feat : 代码梳理 移除所有敏感密钥 通过环境变量方式配置
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"""
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推荐接口实现
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实现探索/利用分支、向量检索、Softmax抽样等功能
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"""
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import logging
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import math
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import random
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import numpy as np
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from typing import List, Dict, Optional
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from app.service.recommendation_system.config import RECOMMENDATION_CONFIG, REDIS_KEY_USER_PREF_PREFIX
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from app.service.recommendation_system.milvus_client import search_similar_vectors, query_random_candidates
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from app.service.recommendation_system.precompute import compute_user_preference_vector
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from app.service.recommendation_system.vector_utils import normalize_vector
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from app.service.utils.redis_utils import Redis
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import json
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logger = logging.getLogger(__name__)
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def get_user_preference_vector(user_id: int, category: str) -> Optional[np.ndarray]:
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"""
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获取用户偏好向量
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Args:
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user_id: 用户ID
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category: 类别
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Returns:
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用户偏好向量（2048维），失败返回 None
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"""
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# 1. 从 Redis 获取
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key = f"{REDIS_KEY_USER_PREF_PREFIX}:{user_id}:{category}"
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vector_json = Redis.read(key)
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if vector_json:
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try:
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vector_list = json.loads(vector_json)
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return np.array(vector_list, dtype=np.float32)
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except Exception as e:
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logger.warning(f"解析 Redis 向量失败 [user={user_id}, category={category}]: {e}")
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# 2. 如果不存在，实时计算
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logger.info(f"Redis 中不存在用户偏好向量，实时计算 [user={user_id}, category={category}]")
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preference_vector = compute_user_preference_vector(user_id, category)
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if preference_vector is not None:
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# 写入 Redis
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vector_json = json.dumps(preference_vector.tolist())
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Redis.write(
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key=key,
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value=vector_json,
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expire=RECOMMENDATION_CONFIG["redis_expire_seconds"]
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)
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return preference_vector
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def explore_branch(category: str, style: Optional[str] = None) -> List[str]:
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"""
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探索分支（随机推荐）
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Args:
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category: 类别
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style: 风格（可选）
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Returns:
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推荐结果列表，每个元素包含 path, style, category 等字段
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"""
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# 查询候选（随机池）
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pool_size = 10 # 固定查询10个，然后随机选择
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candidates = query_random_candidates(category, style, limit=pool_size)
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if not candidates:
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logger.warning(f"探索分支：类别 {category} 没有候选数据")
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return []
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# 随机选择
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if len(candidates) > 1:
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import random
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candidates = random.sample(candidates, 1)
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# 格式化返回结果
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return [candidate.get("path", "") for candidate in candidates[:1]]
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def exploit_branch(
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user_id: int,
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category: str,
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style: Optional[str] = None
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) -> List[str]:
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"""
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利用分支（基于向量相似度推荐）
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Args:
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user_id: 用户ID
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category: 类别
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num_recommendations: 返回数量
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style: 风格（可选，用于加分）
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Returns:
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推荐结果列表，每个元素包含 path, style, category, similarity, sample_score 等字段
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"""
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# 1. 获取用户偏好向量
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embedding = get_user_preference_vector(user_id, category)
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if embedding is None:
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logger.warning(f"利用分支：无法获取用户偏好向量，回退到探索分支 [user={user_id}, category={category}]")
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return explore_branch(category, style)
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# 2. Milvus 相似度检索（内积 IP）
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topk = RECOMMENDATION_CONFIG["topk"]
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results = search_similar_vectors(embedding, category, topk)
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if not results:
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logger.warning(f"利用分支：向量检索无结果，回退到探索分支 [user={user_id}, category={category}]")
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return explore_branch(category, style)
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# 3. Style 加分（可选，需传入 style 参数）
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style_bonus = RECOMMENDATION_CONFIG["style_bonus"]
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if style:
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for result in results:
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similarity = result["score"]
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if result.get("style") == style:
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# 加分：相似度 * (1 + style_bonus)
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similarity = similarity * (1 + style_bonus)
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result["final_score"] = similarity
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else:
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for result in results:
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result["final_score"] = result["score"]
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# 4. Softmax 抽样
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scores = [r["final_score"] for r in results]
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probabilities = softmax_with_temperature(scores, RECOMMENDATION_CONFIG["softmax_temperature"])
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# 根据概率抽样
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if not results:
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return []
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selected_index = np.random.choice(len(results), size=1, p=probabilities, replace=False)
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selected_results = [results[int(selected_index[0])]]
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# 5. 返回结果
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return [result.get("path", "") for result in selected_results]
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def softmax_with_temperature(scores: List[float], temperature: float = 1.0) -> List[float]:
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"""
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Softmax 函数（带温度参数）
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Args:
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scores: 分数列表
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temperature: 温度参数
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Returns:
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概率列表
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"""
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if not scores:
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return []
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# 除以温度
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scaled_scores = [s / temperature for s in scores]
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# 减去最大值（数值稳定性）
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max_score = max(scaled_scores)
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exp_scores = [math.exp(s - max_score) for s in scaled_scores]
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# 归一化
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sum_exp = sum(exp_scores)
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if sum_exp == 0:
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# 如果所有分数都是负无穷或非常小，返回均匀分布
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return [1.0 / len(scores)] * len(scores)
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probabilities = [exp_s / sum_exp for exp_s in exp_scores]
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return probabilities
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def get_recommendations(
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user_id: int,
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category: str,
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style: Optional[str] = None
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) -> List[str]:
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"""
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获取推荐结果（主函数）
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Args:
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user_id: 用户ID
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category: 类别（如 female_skirt）
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num_recommendations: 返回推荐数量（默认 1）
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style: 风格（可选）：若传入，则在利用分支对同 style 的候选进行加分
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Returns:
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推荐结果列表，每个元素包含 path 等字段
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"""
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try:
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# 1. 读取配置参数
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explore_ratio = RECOMMENDATION_CONFIG["explore_ratio"]
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# 2. 探索/利用决策
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r = random.random() # 生成随机数 (0-1)
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if r < explore_ratio:
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logger.debug(f"探索分支 [user={user_id}, category={category}]")
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return explore_branch(category, style)
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logger.debug(f"利用分支 [user={user_id}, category={category}]")
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return exploit_branch(user_id, category, style)
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except Exception as e:
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logger.error(f"获取推荐结果失败 [user={user_id}, category={category}]: {e}", exc_info=True)
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# 容错：回退到探索分支
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return explore_branch(category, style)
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