Delete memory_drift_diagnostician.py

memory_drift_diagnostician.py

@@ -1,89 +0,0 @@
-import logging
-import numpy as np
-from typing import Dict, Any, List, Optional
-from agentic_reliability_framework.runtime.agents.base import BaseAgent, AgentSpecialization
-from ai_event import AIEvent
-
-logger = logging.getLogger(__name__)
-
-
-class MemoryDriftDiagnosticianAgent(BaseAgent):
-    """
-    Detects drift in semantic memory by comparing current retrieval scores
-    with their historical distribution using a z‑score test.
-    """
-
-    def __init__(self, history_window: int = 100, zscore_threshold: float = 2.0):
-        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, context_window: Optional[int] = None) -> Dict[str, Any]:
-        """
-        Analyze drift using retrieval scores.
-        Args:
-            event: AIEvent containing retrieval_scores.
-            context_window: Optional override of history window size.
-        Returns:
-            Dictionary with drift detection findings.
-        """
-        try:
-            if not event.retrieval_scores:
-                return {
-                    'specialization': 'ai_memory_drift',
-                    'confidence': 0.0,
-                    'findings': {},
-                    'recommendations': []
-                }
-
-            current_avg = float(np.mean(event.retrieval_scores))
-            self._retrieval_scores_history.append(current_avg)
-
-            # Use provided context_window if given, else default
-            window = context_window if context_window is not None else self.history_window
-            if len(self._retrieval_scores_history) > window:
-                self._retrieval_scores_history.pop(0)
-
-            if len(self._retrieval_scores_history) < 10:
-                return {
-                    'specialization': 'ai_memory_drift',
-                    'confidence': 0.0,
-                    'findings': {
-                        'drift_detected': False,
-                        'current_avg': current_avg,
-                        'historical_avg': None,
-                        'z_score': None
-                    },
-                    'recommendations': []
-                }
-
-            historical_avg = float(np.mean(self._retrieval_scores_history[:-1]))
-            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 = min(1.0, abs(z_score) / 5.0)
-
-            return {
-                'specialization': 'ai_memory_drift',
-                'confidence': confidence,
-                'findings': {
-                    'drift_detected': drift_detected,
-                    'current_avg': current_avg,
-                    'historical_avg': historical_avg,
-                    'z_score': float(z_score)
-                },
-                'recommendations': [
-                    "Reindex knowledge base",
-                    "Adjust embedding model",
-                    "Update context window"
-                ] if drift_detected else []
-            }
-        except Exception as e:
-            logger.error(f"MemoryDriftDiagnostician error: {e}", exc_info=True)
-            return {
-                'specialization': 'ai_memory_drift',
-                'confidence': 0.0,
-                'findings': {},
-                'recommendations': []
-            }