Merge remote-tracking branch 'origin/develop' into develop

app/api/api_recommendation.py

@@ -137,7 +137,7 @@ router = APIRouter()
 #         logger.error(f"推荐失败: {str(e)}", exc_info=True)
 #         raise HTTPException(status_code=500, detail=str(e))
 
-# @router.on_event("startup")
+@router.on_event("startup")
 async def startup_event():
     """启动时初始化增量监听任务"""
     try:
@@ -173,3 +173,31 @@ async def recommend(
     except Exception as e:
         logger.error("新版推荐接口失败 [user=%s, category=%s]: %s", user_id, category, e, exc_info=True)
         raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/redis/user_pref")
+async def get_all_user_preferences():
+    """
+    获取所有以 user_pref 为前缀的 Redis key 信息
+    """
+    try:
+        from app.service.utils.redis_utils import Redis
+        from app.service.recommendation_system.config import REDIS_KEY_USER_PREF_PREFIX
+        
+        # 扫描所有匹配 user_pref:* 的 key
+        pattern = f"{REDIS_KEY_USER_PREF_PREFIX}:*"
+        keys = Redis.scan_keys(pattern)
+        
+        # 直接返回所有 key 和原始 value
+        result = {}
+        for key in keys:
+            # 读取对应的值
+            value = Redis.read(key)
+            if value:
+                result[key] = value
+        
+        return result
+        
+    except Exception as e:
+        logger.error("获取用户偏好数据失败: %s", e, exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))

app/api/api_route.py

@@ -7,6 +7,7 @@ from app.api import api_design_pre_processing
 from app.api import api_generate_image
 from app.api import api_mannequins_edit
 from app.api import api_pose_transform
+from app.api import api_precompute
 from app.api import api_prompt_generation
 from app.api import api_recommendation
 from app.api import api_test
@@ -21,6 +22,7 @@ router.include_router(api_prompt_generation.router, tags=['prompt_generation'],
 router.include_router(api_design_pre_processing.router, tags=['design_pre_processing'], prefix="/api")
 router.include_router(api_brand_dna.router, tags=['api_brand_dna'], prefix="/api")
 router.include_router(api_recommendation.router, tags=['api_recommendation'], prefix="/api")
+router.include_router(api_precompute.router, tags=['api_precompute'], prefix="/api")
 router.include_router(api_mannequins_edit.router, tags=['api_mannequins_edit'], prefix="/api")
 router.include_router(api_pose_transform.router, tags=['api_pose_transform'], prefix="/api")
 router.include_router(api_clothing_seg.router, tags=['api_clothing_seg'], prefix="/api")

app/service/recommendation_system/config.py

@@ -14,7 +14,7 @@ REDIS_KEY_USER_PREF_PREFIX = "user_pref"
 RECOMMENDATION_CONFIG = {
     # 时间衰减半衰期（用于计算时间衰减权重）
     # 值越小，最近的行为权重越大
-    "K_half": 20,
+    "K_half": 10,
 
     # 探索与利用的比例 (0.0-1.0)
     # - 值越大，使用探索分支（随机推荐）的几率越大，结果更随机
@@ -25,7 +25,7 @@ RECOMMENDATION_CONFIG = {
     # 向量检索返回的候选数量
     # 值越大，候选池越大，但计算成本也越高
     # 建议范围: 100-1000
-    "topk": 1000,
+    "topk": 200,
 
     # Style 加分系数（同 style 的候选进行加分）
     # 值越大，匹配 style 的候选被选中的概率越大
@@ -53,7 +53,7 @@ RECOMMENDATION_CONFIG = {
 }
 
 # 数据库表名
-TABLE_USER_PREFERENCE_LOG = "user_preference_log_test"
+TABLE_USER_PREFERENCE_LOG = "user_preference"
 TABLE_SYS_FILE = "t_sys_file"
 
