ses-dma.py

# Core System Configuration
config = {
    "llm": {
        "endpoint": "http://localhost:1234/v1/chat/completions",  # LM Studio endpoint
        "model": "local-model",
        "temperature": 0.7,
        "max_tokens": 512
    },
    "memory": {
        "db_path": "memory.sqlite",
        "stm_capacity": 10,  # Number of items in short-term memory
        "ltm_threshold": 0.6,  # Fitness threshold for long-term memory
        "consolidation_interval": 300  # 5 minutes
    },
    "logging": {
        "log_path": "ses_dma.log",
        "metrics_db": "metrics.sqlite",
        "log_level": "INFO"
    }
}

# Core Memory Architecture
class MemoryStore:
    def __init__(self, db_path):
        self.conn = sqlite3.connect(db_path)
        self.init_tables()
    
    def init_tables(self):
        """Initialize core memory tables"""
        self.conn.executescript("""
            CREATE TABLE IF NOT EXISTS short_term_memory (
                id INTEGER PRIMARY KEY,
                content TEXT,
                embedding BLOB,
                timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
                access_count INTEGER DEFAULT 1,
                importance_score FLOAT DEFAULT 0.5
            );
            
            CREATE TABLE IF NOT EXISTS long_term_memory (
                id INTEGER PRIMARY KEY,
                content TEXT,
                embedding BLOB,
                created_at DATETIME,
                last_accessed DATETIME,
                access_count INTEGER,
                importance_score FLOAT,
                connections JSON
            );
            
            CREATE TABLE IF NOT EXISTS memory_metrics (
                id INTEGER PRIMARY KEY,
                timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
                memory_type TEXT,
                operation TEXT,
                details JSON
            );
        """)

class MemoryController:
    def __init__(self, config):
        self.store = MemoryStore(config["memory"]["db_path"])
        self.logger = self.setup_logger(config["logging"])
        self.metrics = MetricsCollector(config["logging"]["metrics_db"])
        
    async def process_input(self, query: str):
        """Process incoming query and manage memory operations"""
        self.logger.info(f"Processing query: {query}")
        
        # Record input metrics
        await self.metrics.record_operation("input_processing", {
            "query_length": len(query),
            "timestamp": datetime.now().isoformat()
        })
        
        # Store in short-term memory
        memory_id = await self.store.add_to_stm(query)
        
        return memory_id

    async def consolidate_memories(self):
        """Basic memory consolidation process"""
        memories = await self.store.get_stm_candidates()
        for memory in memories:
            fitness = await self.calculate_fitness(memory)
            if fitness >= self.config["memory"]["ltm_threshold"]:
                await self.store.promote_to_ltm(memory)
                self.logger.info(f"Memory {memory.id} promoted to LTM")

class EvolutionSystem:
    def __init__(self, memory_controller):
        self.memory_controller = memory_controller
        self.fitness_calculator = MemoryFitnessCalculator()
        
    async def calculate_fitness(self, memory_item):
        """Calculate memory item's fitness score"""
        metrics = {
            "recency": self.calculate_recency(memory_item.timestamp),
            "access_frequency": memory_item.access_count,
            "importance": memory_item.importance_score,
        }
        
        return sum(metrics.values()) / len(metrics)
    
    async def prune_memories(self):
        """Basic memory pruning based on fitness"""
        candidates = await self.memory_controller.get_pruning_candidates()
        for memory in candidates:
            fitness = await self.calculate_fitness(memory)
            if fitness < self.config["memory"]["ltm_threshold"]:
                await self.memory_controller.archive_memory(memory.id)

class MonitoringSystem:
    def __init__(self, config):
        self.logger = self.setup_logger(config)
        self.metrics_db = self.init_metrics_db(config)
        
    def setup_logger(self, config):
        """Configure structured logging"""
        logging.basicConfig(
            filename=config["logging"]["log_path"],
            level=config["logging"]["log_level"],
            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        )
        return logging.getLogger("SES-DMA")
    
    async def record_metric(self, metric_type, value, metadata=None):
        """Record system metrics"""
        await self.metrics_db.execute(
            "INSERT INTO metrics (type, value, metadata) VALUES (?, ?, ?)",
            (metric_type, value, json.dumps(metadata or {}))
        )

class LLMInterface:
    def __init__(self, config):
        self.endpoint = config["llm"]["endpoint"]
        self.headers = {
            "Content-Type": "application/json"
        }
    
    async def generate_response(self, prompt, context=None):
        """Interface with LM Studio endpoint"""
        payload = {
            "messages": [
                {"role": "system", "content": "You are a helpful assistant with evolving memory."},
                {"role": "user", "content": prompt}
            ],
            "temperature": self.config["llm"]["temperature"],
            "max_tokens": self.config["llm"]["max_tokens"]
        }
        
        if context:
            payload["messages"].insert(1, {
                "role": "assistant",
                "content": f"Previous context: {context}"
            })
        
        async with aiohttp.ClientSession() as session:
            async with session.post(self.endpoint, json=payload) as response:
                return await response.json()