AI配餐助手原理与实战：从推荐算法到LLM生成完整指南

 传统方式：基于简单规则的手工配餐
def traditional_meal_recommend(user_id):
     规则1：中午12点推荐午餐类
    if datetime.now().hour == 12:
        recommend_list = ["黄焖鸡米饭", "麻辣烫", "牛肉面"]
    else:
        recommend_list = ["粥", "包子", "馄饨"]
    
     规则2：基于用户历史，找点过最多的分类
    user_history = get_user_orders(user_id)
    most_category = Counter([h['category'] for h in user_history]).most_common(1)[0][0]
    filtered = [item for item in recommend_list if category_map[item] == most_category]
    
    return filtered if filtered else recommend_list[:3]   兜底逻辑

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from typing import List, Dict, Any

class AIPlanningAssistant:
    """AI配餐助手核心类：实现混合推荐 + 营养约束过滤"""
    
    def __init__(self, recipe_db: List[Dict], user_profile: Dict):
        self.recipe_db = recipe_db           菜品库，每条含营养标签
        self.user_profile = user_profile     用户画像（口味+健康目标）
        self.nutrition_constraints = {       营养约束阈值（示例）
            'max_calories': user_profile.get('daily_calorie_target', 2000) / 3,
            'min_protein': 20,               每餐最低蛋白质(g)
            'forbidden_ingredients': user_profile.get('allergies', [])
        }
    
    def collaborative_filter_recall(self, top_k: int = 50) -> List[Dict]:
        """协同过滤召回：基于相似用户的历史偏好"""
         此处省略用户相似度矩阵计算的完整实现
         核心思路：找到与当前用户口味最相似的N个用户，取他们高频选择的菜品
        pass
    
    def content_based_recall(self, top_k: int = 50) -> List[Dict]:
        """内容召回：基于菜品标签与用户偏好的相似度"""
        user_vector = np.array(self.user_profile['preference_vector'])
        candidates = []
        for recipe in self.recipe_db:
            recipe_vector = np.array(recipe['feature_vector'])
            sim = cosine_similarity([user_vector], [recipe_vector])[0][0]
            candidates.append((recipe, sim))
        candidates.sort(key=lambda x: x[1], reverse=True)
        return [item[0] for item in candidates[:top_k]]
    
    def nutrition_filter(self, candidates: List[Dict]) -> List[Dict]:
        """营养约束过滤：剔除不符合健康标准的候选菜品"""
        filtered = []
        for recipe in candidates:
             检查过敏原
            if any(ing in recipe['ingredients'] 
                   for ing in self.nutrition_constraints['forbidden_ingredients']):
                continue
             检查热量上限
            if recipe['calories'] > self.nutrition_constraints['max_calories']:
                continue
             检查蛋白质下线
            if recipe['protein_g'] < self.nutrition_constraints['min_protein']:
                continue
            filtered.append(recipe)
        return filtered
    
    def llm_enhanced_rerank(self, candidates: List[Dict]) -> List[Dict]:
        """LLM精排：调用大模型进行上下文感知的重排序"""
         实际生产环境调用API（如通义千问、DeepSeek）
         此处展示调用逻辑框架
        prompt = f"""
        用户偏好：{self.user_profile['preference_description']}
        当日上下文：{self.user_profile['context']}
        候选菜品：{[c['name'] for c in candidates[:10]]}
        请从营养均衡、口味搭配、烹饪复杂度三个维度，对以上菜品进行排序，
        返回排序后的菜品名称列表。
        """
         llm_response = call_llm_api(prompt)   实际调用
         reranked_names = parse_llm_response(llm_response)
         return [c for c in candidates if c['name'] in reranked_names]
        return candidates   示例中暂不做实际调用
    
    def recommend(self) -> List[Dict]:
        """完整推荐流程：召回 → 过滤 → 精排"""
         第一步：多路召回（协同过滤 + 内容召回）
        recall_candidates = []
        recall_candidates.extend(self.collaborative_filter_recall(top_k=30))
        recall_candidates.extend(self.content_based_recall(top_k=30))
        recall_candidates = list({r['id']: r for r in recall_candidates}.values())
        
         第二步：营养约束过滤
        filtered_candidates = self.nutrition_filter(recall_candidates)
        
         第三步：LLM精排优化
        final_recommendations = self.llm_enhanced_rerank(filtered_candidates)
        
        return final_recommendations[:5]   返回Top-5推荐


 使用示例
if __name__ == "__main__":
     初始化菜品库和用户画像
    recipe_db = [...]       从数据库加载
    user_profile = {
        'preference_vector': [0.8, 0.2, 0.5, 0.1],   口味偏好向量
        'daily_calorie_target': 2000,
        'allergies': ['花生', '海鲜'],
        'preference_description': '喜欢川菜、微辣、不吃香菜',
        'context': '今日气温28度，下午有会议需要保持精力'
    }
    
    assistant = AIPlanningAssistant(recipe_db, user_profile)
    recommendations = assistant.recommend()
    
    print("AI配餐助手为您推荐：")
    for idx, rec in enumerate(recommendations, 1):
        print(f"{idx}. {rec['name']} | 热量: {rec['calories']}kcal | 蛋白质: {rec['protein_g']}g")