Update src/utils/response_verifier.py

src/utils/response_verifier.py

@@ -1,170 +1,3 @@
-File: src\utils\response_verifier.py
-````````python
-from typing import Dict, Any, List, Optional
-from ..knowledge_base.grounding_truth import GroundingTruth
-from ..components.response_templates import get_response_templates
-import logging
-
-logger = logging.getLogger(__name__)
-
-class ResponseVerifier:
-    """Verifies responses using grounding truth while preserving multi-perspective analysis"""
-    
-    def __init__(self):
-        self.grounding_truth = GroundingTruth()
-        self.response_templates = get_response_templates()
-        self.mode_confidence_thresholds = {
-            "scientific": 0.9,
-            "creative": 0.7,
-            "emotional": 0.6,
-            "quantum": 0.8,
-            "philosophical": 0.7
-        }
-        
-    def verify_insight(
-        self, 
-        insight: str, 
-        mode: str,
-        context: Optional[Dict[str, Any]] = None
-    ) -> Dict[str, Any]:
-        """
-        Verify a single insight from a specific perspective mode
-        
-        Args:
-            insight: The insight to verify
-            mode: The perspective mode that generated it
-            context: Optional additional context
-            
-        Returns:
-            Dict containing verification results and confidence
-        """
-        verification = self.grounding_truth.verify_statement(insight, context)
-        confidence_threshold = self.mode_confidence_thresholds.get(mode, 0.7)
-        
-        # Adjust verification based on mode characteristics
-        if mode == "creative":
-            # Allow more speculative statements in creative mode
-            verification["confidence"] *= 1.2
-        elif mode == "quantum":
-            # Account for quantum uncertainty
-            verification["confidence"] = max(
-                verification["confidence"],
-                0.5  # Quantum superposition baseline
-            )
-            
-        return {
-            "verified": verification["verified"] or verification["confidence"] >= confidence_threshold,
-            "confidence": verification["confidence"],
-            "mode": mode,
-            "original_insight": insight,
-            "requires_qualifier": verification["confidence"] < confidence_threshold
-        }
-        
-    def process_multi_perspective_response(
-        self,
-        insights: List[str],
-        modes: List[str],
-        consciousness_state: Optional[Dict[str, Any]] = None
-    ) -> Dict[str, Any]:
-        """
-        Process and verify a multi-perspective response
-        
-        Args:
-            insights: List of insights from different perspectives
-            modes: List of perspective modes that generated the insights
-            consciousness_state: Optional consciousness state info
-            
-        Returns:
-            Processed response with verification metadata
-        """
-        verified_insights = []
-        uncertain_insights = []
-        
-        for insight, mode in zip(insights, modes):
-            result = self.verify_insight(
-                insight, 
-                mode, 
-                {"consciousness": consciousness_state} if consciousness_state else None
-            )
-            
-            if result["verified"]:
-                verified_insights.append({
-                    "text": result["original_insight"],
-                    "mode": mode,
-                    "confidence": result["confidence"]
-                })
-            else:
-                qualified_insight = self._add_qualifier(
-                    result["original_insight"],
-                    result["confidence"],
-                    mode
-                )
-                uncertain_insights.append({
-                    "text": qualified_insight,
-                    "mode": mode,
-                    "confidence": result["confidence"]
-                })
-                
-        return {
-            "verified_insights": verified_insights,
-            "uncertain_insights": uncertain_insights,
-            "overall_confidence": self._calculate_overall_confidence(
-                verified_insights, uncertain_insights
-            )
-        }
-        
-    def _add_qualifier(self, insight: str, confidence: float, mode: str) -> str:
-        """Add appropriate qualifier based on confidence and mode"""
-        base_qualifier = self._get_base_qualifier(confidence)
-        mode_specific = self._get_mode_qualifier(mode)
-        
-        if mode_specific:
-            return f"{base_qualifier} {insight} ({mode_specific})"
-        return f"{base_qualifier} {insight}"
-        
-    def _get_base_qualifier(self, confidence: float) -> str:
-        """Get base confidence qualifier"""
-        if confidence >= 0.8:
-            return "Evidence suggests that"
-        elif confidence >= 0.6:
-            return "It appears that"
-        elif confidence >= 0.4:
-            uncertain = self.response_templates.get_uncertain_prefix()
-            return uncertain.rstrip(',')  # Remove trailing comma if present
-        elif confidence >= 0.2:
-            return "There is a speculation that"
-        else:
-            return "There is an unverified hypothesis that"
-            
-    def _get_mode_qualifier(self, mode: str) -> Optional[str]:
-        """Get mode-specific qualifier"""
-        qualifiers = {
-            "scientific": "based on available data",
-            "creative": "from a creative perspective",
-            "emotional": "from an emotional standpoint",
-            "quantum": "considering quantum possibilities",
-            "philosophical": "from a philosophical view"
-        }
-        return qualifiers.get(mode)
-        
-    def _calculate_overall_confidence(
-        self,
-        verified: List[Dict[str, Any]],
-        uncertain: List[Dict[str, Any]]
-    ) -> float:
-        """Calculate overall response confidence"""
-        if not verified and not uncertain:
-            return 0.0
-            
-        total_insights = len(verified) + len(uncertain)
-        confidence_sum = sum(v["confidence"] for v in verified)
-        confidence_sum += sum(u["confidence"] for u in uncertain)
-        
-        return confidence_sum / total_insights
-````````
-
-# Response
-````````python
 from typing import Dict, Any, List, Optional
 from ..knowledge_base.grounding_truth import GroundingTruth
 from ..components.response_templates import get_response_templates