新增 取消agent配饰推荐，改为默认随机配饰搭配

app/server/ChatbotAgent/core/stylist_agent_server.py

@@ -19,7 +19,16 @@ logger = logging.getLogger(__name__)
 
 
 class AsyncStylistAgent:
-    CATEGORY_SET = {'Activewear', 'Watches', 'Shopping Totes', 'Underwear', 'Sunglasses', 'Dresses', 'Outerwear', 'Handbags', 'Backpacks', 'Belts', 'Hats', 'Skirts', 'Swimwear', 'Jewelry', 'Briefcases', 'Socks', 'Neckties', 'Pants', 'Suits', 'Shoes', 'Shirts & Tops', 'Scarves & Shawls'}
+    CATEGORY_SET = {
+        'Activewear', 'Dresses', 'Outerwear', 'Pants', 'Shirts & Tops', 'Skirts', 'Suits', 'Swimwear', 'Underwear'
+        # 取消推荐配饰
+        # , 'Watches', 'Shopping Totes', 'Sunglasses', 'Handbags', 'Backpacks', 'Belts', 'Hats', 'Jewelry', 'Briefcases', 'Socks', 'Neckties', 'Shoes', 'Scarves & Shawls'
+    }
+    CATEGORY_SET_ALL = {
+        'Activewear', 'Dresses', 'Outerwear', 'Pants', 'Shirts & Tops', 'Skirts', 'Suits', 'Swimwear', 'Underwear',
+        'Watches', 'Shopping Totes', 'Sunglasses', 'Handbags', 'Backpacks', 'Belts', 'Hats', 'Jewelry',
+        'Briefcases', 'Socks', 'Neckties', 'Shoes', 'Scarves & Shawls'
+    }
 
     def __init__(self, local_db, max_len: int, gemini_model_name: str, outfit_id=str):
         # self.outfit_items: List[Dict[str, str]] = []
@@ -99,8 +108,8 @@ class AsyncStylistAgent:
 
         ## Your Workflow and Constraints
 
-        1.  **Style Adherence**: You must strictly observe all rules in the Style Guide concerning **color palette, fit, layering principles, pattern restrictions, accessory stacking, and shoe/bag coordination**.
-        2.  **Step Planning**: The styling sequence must follow a **top-down, inside-out** approach: First major garments (tops/outerwear/bottoms/dresses), then shoes and bags, and finally accessories.
+        1.  **Style Adherence**: You must strictly observe all rules in the Style Guide concerning **color palette, fit, layering principles, pattern restrictions coordination**.
+        2.  **Step Planning**: The styling sequence must follow a **top-down, inside-out** approach:  major garments (tops/outerwear/bottoms/dresses).
         3.  **Structured Output**: Every response must recommend the **next single item**. You must strictly use the **JSON format** for your output, as follows:
 
         ```json
@@ -118,7 +127,6 @@ class AsyncStylistAgent:
             * **Fit/Silhouette** (e.g., Oversize, loose, slim-fit)
             * **Material/Detail** (e.g., 100% cotton, linen, gold clasp, thin stripe, checkered pattern)
             * **Role in the Outfit** (e.g., serves as the innermost base layer for layering; acts as the crucial tie accent for the smart casual look)
-            * **[CRITICAL FOR JEWELRY] If recommending 'Jewelry' (especially Necklaces), the description must specify its distinction (length, thickness, pendant style) from all previously selected necklaces to ensure layered variety.**
 
         4.  **Termination Condition**: Only when you deem the entire outfit complete and **all mandatory elements stipulated in the Style Guide are met**, you must output the following JSON format to terminate the process:
 
@@ -156,7 +164,7 @@ class AsyncStylistAgent:
         # self._clear_uploaded_files()
         # 1. 添加图片内容
         if self.outfit_items:
-            merged_image = merge_images_to_square(self.outfit_items, max_len=self.max_len, add_text=False)
+            merged_image = merge_images_to_square(self.outfit_items, max_len=self.max_len + 1, add_text=False)
             image_bytes_io = io.BytesIO()
             image_format = 'JPEG'
             mime_type = 'image/jpeg'
@@ -252,6 +260,21 @@ class AsyncStylistAgent:
             print(f"An error occurred during item retrieval: {e}")
             return None
 
+    def _get_random_accessories(self):
+        results = self.local_db.random_get_accessories()
+
+        # 3. 模拟 Agent 审核(实际应用中,你需要将图片发回给 Agent进行审核)
+        best_meta = results['metadatas'][0]
+        return {
+            "item_id": best_meta['item_id'],  # 从 metadata 字典中安全获取
+            "category": best_meta['category'],
+            "gpt_description": best_meta['description'],
+            'description': best_meta['description'],
+            # 假设 'item_path' 存储在 metadata 中，或从 'item_id' 推导
+            # 这里假设 item_id 就是文件名的一部分
+            "image_path": os.path.join(f"{best_meta['item_id']}.jpg")
+        }
+
     def _build_user_input(self) -> str:
         """构建发送给 Gemini 的用户输入,包含已选单品信息。"""
         if not self.outfit_items:
@@ -313,7 +336,20 @@ class AsyncStylistAgent:
 
