NexusMind

from typing import Any, Dict, List, Optional from loguru import logger # from src.asr_got_reimagined.domain.models.graph_elements import FalsificationCriteria, BiasFlag # If needed def assess_falsifiability_score( criteria: Optional[Any], ) -> float: # criteria: Optional[FalsificationCriteria] logger.warning( "Falsifiability assessment (P1.16) not fully implemented. Returning placeholder." ) return 0.5 if criteria else 0.0 def detect_potential_biases(node_data: Dict[str, Any]) -> List[Any]: # List[BiasFlag] logger.warning( "Bias detection (P1.17) not fully implemented. Returning placeholder." ) return [] def calculate_semantic_similarity(text1: str, text2: str) -> float: """P1.8: Semantic similarity for IBN creation.""" logger.warning( "Semantic similarity calculation not fully implemented. Returning placeholder." ) # Placeholder - use NLP models (e.g., sentence transformers) in a real implementation if not text1 or not text2: return 0.0 # Simple common word overlap for now words1 = set(text1.lower().split()) words2 = set(text2.lower().split()) if not words1 or not words2: return 0.0 return len(words1.intersection(words2)) / len(words1.union(words2))