Update app/services/risk_service.py

app/services/risk_service.py

@@ -1,158 +1,97 @@
-from agentic_reliability_framework.core.governance.risk_engine import RiskEngine
-from agentic_reliability_framework.core.governance.intents import InfrastructureIntent
 from typing import Optional, List, Dict, Any
-
-from agentic_reliability_framework.core.models.event import ReliabilityEvent, HealingAction
-from agentic_reliability_framework.core.governance.policy_engine import PolicyEngine
-from agentic_reliability_framework.core.decision.decision_engine import DecisionEngine
-from agentic_reliability_framework.runtime.memory.rag_graph import RAGGraphMemory
-
-# NEW: Import eclipse probe
-from agentic_reliability_framework.core.research.eclipse_probe import compute_epistemic_risk
+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: InfrastructureIntent,
+    intent,
     cost_estimate: Optional[float],
     policy_violations: List[str]
 ) -> dict:
-    """
-    Evaluate an infrastructure intent using the Bayesian risk engine.
-    Returns a dictionary with risk score, explanation, and contributions.
-    """
-    score, explanation, contributions = engine.calculate_risk(
-        intent=intent,
-        cost_estimate=cost_estimate,
-        policy_violations=policy_violations
-    )
+    """Mock sandbox evaluation – returns a fixed risk score."""
     return {
-        "risk_score": score,
-        "explanation": explanation,
-        "contributions": contributions
+        "risk_score": 0.38,
+        "explanation": "Sandbox mock: high latency detected, escalating.",
+        "contributions": {"conjugate_mean": 0.38}
     }
 
 
 def evaluate_healing_decision(
-    event: ReliabilityEvent,
+    event,
     policy_engine: PolicyEngine,
     decision_engine: Optional[DecisionEngine] = None,
     rag_graph: Optional[RAGGraphMemory] = None,
-    model=None,          # NEW: optional HuggingFace model
-    tokenizer=None,      # NEW: optional tokenizer
+    model=None,
+    tokenizer=None,
 ) -> Dict[str, Any]:
-    """
-    Evaluate healing actions for a given reliability event using decision‑theoretic selection.
-    Now includes epistemic risk signals from the eclipse probe.
-
-    Returns:
-        Dictionary with keys: risk_score, selected_action, expected_utility, alternatives,
-        explanation, epistemic_signals (new).
-    """
-    # If decision_engine not provided, try to get from policy_engine
-    if decision_engine is None and hasattr(policy_engine, 'decision_engine'):
-        decision_engine = policy_engine.decision_engine
-
-    # If still None, create a minimal one (global stats only)
-    if decision_engine is None:
-        decision_engine = DecisionEngine(rag_graph=rag_graph)
-
-    # Get raw candidate actions (by temporarily disabling decision engine)
-    orig_use = policy_engine.use_decision_engine
-    try:
-        policy_engine.use_decision_engine = False
-        raw_actions = policy_engine.evaluate_policies(event)
-    finally:
-        policy_engine.use_decision_engine = orig_use
-
-    # If no actions, return NO_ACTION
-    if not raw_actions or raw_actions == [HealingAction.NO_ACTION]:
-        return {
-            "risk_score": 0.0,
-            "selected_action": HealingAction.NO_ACTION.value,
-            "expected_utility": 0.0,
-            "alternatives": [],
-            "explanation": "No candidate actions triggered.",
-            "epistemic_signals": None,
-        }
-
-    # === NEW: Compute epistemic signals from triggered policies ===
-    # Build reasoning text from the policies that triggered the actions
-    reasoning_parts = []
-    for policy in policy_engine.policies:
-        # Check if any of the policy's actions are in raw_actions
-        if any(a in policy.actions for a in raw_actions):
-            conditions_str = ", ".join(
-                f"{c.metric} {c.operator} {c.threshold}" for c in policy.conditions
-            )
-            reasoning_parts.append(
-                f"Policy {policy.name} triggered by {conditions_str} → actions {[a.value for a in policy.actions]}"
-            )
-    reasoning_text = " ".join(reasoning_parts)
-
-    # Build evidence text from the event
-    evidence_text = (
-        f"Component: {event.component}, "
-        f"latency_p99: {event.latency_p99}, "
-        f"error_rate: {event.error_rate}, "
-        f"cpu_util: {event.cpu_util}, "
-        f"memory_util: {event.memory_util}"
-    )
-
-    # Compute epistemic signals (if model/tokenizer provided)
-    epistemic_signals = None
-    if model is not None and tokenizer is not None:
-        epistemic_signals = compute_epistemic_risk(
-            reasoning_text, evidence_text, model, tokenizer
-        )
-    else:
-        # In OSS, we may not have model; use zeros as fallback
-        epistemic_signals = {
+    """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,
-        }
-
-    # Run decision engine to get best action and alternatives, passing epistemic signals
-    decision = decision_engine.select_optimal_action(
-        raw_actions, event, component=event.component,
-        epistemic_signals=epistemic_signals
-    )
-
-    # Risk of the selected action
-    risk_score = None
-    for alt in decision.alternatives:
-        if alt.action == decision.best_action:
-            risk_score = alt.risk
-            break
-    if risk_score is None:
-        # Compute risk separately
-        risk_score = decision_engine.compute_risk(decision.best_action, event, event.component)
-
-    # Format alternatives (top 3 only)
-    alt_list = []
-    for alt in decision.alternatives[:3]:
-        alt_list.append({
-            "action": alt.action.value,
-            "expected_utility": alt.utility,
-            "risk": alt.risk,
-        })
-
-    # Build final response
-    response = {
-        "risk_score": risk_score,
-        "selected_action": decision.best_action.value,
-        "expected_utility": decision.expected_utility,
-        "alternatives": alt_list,
-        "explanation": decision.explanation,
-        "raw_decision": decision.raw_data,
-        "epistemic_signals": epistemic_signals,   # NEW
+        },
     }
-    return response
 
 
 def get_system_risk() -> float:
-    # Placeholder – this endpoint is being deprecated; we keep it for backward compatibility.
     import random
-    return round(random.uniform(0, 1), 2)
+    return round(random.uniform(0, 1), 2)