新增 取消agent配饰（保留鞋子）推荐，改为默认随机配饰搭配 使用json文件补充stylist删除掉的必要配饰

app/server/ChatbotAgent/core/stylist_agent_server.py

@@ -296,21 +296,36 @@ class AsyncStylistAgent:
     async def _get_random_accessories(self, stylist, item_count):
         stylist_item = []
         stylist_item_ids = []
+
+        filter_items = [
+            {"item_group_id": {"$ne": "Clothing"}},
+            {"item_group_id": {"$ne": "Shoes"}},
+            {"modality": "image"}
+        ]
+        random_items = []
+
         for i in stylist:
             # 1. 根据stylist要求抽取item
             query_embedding = self.local_db.get_clip_embedding(i['text'], is_image=False)
             stylist_results = self.local_db.query_local_db(query_embedding, i['category'], n_results=10)
             stylist_item += random.choices(stylist_results['metadatas'][0], k=i['count'])
             stylist_item_ids += [item_id['item_id'] for item_id in stylist_item]
+            filter_items.append({"item_group_id": {"$ne": i['category']}})
 
         accessories_count = 9 - item_count - len(stylist_item)
 
         if accessories_count > 0:
-            accessories_count = random.randint(1, accessories_count)
+            if accessories_count > 4:
-            # 2. 在配饰池中过滤掉已经选中的item ，然后抽两个item
+                accessories_count = 4
-            random_single_ids = random.choices(list(set(self.local_db.cache_filtered_ids) - set([f"{i}_img" for i in stylist_item_ids])), k=accessories_count)
+            for i in range(accessories_count):
-            random_items = self.local_db.random_get_accessories(random_single_ids)['metadatas']
+                random_poll = self.local_db.load_filtered_ids(filter_items)
+                # 2. 在配饰池中过滤掉已经选中的item ，然后抽两个item
+                item = self.local_db.random_get_accessories(random.choice(random_poll))
+                filter_items.append({"item_group_id": {"$ne": item['metadatas'][0]['category']}})
+                random_items.append(item['metadatas'][0])
+
             all_items = stylist_item + random_items
+
         else:
             all_items = stylist_item
 

app/server/ChatbotAgent/core/vector_database.py

@@ -1,4 +1,5 @@
 import random
+import time
 
 import torch
 import chromadb
@@ -17,8 +18,12 @@ class VectorDatabase():
 
         self.model = CLIPModel.from_pretrained(embedding_model_name).to(self.device)
         self.processor = CLIPProcessor.from_pretrained(embedding_model_name)
-        self.cache_filtered_ids = self.load_filtered_ids()
+        # self.cache_filtered_ids = self.load_filtered_ids([
-        self.total_count = len(self.cache_filtered_ids)
+        #     {"item_group_id": {"$ne": "Clothing"}},
+        #     {"item_group_id": {"$ne": "Shoes"}},
+        #     {"modality": "image"}
+        # ])
+        # self.total_count = len(self.cache_filtered_ids)
 
     def get_clip_embedding(self, data: str | Image.Image, is_image: bool) -> List[float]:
         """生成图像或文本的 CLIP 嵌入，并进行 L2 归一化。"""
@@ -63,14 +68,11 @@ class VectorDatabase():
         )
         return results
 
-    def load_filtered_ids(self):
+    def load_filtered_ids(self, filter_item):
-        print("\n--- 初始化阶段：加载所有符合条件的 ID ---")
+        # print("\n--- 初始化阶段：加载所有符合条件的 ID ---")
+        start_time = time.time()
         FILTER_CRITERIA = {
-            "$and": [
+            "$and": filter_item
-                {"item_group_id": {"$ne": "Clothing"}},
-                {"item_group_id": {"$ne": "Shoes"}},  # 新增：过滤 Shoes
-                {"modality": "image"}
-            ]
         }
         MAX_LIMIT = 100000
 
@@ -82,7 +84,8 @@ class VectorDatabase():
                 include=[]
             )
             all_matched_ids = all_ids_results['ids']
-            print(f"🎉 成功加载 {len(all_matched_ids)} 个 ID 到缓存。")
+            # print(f"🎉 成功加载 {len(all_matched_ids)} 个 ID 到缓存。")
+            print(time.time() - start_time)
             return all_matched_ids
 
         except Exception as e:
@@ -114,20 +117,29 @@ if __name__ == '__main__':
         'count': 2,
         'category': "Jewelry"
     }
+
     max_len = 5
     local_db = VectorDatabase(vector_db_dir="/workspace/lc_stylist_agent/db", collection_name="lc_clothing_embedding", embedding_model_name="openai/clip-vit-base-patch32")
+    A = local_db.load_filtered_ids([
+        {"item_group_id": {"$ne": "Clothing"}},
+        {"item_group_id": {"$ne": "Shoes"}},
+        {"modality": "image"}
+    ])
     # print(db.random_get_accessories())
-
+    start_time = time.time()
-    query_embedding = local_db.get_clip_embedding(stylist['text'], is_image=False)
+    X = local_db.random_get_accessories(['ELI699_img'])
-
+    print(X)
-    results = local_db.query_local_db(query_embedding, stylist['category'], n_results=10)
+    print(time.time() - start_time)
-    # 2. 从结果集中抽 stylist['count'] 个item
+    # query_embedding = local_db.get_clip_embedding(stylist['text'], is_image=False)
-    stylist_item = random.choices(results['metadatas'][0], k=stylist['count'])
+    #
-    stylist_item_ids = [item_id['item_id'] for item_id in stylist_item]
+    # results = local_db.query_local_db(query_embedding, stylist['category'], n_results=10)
-
+    # # 2. 从结果集中抽 stylist['count'] 个item
-    # 3. 从随机库中抽取配饰，总数达到9件 ，需过滤掉已经抽中的item
+    # stylist_item = random.choices(results['metadatas'][0], k=stylist['count'])
-    accessories_count = 9 - max_len - stylist['count']
+    # stylist_item_ids = [item_id['item_id'] for item_id in stylist_item]
-
+    #
-    random_single_ids = random.choices(list(set(local_db.cache_filtered_ids) - set([f"{i}_img" for i in stylist_item_ids])), k=accessories_count)
+    # # 3. 从随机库中抽取配饰，总数达到9件 ，需过滤掉已经抽中的item
-    random_items = local_db.random_get_accessories(random_single_ids)['metadatas']
+    # accessories_count = 9 - max_len - stylist['count']
-    all_items = stylist_item + random_items
+    #
+    # random_single_ids = random.choices(list(set(local_db.cache_filtered_ids) - set([f"{i}_img" for i in stylist_item_ids])), k=accessories_count)
+    # random_items = local_db.random_get_accessories(random_single_ids)['metadatas']
+    # all_items = stylist_item + random_items