 # MySQL 连接配置（用于推荐系统）

app/service/recommendation_system/incremental_listener.py

@@ -1,6 +1,6 @@
 """
 增量监听模块
-实时监听 user_preference_log_test 表的新增记录，更新用户偏好向量
+实时监听 user_preference 表的新增记录，更新用户偏好向量
 """
 import logging
 import math
@@ -48,7 +48,7 @@ class IncrementalListener:
             if self.last_process_time is None:
                 # 第一次运行，查询最近30分钟的数据
                 cursor.execute(f"""
-                    SELECT id, account_id, path, category, style, data_time, is_system_sketch, sys_file_id
+                    SELECT id, account_id, path, category, style, data_time
                     FROM {TABLE_USER_PREFERENCE_LOG}
                     WHERE data_time > DATE_SUB(NOW(), INTERVAL 30 MINUTE)
                     ORDER BY data_time
@@ -56,7 +56,7 @@ class IncrementalListener:
             else:
                 # 基于上次处理时间查询
                 cursor.execute(f"""
-                    SELECT id, account_id, path, category, style, data_time, is_system_sketch, sys_file_id
+                    SELECT id, account_id, path, category, style, data_time
                     FROM {TABLE_USER_PREFERENCE_LOG}
                     WHERE data_time > %s
                     ORDER BY data_time
@@ -258,7 +258,7 @@ class IncrementalListener:
                     }
                 else:
                     # 用户图
-                    # 从 user_preference_log_test 获取 category（如果有）
+                    # 从 user_preference 获取 category（如果有）
                     cursor.execute(f"""
                         SELECT category 
                         FROM {TABLE_USER_PREFERENCE_LOG} 

app/service/recommendation_system/milvus_client.py

@@ -203,7 +203,8 @@ def search_similar_vectors(
         query_vector: np.ndarray,
         category: str,
         topk: int = 500,
-        style: Optional[str] = None
+        style: Optional[str] = None,
+        style_boost_ratio: float = 0.2
 ) -> List[Dict]:
     """
     向量相似度检索
@@ -212,7 +213,8 @@ def search_similar_vectors(
         query_vector: 查询向量（2048维）
         category: 类别过滤
         topk: 返回数量
-        style: 风格过滤（可选）
+        style: 风格过滤（可选）- 当提供时，会给对应style的结果加分
+        style_boost_ratio: 风格加分比例（默认0.1，即10%）
 
     Returns:
         检索结果列表，每个元素包含 path, score, style, category 等字段
@@ -220,22 +222,55 @@ def search_similar_vectors(
     client = get_milvus_client()
 
     try:
-        # 构建过滤表达式
+        # 如果没有指定style，使用原始逻辑
-        # 使用 filter 参数而不是 expr（根据 pymilvus MilvusClient API）
+        if not style:
-        filter_expr = f"category == '{category}' && deprecated == 0"
+            filter_expr = f"category == '{category}' && deprecated == 0"
-        if style:
+            results = client.search(
-            filter_expr += f" && style == '{style}'"
+                collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
+                data=[query_vector.tolist()],
+                anns_field="feature_vector",
+                search_params={"metric_type": "IP", "params": {"nprobe": 10}},
+                limit=topk,
+                filter=filter_expr,
+                output_fields=["path", "style", "category", "sys_file_id"]
+            )
+        else:
+            # 有style参数时，使用两阶段搜索策略
 
-        # 搜索
+            # 第一阶段：搜索匹配style的向量，使用boosted query vector
-        results = client.search(
+            filter_expr_style = f"category == '{category}' && deprecated == 0 && style == '{style}'"
-            collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
+            boosted_query = query_vector * (1 + style_boost_ratio)
-            data=[query_vector.tolist()],
+            results_style = client.search(
-            anns_field="feature_vector",
+                collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
-            search_params={"metric_type": "IP", "params": {"nprobe": 10}},
+                data=[boosted_query.tolist()],
-            limit=topk,
+                anns_field="feature_vector",
-            filter=filter_expr,
+                search_params={"metric_type": "IP", "params": {"nprobe": 10}},
-            output_fields=["path", "style", "category", "sys_file_id"]
+                limit=topk,
-        )
+                filter=filter_expr_style,
+                output_fields=["path", "style", "category", "sys_file_id"]
+            )
+
+            # 第二阶段：搜索其他style的向量
+            filter_expr_others = f"category == '{category}' && deprecated == 0 && style != '{style}'"
+            results_others = client.search(
+                collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
+                data=[query_vector.tolist()],
+                anns_field="feature_vector",
+                search_params={"metric_type": "IP", "params": {"nprobe": 10}},
+                limit=topk,
+                filter=filter_expr_others,
+                output_fields=["path", "style", "category", "sys_file_id"]
+            )
+
+            # 合并结果
+            results = []
+            if results_style and len(results_style) > 0:
+                results.extend(results_style[0])
+            if results_others and len(results_others) > 0:
+                results.extend(results_others[0])
+
+            # 转换为单个结果列表格式
+            results = [results] if results else []
 
