from typing import List, Dict, Any import math def rerank_mentors( similar_mentors: List[Dict[str, Any]], mentee_data: Dict[str, Any], final_count: int = 8 ) -> List[Dict[str, Any]]: scored_mentors = [] for mentor in similar_mentors: score = mentor["score"] metadata = mentor.get("metadata", {}) semantic_score = score * 0.7 rating_score = _calculate_rating_score(metadata.get("rating", 0.0)) availability_score = _calculate_availability_score(metadata.get("available_slots", 0)) rule_based_score = ( rating_score * 0.5 + availability_score * 0.5 ) * 0.3 final_score = semantic_score + rule_based_score reason = _generate_reason( score, metadata, mentee_data, rating_score, availability_score ) scored_mentors.append({ "mentor_id": mentor["mentor_id"], "score": final_score, "semantic_similarity": score, "metadata": metadata, "reason": reason }) scored_mentors.sort(key=lambda x: x["score"], reverse=True) return scored_mentors[:final_count] def _calculate_rating_score(rating: float) -> float: if rating <= 0: return 0.0 return min(rating / 5.0, 1.0) def _calculate_availability_score(available_slots: int) -> float: if available_slots <= 0: return 0.0 if available_slots >= 10: return 1.0 return min(available_slots / 10.0, 1.0) def _generate_reason( semantic_score: float, metadata: Dict[str, Any], mentee_data: Dict[str, Any], rating_score: float, availability_score: float ) -> str: reasons = [] if semantic_score >= 0.8: reasons.append("Highly relevant expertise") elif semantic_score >= 0.6: reasons.append("Good match for your goals") rating = metadata.get("rating", 0.0) if rating >= 4.5: reasons.append("Excellent ratings") elif rating >= 4.0: reasons.append("High ratings") available_slots = metadata.get("available_slots", 0) if available_slots > 0: reasons.append("Has available slots") if not reasons: reasons.append("Good overall match") return "; ".join(reasons[:3])