Delete hallucination_detective.py

hallucination_detective.py

@@ -1,65 +0,0 @@
-import logging
-from typing import Dict, Any, Optional
-from agentic_reliability_framework.runtime.agents.base import BaseAgent, AgentSpecialization
-from ai_event import AIEvent
-from nli_detector import NLIDetector
-
-logger = logging.getLogger(__name__)
-
-class HallucinationDetectiveAgent(BaseAgent):
-    """
-    Detects potential hallucinations in generated text by combining:
-    - Model confidence score (lower confidence → higher risk)
-    - Natural Language Inference (NLI) entailment score (lower entailment → higher risk)
-    """
-
-    def __init__(self, nli_detector: Optional[NLIDetector] = None):
-        super().__init__(AgentSpecialization.DETECTIVE)
-        self._thresholds = {
-            'confidence': 0.7,
-            'entailment': 0.6
-        }
-        self.nli = nli_detector or NLIDetector()
-
-    async def analyze(self, event: AIEvent) -> Dict[str, Any]:
-        try:
-            flags = []
-            risk_score = 1.0
-            entail_prob = None
-
-            if event.confidence < self._thresholds['confidence']:
-                flags.append('low_confidence')
-                risk_score *= 0.5
-
-            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']:
-                    flags.append('low_entailment')
-                    risk_score *= 0.6
-
-            is_hallucination = len(flags) > 0
-
-            return {
-                'specialization': 'ai_hallucination',
-                'confidence': 1 - risk_score if is_hallucination else 0,
-                'findings': {
-                    'is_hallucination': is_hallucination,
-                    'flags': flags,
-                    'risk_score': risk_score,
-                    'confidence': event.confidence,
-                    'entailment': entail_prob
-                },
-                'recommendations': [
-                    "Regenerate with lower temperature",
-                    "Provide more context",
-                    "Use a different model"
-                ] if is_hallucination else []
-            }
-        except Exception as e:
-            logger.error(f"HallucinationDetective error: {e}", exc_info=True)
-            return {
-                'specialization': 'ai_hallucination',
-                'confidence': 0.0,
-                'findings': {},
-                'recommendations': []
-            }