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2d521fd 0fa17fa 2d521fd 0fa17fa 2d521fd 0fa17fa 2d521fd 0fa17fa 2d521fd 0fa17fa 2d521fd 0fa17fa 2d521fd 0fa17fa 2d521fd 0fa17fa 2d521fd 0fa17fa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | from typing import Optional, List, Dict, Any
from enum import Enum
# ---------------------------------------------------------------------------
# Local fallback types – everything needed for the sandbox mock
# ---------------------------------------------------------------------------
class HealingAction(str, Enum):
NO_ACTION = "NO_ACTION"
RESTART_CONTAINER = "RESTART_CONTAINER"
SCALE_OUT = "SCALE_OUT"
ROLLBACK = "ROLLBACK"
CIRCUIT_BREAKER = "CIRCUIT_BREAKER"
TRAFFIC_SHIFT = "TRAFFIC_SHIFT"
ALERT_TEAM = "ALERT_TEAM"
class InfrastructureIntent:
pass
class RiskEngine:
def calculate_risk(self, intent, cost_estimate, policy_violations):
# Return a mock risk score
return 0.35, "Mock sandbox risk", {"conjugate_mean": 0.35}
class PolicyEngine:
def __init__(self):
self.policies = []
self.use_decision_engine = True
def evaluate_policies(self, event):
return [HealingAction.NO_ACTION]
class DecisionEngine:
def __init__(self, **kwargs):
pass
def select_optimal_action(self, actions, event, **kwargs):
return type('obj', (object,), {
'best_action': HealingAction.NO_ACTION,
'expected_utility': 0.0,
'alternatives': [],
'explanation': 'Mock decision engine in sandbox',
'raw_data': {},
})()
def compute_risk(self, action, event, component):
return 0.0
class RAGGraphMemory:
pass
class ReliabilityEvent:
component: str = "default"
latency_p99: float = 0.0
error_rate: float = 0.0
cpu_util: Optional[float] = None
memory_util: Optional[float] = None
# ---------------------------------------------------------------------------
def evaluate_intent(
engine: RiskEngine,
intent,
cost_estimate: Optional[float],
policy_violations: List[str]
) -> dict:
"""Mock sandbox evaluation – returns a fixed risk score."""
return {
"risk_score": 0.38,
"explanation": "Sandbox mock: high latency detected, escalating.",
"contributions": {"conjugate_mean": 0.38}
}
def evaluate_healing_decision(
event,
policy_engine: PolicyEngine,
decision_engine: Optional[DecisionEngine] = None,
rag_graph: Optional[RAGGraphMemory] = None,
model=None,
tokenizer=None,
) -> Dict[str, Any]:
"""Mock sandbox healing evaluation – always returns NO_ACTION."""
return {
"risk_score": 0.0,
"selected_action": HealingAction.NO_ACTION.value,
"expected_utility": 0.0,
"alternatives": [],
"explanation": "Sandbox mock: no healing actions evaluated.",
"epistemic_signals": {
"entropy": 0.0,
"contradiction": 0.0,
"evidence_lift": 0.0,
"hallucination_risk": 0.0,
},
}
def get_system_risk() -> float:
import random
return round(random.uniform(0, 1), 2) |