Update hallucination_detective.py

hallucination_detective.py

@@ -15,38 +15,22 @@ class HallucinationDetectiveAgent(BaseAgent):
 
     def __init__(self, nli_detector: Optional[NLIDetector] = None):
         super().__init__(AgentSpecialization.DETECTIVE)
-        # Thresholds for flagging – can be overridden by subclass or config
         self._thresholds = {
-            'confidence': 0.7,    # below this → low confidence
-            'entailment': 0.6     # below this → low entailment (possible hallucination)
+            'confidence': 0.7,
+            'entailment': 0.6
         }
         self.nli = nli_detector or NLIDetector()
 
     async def analyze(self, event: AIEvent) -> Dict[str, Any]:
-        """
-        Analyze an AIEvent and return hallucination risk assessment.
-
-        Args:
-            event: AIEvent containing prompt, response, and confidence.
-
-        Returns:
-            Dictionary with keys:
-                - specialization: str
-                - confidence: float (0‑1, where higher means more likely hallucination)
-                - findings: dict with detailed flags
-                - recommendations: list of strings
-        """
         try:
             flags = []
             risk_score = 1.0
             entail_prob = None
 
-            # 1. Check confidence
             if event.confidence < self._thresholds['confidence']:
                 flags.append('low_confidence')
                 risk_score *= 0.5
 
-            # 2. Check NLI entailment (if available)
             if event.prompt and event.response and self.nli.pipeline is not None:
                 entail_prob = self.nli.check(event.prompt, event.response)
                 if entail_prob is not None and entail_prob < self._thresholds['entailment']: