    • Event handling

  • Coordination

    • Task distribution
    • Resource management
    • Error handling

Performance Optimization

Caching

class ResponseCache:
    """Caches AI responses."""
    def __init__(self, capacity: int = 1000):
        self.cache = LRUCache(capacity)

    def get_response(self, prompt: str) -> Optional[str]:
        """Get cached response if available."""
        key = self.hash_prompt(prompt)
        return self.cache.get(key)

  • Cache Strategies

    • Response caching
    • Context caching
    • Cache invalidation

  • Optimization

    • Memory usage
    • Response time
    • Resource efficiency

Async Processing

  • Parallel Processing

    • Task distribution
    • Resource management
    • Result aggregation

  • Queue Management

    • Priority queues
    • Rate limiting
    • Error handling

Best Practices

Development

  1. Code Organization

    • Clear structure
    • Documentation
    • Testing

  2. Error Handling

    • Graceful degradation
    • Error recovery
    • Logging

Deployment

  1. Resource Management

    • CPU usage
    • Memory allocation
    • API quotas

  2. Monitoring

    • Performance metrics
    • Error tracking
    • Usage statistics

Next Steps