             # 3. 检查终止条件
             if gemini_data.get('action') == 'stop':
-                print(f"🛑 搭配完成,终止原因: {gemini_data.get('reason')}")
+                response_data['path'] = minio_path
+                response_data['items'].append({"item_id": item_id, "category": item_category})
+                response_data['status'] = "ok"
+                response = post_request(url=url, data=json.dumps(response_data), headers=headers)
+                logger.info(f"request data ：{response_data} | JAVA callback info -> status:{response.status_code} | message:{response.text}")
+
+                # 新增配饰
+                new_item = self._get_random_accessories()
+                self.outfit_items.append(new_item)
+                user_input = self._build_user_input()
+                gemini_response_text, response_data['path'] = await self._call_gemini(user_input, user_id)
+                response_data['items'].append({"item_id": new_item.get('item_id'), "category": new_item.get('category')})
+
+                logger.info(f"🛑 搭配完成,终止原因: {gemini_data.get('reason')}")
                 self.stop_reason = "Finish reason: " + gemini_data.get('reason', 'No reason provided')
                 response_data['status'] = "stop"
                 response_data['message'] = self.stop_reason
@@ -327,7 +363,7 @@ class AsyncStylistAgent:
                 description = gemini_data.get('description')
 
                 # 4a. 检查类别是否有效 (重要步骤)
-                if category not in self.CATEGORY_SET:
+                if category not in self.CATEGORY_SET_ALL:
                     print(f"❌ Agent 推荐了无效类别: {category}。要求 Agent 重新输出。")
                     # 在实际应用中,这里需要将错误信息发回给 Agent,要求它更正
                     # 这里简化为跳过本次循环
@@ -382,6 +418,18 @@ class AsyncStylistAgent:
                     break
 
             if len(self.outfit_items) >= self.max_len:  # 设置一个最大循环限制,防止无限循环
+                gemini_response_text, response_data['path'] = await self._call_gemini(user_input, user_id)
+                response_data['items'].append({"item_id": self.outfit_items[-1]['item_id'], "category": self.outfit_items[-1]['category']})
+                response_data['status'] = "ok"
+                response = post_request(url=url, data=json.dumps(response_data), headers=headers)
+                logger.info(f"request data ：{response_data} | JAVA callback info -> status:{response.status_code} | message:{response.text}")
+
+                #  新增配饰
+                new_item = self._get_random_accessories()
+                self.outfit_items.append(new_item)
+                user_input = self._build_user_input()
+                gemini_response_text, response_data['path'] = await self._call_gemini(user_input, user_id)
+                response_data['items'].append({"item_id": new_item.get('item_id'), "category": new_item.get('category')})
                 logger.info("🚨 达到最大搭配数量限制,强制终止。")
                 self.stop_reason = "Finish reason: Reached max outfit length."
                 response_data['status'] = "stop"

app/server/ChatbotAgent/core/vector_database.py

@@ -1,3 +1,5 @@
+import random
+
 import torch
 import chromadb
 from PIL import Image
@@ -15,6 +17,8 @@ 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.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。"""
@@ -58,3 +62,52 @@ class VectorDatabase():
             include=['documents', 'metadatas', 'distances']
         )
         return results
+
+    def load_filtered_ids(self):
+        print("\n--- 初始化阶段：加载所有符合条件的 ID ---")
+        FILTER_CRITERIA = {
+            "$and": [
+                {"item_group_id": {"$ne": "Clothing"}},
+                {"modality": "image"},
+            ]
+        }
+        MAX_LIMIT = 100000
+
+        try:
+            # 获取所有符合条件的 ID
+            all_ids_results = self.collection.get(
+                where=FILTER_CRITERIA,
+                limit=MAX_LIMIT,
+                include=[]
+            )
+            all_matched_ids = all_ids_results['ids']
+            print(f"🎉 成功加载 {len(all_matched_ids)} 个 ID 到缓存。")
+            return all_matched_ids
+
+        except Exception as e:
+            print(f"❌ 初始化失败：获取 ID 列表时发生错误: {e}")
+            return []
+
+    def random_get_accessories(self):
+        random_single_id = random.choice(self.cache_filtered_ids)
+        # 2. 调用 ChromaDB：只查询这一个 ID 的详细信息
+        try:
+            final_results = self.collection.get(
+                ids=[random_single_id],
+                include=["metadatas"]  # 你只需要元数据
+            )
+
+            # 提取结果
+            if final_results['ids']:
+                return final_results
+            else:
+                return None
+
+        except Exception as e:
+            print(f"❌ 获取最终记录时发生错误: {e}")
+            return None
+
+
+if __name__ == '__main__':
+    db = VectorDatabase(vector_db_dir="/workspace/lc_stylist_agent/db", collection_name="lc_clothing_embedding", embedding_model_name="openai/clip-vit-base-patch32")
+    print(db.random_get_accessories())