knowledge_bases.py

# knowledge_bases.py
from dataclasses import dataclass
import sqlite3
import json

@dataclass
class KnowledgeBaseConfig:
    db_path: str
    schema: dict
    agent_type: str

class AgentKnowledgeBase:
    def __init__(self, config: KnowledgeBaseConfig):
        self.conn = sqlite3.connect(config.db_path)
        self.agent_type = config.agent_type
        self.init_schema(config.schema)

    def init_schema(self, schema):
        cursor = self.conn.cursor()
        for table_name, columns in schema.items():
            cursor.execute(f"""
                CREATE TABLE IF NOT EXISTS {table_name} (
                    id INTEGER PRIMARY KEY,
                    {columns},
                    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
                )
            """)
        self.conn.commit()

# Implementation of specific knowledge bases
class MemoryKnowledgeBase(AgentKnowledgeBase):
    def __init__(self):
        super().__init__(KnowledgeBaseConfig(
            db_path="memory_kb.sqlite",
            schema={
                "concepts": "name TEXT, importance FLOAT, usage_count INTEGER",
                "patterns": "pattern TEXT, frequency INTEGER, last_seen TIMESTAMP",
                "relationships": "source_id INTEGER, target_id INTEGER, strength FLOAT"
            },
            agent_type="memory"
        ))

class EvolutionKnowledgeBase(AgentKnowledgeBase):
    def __init__(self):
        super().__init__(KnowledgeBaseConfig(
            db_path="evolution_kb.sqlite",
            schema={
                "fitness_metrics": "metric_name TEXT, weight FLOAT, adaptation_rate FLOAT",
                "optimization_rules": "rule TEXT, priority INTEGER, success_rate FLOAT",
                "evolution_history": "change TEXT, impact FLOAT, timestamp TIMESTAMP"
            },
            agent_type="evolution"
        ))

# communication.py
class MessageBroker:
    def __init__(self):
        self.queues = {}
        self.subscribers = {}
        self.feedback_loops = {}

    async def publish(self, topic: str, message: dict):
        if topic in self.subscribers:
            for subscriber in self.subscribers[topic]:
                await subscriber.process_message(message)

    async def subscribe(self, topic: str, callback):
        if topic not in self.subscribers:
            self.subscribers[topic] = []
        self.subscribers[topic].append(callback)

class FeedbackController:
    def __init__(self):
        self.feedback_history = []
        self.learning_rate = 0.1

    async def process_feedback(self, source_agent: str, target_agent: str, feedback: dict):
        self.feedback_history.append({
            "source": source_agent,
            "target": target_agent,
            "feedback": feedback,
            "timestamp": datetime.now().isoformat()
        })
        
        # Analyze feedback patterns
        patterns = self.analyze_feedback_patterns()
        
        # Update agent behaviors based on feedback
        await self.update_agent_behavior(target_agent, patterns)

# prompt_management.py
class PromptTemplate:
    def __init__(self, agent_type: str):
        self.agent_type = agent_type
        self.templates = self.load_templates()
        self.performance_metrics = {}

    def load_templates(self) -> dict:
        return {
            "memory": {
                "process_memory": """
                Process this memory with the following considerations:
                Context: {context}
                Previous Memories: {previous_memories}
                
                Tasks:
                1. Analyze semantic content
                2. Extract key relationships
                3. Determine storage priority
                4. Identify potential connections
                
                Format response as JSON with reasoning.
                """,
                "consolidate_memory": """..."""
            },
            "evolution": {
                "evaluate_fitness": """
                Evaluate system fitness with these metrics:
                Current State: {current_state}
                Historical Performance: {history}
                
                Consider:
                1. Resource efficiency
                2. Learning rate
                3. Adaptation speed
                4. Error reduction
                
                Provide numerical scores and explanations.
                """,
                "optimize_system": """..."""
            },
            "supervisor": {
                "monitor_system": """
                Monitor system health:
                Agent States: {agent_states}
                Resource Usage: {resources}
                Error Rates: {errors}
                
                Tasks:
                1. Identify bottlenecks
                2. Suggest optimizations
                3. Coordinate resources
                4. Prioritize actions
                
                Provide structured supervision plan.
                """,
                "coordinate_agents": """..."""
            }
        }

class PromptOptimizer:
    def __init__(self):
        self.performance_history = []
        self.optimization_rules = {}

    async def optimize_prompt(self, template: str, performance_metrics: dict) -> str:
        # Analyze prompt performance
        optimization_score = self.calculate_optimization_score(performance_metrics)
        
        # Apply optimization rules
        optimized_template = self.apply_optimizations(template, optimization_score)
        
        return optimized_template

# Enhanced Agent Implementation
class EnhancedAgent(BaseAgent):
    def __init__(self, config: AgentConfig, knowledge_base: AgentKnowledgeBase):
        super().__init__(config)
        self.knowledge_base = knowledge_base
        self.prompt_template = PromptTemplate(config.agent_type)
        self.message_broker = MessageBroker()
        self.feedback_controller = FeedbackController()

    async def process_message(self, message: dict):
        # Process incoming message
        prompt = self.prompt_template.get_prompt(
            template_name=message["type"],
            context=message["context"]
        )
        
        # Generate response
        response = await self.generate_response(prompt)
        
        # Store in knowledge base
        await self.knowledge_base.store_interaction(message, response)
        
        # Publish feedback
        await self.message_broker.publish(
            "feedback",
            {
                "source": self.config.agent_type,
                "content": response,
                "performance_metrics": self.calculate_performance(response)
            }
        )
        
        return response

# Example usage
async def main():
    # Initialize knowledge bases
    memory_kb = MemoryKnowledgeBase()
    evolution_kb = EvolutionKnowledgeBase()
    
    # Initialize agents with knowledge bases
    memory_agent = EnhancedAgent(
        config=AgentConfig(...),
        knowledge_base=memory_kb
    )
    
    evolution_agent = EnhancedAgent(
        config=AgentConfig(...),
        knowledge_base=evolution_kb
    )
    
    # Set up communication
    message_broker = MessageBroker()
    await message_broker.subscribe("memory_events", memory_agent.process_message)
    await message_broker.subscribe("evolution_events", evolution_agent.process_message)
    
    # Example interaction
    test_message = {
        "type": "process_memory",
        "content": "New memory to process",
        "context": {"previous_memories": [...]}
    }
    
    # Publish message and get response
    await message_broker.publish("memory_events", test_message)