Update memory_drift_diagnostician.py

memory_drift_diagnostician.py

@@ -1,6 +1,6 @@
 import logging
 import numpy as np
-from typing import Dict, Any, List, Optional
+from typing import Dict, Any, List
 from agentic_reliability_framework.runtime.agents.base import BaseAgent, AgentSpecialization
 from ai_event import AIEvent
 
@@ -14,32 +14,13 @@ class MemoryDriftDiagnosticianAgent(BaseAgent):
     """
 
     def __init__(self, history_window: int = 100, zscore_threshold: float = 2.0):
-        """
-        Args:
-            history_window: Number of recent scores to keep for baseline statistics.
-            zscore_threshold: Absolute z‑score above which drift is flagged.
-        """
         super().__init__(AgentSpecialization.DIAGNOSTICIAN)
         self.history_window = history_window
         self.zscore_threshold = zscore_threshold
         self._retrieval_scores_history: List[float] = []
 
     async def analyze(self, event: AIEvent) -> Dict[str, Any]:
-        """
-        Analyze retrieval scores for drift.
-
-        Args:
-            event: AIEvent containing `retrieval_scores` (list of floats).
-
-        Returns:
-            Dictionary with keys:
-                - specialization: str
-                - confidence: float (0‑1) based on z‑score magnitude
-                - findings: dict with drift detection and statistics
-                - recommendations: list of strings if drift detected
-        """
         try:
-            # If no retrieval scores, cannot compute drift
             if not event.retrieval_scores:
                 return {
                     'specialization': 'ai_memory_drift',
@@ -48,15 +29,12 @@ class MemoryDriftDiagnosticianAgent(BaseAgent):
                     'recommendations': []
                 }
 
-            # Current average score
             current_avg = float(np.mean(event.retrieval_scores))
             self._retrieval_scores_history.append(current_avg)
 
-            # Trim history to window size
             if len(self._retrieval_scores_history) > self.history_window:
                 self._retrieval_scores_history.pop(0)
 
-            # Need at least 10 points for a reliable baseline
             if len(self._retrieval_scores_history) < 10:
                 return {
                     'specialization': 'ai_memory_drift',
@@ -70,13 +48,10 @@ class MemoryDriftDiagnosticianAgent(BaseAgent):
                     'recommendations': []
                 }
 
-            # Historical baseline (excluding current point)
             historical_avg = float(np.mean(self._retrieval_scores_history[:-1]))
-            historical_std = float(np.std(self._retrieval_scores_history[:-1])) + 1e-6  # avoid division by zero
+            historical_std = float(np.std(self._retrieval_scores_history[:-1])) + 1e-6
             z_score = (current_avg - historical_avg) / historical_std
             drift_detected = abs(z_score) > self.zscore_threshold
-
-            # Confidence derived from z‑score magnitude (capped at 1.0)
             confidence = min(1.0, abs(z_score) / 5.0)
 
             return {
@@ -94,7 +69,6 @@ class MemoryDriftDiagnosticianAgent(BaseAgent):
                     "Update context window"
                 ] if drift_detected else []
             }
-
         except Exception as e:
             logger.error(f"MemoryDriftDiagnostician error: {e}", exc_info=True)
             return {