Codette3.0 / src /models /healing_system.py
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"""
This module implements a self-healing system for automated recovery and maintenance.
"""
import asyncio
from datetime import datetime
from typing import Dict, Any, List, Optional
class SelfHealingSystem:
"""Provides self-healing capabilities for system components"""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.health_metrics = {}
self.healing_actions = []
self.last_check = None
self.healing_threshold = config.get("healing_threshold", 0.7)
self.check_interval = config.get("health_check_interval", 300) # 5 minutes
async def check_health(self) -> Dict[str, Any]:
"""
Check the health status of the system.
Returns:
Dictionary containing health metrics
"""
self.last_check = datetime.now()
# Gather system metrics
metrics = await self._gather_metrics()
# Update health metrics
self.health_metrics.update(metrics)
# Evaluate if healing is needed
if self._needs_healing(metrics):
await self._initiate_healing()
return self.get_health_summary()
async def _gather_metrics(self) -> Dict[str, float]:
"""
Gather various system health metrics.
Returns:
Dictionary of metric names and values
"""
return {
"memory_usage": await self._check_memory_usage(),
"response_time": await self._check_response_time(),
"error_rate": await self._check_error_rate(),
"model_coherence": await self._check_model_coherence(),
"system_stability": await self._check_system_stability()
}
def _needs_healing(self, metrics: Dict[str, float]) -> bool:
"""
Determine if the system needs healing based on metrics.
Args:
metrics: Dictionary of system metrics
Returns:
True if healing is needed, False otherwise
"""
# Calculate average health score
health_score = sum(metrics.values()) / len(metrics)
return health_score < self.healing_threshold
async def _initiate_healing(self):
"""
Initiate self-healing procedures.
"""
healing_actions = [
self._heal_memory_issues(),
self._heal_response_issues(),
self._heal_model_issues(),
self._heal_stability_issues()
]
# Execute healing actions concurrently
await asyncio.gather(*healing_actions)
# Log healing action
self.healing_actions.append({
"timestamp": datetime.now().isoformat(),
"metrics": self.health_metrics.copy(),
"actions": ["memory", "response", "model", "stability"]
})
async def _heal_memory_issues(self):
"""Handle memory-related issues"""
# Implementation for memory healing
pass
async def _heal_response_issues(self):
"""Handle response time issues"""
# Implementation for response healing
pass
async def _heal_model_issues(self):
"""Handle model coherence issues"""
# Implementation for model healing
pass
async def _heal_stability_issues(self):
"""Handle system stability issues"""
# Implementation for stability healing
pass
async def _check_memory_usage(self) -> float:
"""Check system memory usage"""
return 0.8 # Placeholder: 80% healthy
async def _check_response_time(self) -> float:
"""Check system response time"""
return 0.9 # Placeholder: 90% healthy
async def _check_error_rate(self) -> float:
"""Check system error rate"""
return 0.95 # Placeholder: 95% healthy
async def _check_model_coherence(self) -> float:
"""Check model coherence"""
return 0.85 # Placeholder: 85% healthy
async def _check_system_stability(self) -> float:
"""Check overall system stability"""
return 0.9 # Placeholder: 90% healthy
def get_health_summary(self) -> Dict[str, Any]:
"""
Get a summary of the system's health status.
Returns:
Dictionary containing health summary
"""
return {
"last_check": self.last_check.isoformat() if self.last_check else None,
"metrics": self.health_metrics,
"recent_healing_actions": self.healing_actions[-5:], # Last 5 healing actions
"overall_health": sum(self.health_metrics.values()) / len(self.health_metrics) if self.health_metrics else 0
}
def get_healing_history(self) -> List[Dict[str, Any]]:
"""
Get the history of healing actions.
Returns:
List of healing action records
"""
return self.healing_actions
async def manual_heal(self, component: str) -> Dict[str, Any]:
"""
Manually trigger healing for a specific component.
Args:
component: Name of the component to heal
Returns:
Results of the healing action
"""
healing_map = {
"memory": self._heal_memory_issues,
"response": self._heal_response_issues,
"model": self._heal_model_issues,
"stability": self._heal_stability_issues
}
if component in healing_map:
await healing_map[component]()
return {"status": "success", "component": component}
else:
return {"status": "error", "message": f"Unknown component: {component}"}