新推荐接口first commit

app/service/recommendation_system/config.py

@@ -2,12 +2,7 @@
 推荐系统配置
 """
 import os
-from app.core.config import (
+from app.core.config import settings
-    DB_CONFIG, DB_HOST, DB_PORT, DB_USERNAME, DB_PASSWORD, DB_NAME,
-    REDIS_HOST, REDIS_PORT, REDIS_DB,
-    MILVUS_URL, MILVUS_TOKEN, MILVUS_ALIAS,
-    MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE
-)
 
 # Milvus 集合名称
 MILVUS_COLLECTION_SKETCH_VECTORS = "sketch_vectors_norm"
@@ -20,39 +15,39 @@ 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,
-    
+
     # 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
     # - 值越小，使用利用分支（基于用户偏好）的几率越大，结果更精准
     # - 建议范围: 0.3-0.7，要增加随机性可提高到 0.6-0.8
     "explore_ratio": 0.5,
-    
+
     # 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,
-    
+
     # 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
     # 要降低某个结果的重复率，可以降低此值（如 0.1 或 0.05）
     "style_bonus": 0.2,
-    
+
     # Softmax 抽样的温度参数
     # - 温度越高（>1.0），概率分布越均匀，结果更随机，重复率更低
     # - 温度越低（<1.0），高分项概率越大，结果更集中，重复率更高
     # - 温度=1.0 为标准 Softmax
     # - 建议范围: 1.0-3.0，要增加随机性可提高到 2.0-3.0
     "softmax_temperature": 0.07,
-    
+
     # 监听间隔（秒）
     "listen_interval_sec": 30,
-    
+
     # 批量处理大小
     "batch_size": 1000,
-    
+
     # Redis 过期时间（秒，30天）
     "redis_expire_seconds": 2592000,
-    
+
     # 向量维度
     "vector_dim": 2048,
 }
@@ -63,11 +58,10 @@ TABLE_SYS_FILE = "t_sys_file"
 
 # MySQL 连接配置（用于推荐系统）
 MYSQL_CONFIG = {
-    "host": DB_HOST,
+    "host": settings.MYSQL_HOST,
-    "port": DB_PORT,
+    "port": settings.MYSQL_PORT,
-    "user": DB_USERNAME,
+    "user": settings.MYSQL_USER,
-    "password": DB_PASSWORD,
+    "password": settings.MYSQL_PASSWORD,
-    "database": DB_NAME,
+    "database": settings.MYSQL_DB,
     "charset": "utf8mb4"
 }
-

app/service/recommendation_system/milvus_client.py

@@ -6,7 +6,7 @@ from typing import List, Dict, Optional, Any
 import numpy as np
 from pymilvus import MilvusClient, FieldSchema, CollectionSchema, DataType, connections, Collection
 
-from app.core.config import MILVUS_URL, MILVUS_TOKEN, MILVUS_ALIAS
+from app.core.config import settings
 from app.service.recommendation_system.config import MILVUS_COLLECTION_SKETCH_VECTORS, RECOMMENDATION_CONFIG
 