         # 格式化结果
         formatted_results = []
@@ -249,7 +284,10 @@ def search_similar_vectors(
                     "sys_file_id": hit.get("entity", {}).get("sys_file_id")
                 })
 
-        return formatted_results
+        # 按分数排序并返回topk
+        formatted_results.sort(key=lambda x: x["score"], reverse=True)
+        return formatted_results[:topk]
+
     except Exception as e:
         logger.error(f"向量检索失败: {e}", exc_info=True)
         return []
@@ -280,7 +318,7 @@ def query_random_candidates(category: str, style: Optional[str] = None, limit: i
             collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
             filter=filter_expr,
             output_fields=["path", "style", "category"],
-            limit=10000  # 先查询大量数据，然后随机选择
+            limit=10000
         )
 
         # 随机选择

app/service/recommendation_system/precompute.py

@@ -6,6 +6,7 @@ import logging
 import math
 import pymysql
 import numpy as np
+from datetime import datetime
 from typing import List, Dict, Tuple, Optional
 from collections import defaultdict
 
@@ -25,7 +26,7 @@ logger = logging.getLogger(__name__)
 
 def optimize_database_table():
     """
-    优化 user_preference_log_test 表结构
+    优化 user_preference 表结构
     添加冗余字段和索引
     """
     conn = None
@@ -317,8 +318,8 @@ def precompute_system_sketch_vectors(batch_size: int = 1000, retry_times: int =
 def compute_user_preference_vector(
     account_id: int,
     category: str,
-    conn: Optional[pymysql.connections.Connection] = None
+    conn: Optional[pymysql.connections.Connection] = None,
-    # max_date: Optional[datetime] = None
+    max_date: Optional[datetime] = None
 ) -> Optional[np.ndarray]:
     """
     计算用户偏好向量
@@ -419,8 +420,8 @@ def compute_user_preference_vector(
             p_i = 1 + math.log(1 + like_count)
             
             # 综合权重
-            # w_i = d_k * p_i
+            w_i = d_k * p_i
-            w_i = p_i
+            # w_i = p_i
 
             vectors.append(feature_vector)
             weights.append(w_i)
@@ -518,16 +519,16 @@ def run_precompute():
     logger.info("=" * 50)
     
     # 1. 优化数据库表结构
-    logger.info("\n[1/5] 优化数据库表结构...")
+    # logger.info("\n[1/5] 优化数据库表结构...")
-    optimize_database_table()
+    # optimize_database_table()
     
     # # 2. 创建 Milvus 集合
     # logger.info("\n[2/5] 创建 Milvus 集合...")
     # create_collection()
     
     # 3. 历史数据迁移
-    logger.info("\n[3/5] 历史数据迁移...")
+    # logger.info("\n[3/5] 历史数据迁移...")
-    migrate_historical_data()
+    # migrate_historical_data()
     
     # # 4. 系统图向量预计算
     # logger.info("\n[4/5] 系统图向量预计算...")
@@ -543,13 +544,13 @@ def run_precompute():
 
 
 if __name__ == "__main__":
-    # 1. 优化数据库表结构
+    # # 1. 优化数据库表结构
-    logger.info("\n[1/5] 优化数据库表结构...")
+    # logger.info("\n[1/5] 优化数据库表结构...")
-    optimize_database_table()
+    # optimize_database_table()
-
+    #
-    # 3. 历史数据迁移
+    # # 3. 历史数据迁移
-    logger.info("\n[3/5] 历史数据迁移...")
+    # logger.info("\n[3/5] 历史数据迁移...")
-    migrate_historical_data()
+    # migrate_historical_data()
 
     # 5. 初始用户偏好向量生成
     logger.info("\n[5/5] 初始用户偏好向量生成...")

app/service/utils/redis_utils.py

@@ -91,6 +91,21 @@ class Redis(object):
         r = cls._get_r()
         r.expire(name, expire_in_seconds)
 
+    @classmethod
+    def scan_keys(cls, pattern="*"):
+        """
+        扫描匹配模式的key
+        """
+        r = cls._get_r()
+        keys = []
+        cursor = 0
+        while True:
+            cursor, partial_keys = r.scan(cursor, match=pattern, count=1000)
+            keys.extend(partial_keys)
+            if cursor == 0:
+                break
+        return [key.decode('utf-8') if isinstance(key, bytes) else key for key in keys]
+
 
 if __name__ == '__main__':
     redis_client = Redis()