 logger = logging.getLogger(__name__)
@@ -21,9 +21,9 @@ def get_milvus_client() -> MilvusClient:
     if _milvus_client is None:
         try:
             _milvus_client = MilvusClient(
-                uri=MILVUS_URL,
+                uri=settings.MILVUS_URL,
-                token=MILVUS_TOKEN,
+                token=settings.MILVUS_TOKEN,
-                db_name=MILVUS_ALIAS
+                db_name=settings.MILVUS_DB,
             )
             logger.info("Milvus 客户端连接成功")
         except Exception as e:
@@ -46,32 +46,32 @@ def create_collection():
     - feature_vector (FloatVector(2048)) - 2048维特征向量
     """
     client = get_milvus_client()
-    
+
     # 检查集合是否已存在
     collections = client.list_collections()
     if MILVUS_COLLECTION_SKETCH_VECTORS in collections:
         logger.info(f"集合 {MILVUS_COLLECTION_SKETCH_VECTORS} 已存在")
         return
-    
+
     try:
         # 解析 Milvus URL
         # 处理 http://host.docker.internal:19530 格式
-        url_clean = MILVUS_URL.replace("http://", "").replace("https://", "")
+        url_clean = settings.MILVUS_URL.replace("http://", "").replace("https://", "")
         if ":" in url_clean:
             host, port_str = url_clean.split(":", 1)
             port = int(port_str)
         else:
             host = url_clean
             port = 19530
-        
+
         # 使用传统 API 创建集合（更可靠）
         # 连接到 Milvus（如果未连接）
         try:
             connections.connect(
-                alias=MILVUS_ALIAS,
+                alias=settings.MILVUS_ALIAS,
                 host=host,
                 port=port,
-                token=MILVUS_TOKEN if MILVUS_TOKEN else None
+                token=settings.MILVUS_TOKEN if settings.MILVUS_TOKEN else None
             )
             logger.info(f"已连接到 Milvus: {host}:{port}")
         except Exception as conn_e:
@@ -80,7 +80,7 @@ def create_collection():
                 logger.info("Milvus 连接已存在")
             else:
                 logger.warning(f"连接 Milvus 时出现警告: {conn_e}")
-        
+
         # 定义字段
         fields = [
             FieldSchema(name="path", dtype=DataType.VARCHAR, is_primary=True, max_length=512),
@@ -95,20 +95,20 @@ def create_collection():
                 dim=RECOMMENDATION_CONFIG["vector_dim"]
             )
         ]
-        
+
         # 创建 schema
         schema = CollectionSchema(
             fields=fields,
             description="Sketch vectors collection for recommendation system"
         )
-        
+
         # 创建集合
         collection = Collection(
             name=MILVUS_COLLECTION_SKETCH_VECTORS,
             schema=schema,
-            using=MILVUS_ALIAS
+            using=settings.MILVUS_ALIAS
         )
-        
+
         # 创建索引
         # 注意：使用 IP（内积）作为度量类型，与搜索时保持一致
         # 如果向量已归一化，IP 等价于 COSINE
@@ -117,14 +117,14 @@ def create_collection():
             "index_type": "IVF_FLAT",
             "params": {"nlist": 1024}
         }
-        
+
         collection.create_index(
             field_name="feature_vector",
             index_params=index_params
         )
-        
+
         logger.info(f"集合 {MILVUS_COLLECTION_SKETCH_VECTORS} 创建成功")
-        
+
     except Exception as e:
         logger.error(f"创建集合失败: {e}", exc_info=True)
         raise
@@ -146,9 +146,9 @@ def insert_vectors(data: List[Dict[str, Any]]):
     """
     if not data:
         return
-    
+
     client = get_milvus_client()
-    
+
     try:
         client.insert(
             collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
@@ -172,27 +172,27 @@ def query_vectors_by_paths(paths: List[str]) -> Dict[str, Dict]:
     """
     if not paths:
         return {}
-    
+
     client = get_milvus_client()
-    
+
     try:
         # 构建查询表达式
         # 使用 filter 参数而不是 expr（根据 pymilvus MilvusClient API）
         # 对于字符串列表，使用单引号包裹每个值
         path_list = ", ".join([f"'{p}'" for p in paths])
         filter_expr = f"path in [{path_list}]"
-        
+
         results = client.query(
             collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
             filter=filter_expr,
             output_fields=["path", "feature_vector", "style", "category", "sys_file_id", "is_system_sketch", "deprecated"]
         )
-        
+
         # 转换为字典
         result_dict = {}
         for r in results:
             result_dict[r["path"]] = r
-        
+
         return result_dict
     except Exception as e:
         logger.error(f"查询向量失败: {e}", exc_info=True)
@@ -200,10 +200,10 @@ def query_vectors_by_paths(paths: List[str]) -> Dict[str, Dict]:
 
 
 def search_similar_vectors(
-    query_vector: np.ndarray,
+        query_vector: np.ndarray,
-    category: str,
+        category: str,
-    topk: int = 500,
+        topk: int = 500,
-    style: Optional[str] = None
+        style: Optional[str] = None
 ) -> List[Dict]:
     """
     向量相似度检索
@@ -218,14 +218,14 @@ def search_similar_vectors(
         检索结果列表，每个元素包含 path, score, style, category 等字段
     """
     client = get_milvus_client()
-    
+
     try:
         # 构建过滤表达式
         # 使用 filter 参数而不是 expr（根据 pymilvus MilvusClient API）
         filter_expr = f"category == '{category}' && deprecated == 0"
         if style:
             filter_expr += f" && style == '{style}'"
-        
+
         # 搜索
         results = client.search(
             collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
@@ -236,7 +236,7 @@ def search_similar_vectors(
             filter=filter_expr,
             output_fields=["path", "style", "category", "sys_file_id"]
         )
-        
+
         # 格式化结果
         formatted_results = []
         if results and len(results) > 0:
@@ -248,7 +248,7 @@ def search_similar_vectors(
                     "category": hit.get("entity", {}).get("category", ""),
                     "sys_file_id": hit.get("entity", {}).get("sys_file_id")
                 })
-        
+
         return formatted_results
     except Exception as e:
         logger.error(f"向量检索失败: {e}", exc_info=True)
@@ -268,13 +268,13 @@ def query_random_candidates(category: str, style: Optional[str] = None, limit: i
         候选列表
     """
     client = get_milvus_client()
-    
+
     try:
         # 构建过滤表达式
         filter_expr = f"category == '{category}' && deprecated == 0"
         if style:
             filter_expr += f" && style == '{style}'"
-        
+
         # 查询所有符合条件的记录
         results = client.query(
             collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
@@ -282,14 +282,13 @@ def query_random_candidates(category: str, style: Optional[str] = None, limit: i
             output_fields=["path", "style", "category"],
             limit=10000  # 先查询大量数据，然后随机选择
         )
-        
+
         # 随机选择
         if len(results) > limit:
             import random
             results = random.sample(results, limit)
-        
+
         return results
     except Exception as e:
         logger.error(f"随机查询候选失败: {e}", exc_info=True)
         return []
-