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**.
+        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: First major garments (tops/outerwear/bottoms/dresses), then shoes and bags, and finally accessories.
+        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())

app/test/chromadb/random_accessories.py

@@ -0,0 +1,106 @@
+import time
+
+import chromadb
+import random
+from typing import Dict, Any, List
+
+# --- 你的配置 ---
+DB_PATH = "/workspace/lc_stylist_agent/db"
+COLLECTION_NAME = 'lc_clothing_embedding'
+FILTER_CRITERIA = {
+    "$and": [
+        {"item_group_id": {"$ne": "Clothing"}},
+        {"modality": "image"},
+    ]
+}
+MAX_LIMIT = 1000000  # 用于第一次获取所有ID的限制
+
+client = chromadb.PersistentClient(path=DB_PATH)
+try:
+    collection = client.get_collection(name=COLLECTION_NAME)
+    print(f"✅ 连接到 Collection: {COLLECTION_NAME}")
+except ValueError:
+    print(f"⚠️ Collection '{COLLECTION_NAME}' 不存在。")
+    exit()
+
+
+# -----------------------------------------------
+# 步骤 1: 应用程序启动时/初始化时执行（只执行一次）
+# -----------------------------------------------
+def load_filtered_ids(coll: chromadb.api.models.Collection.Collection, filter_criteria: Dict[str, Any]) -> List[str]:
+    """
+    加载并缓存所有符合条件的记录ID。
+    """
+    print("\n--- 初始化阶段：加载所有符合条件的 ID ---")
+
+    try:
+        # 获取所有符合条件的 ID
+        all_ids_results = coll.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 []
+
+
+# 存储所有符合条件的 ID 的全局变量 (缓存)
+start_time = time.time()
+CACHED_FILTERED_IDS = load_filtered_ids(collection, FILTER_CRITERIA)
+print(time.time() - start_time)
+
+
+# -----------------------------------------------
+# 步骤 2: 每次需要随机记录时调用 (高效重复执行)
+# -----------------------------------------------
+def get_random_record_from_cache(coll: chromadb.api.models.Collection.Collection, cached_ids: List[str]) -> Dict[str, Any] | None:
+    """
+    从缓存的 ID 列表中随机选择一个 ID，然后查询其详细信息。
+    """
+    total_count = len(cached_ids)
+
+    if total_count == 0:
+        return None
+
+    # 1. 纯 Python 内存操作：从缓存中随机选择一个 ID
+    random_single_id = random.choice(cached_ids)
+
+    # 2. 调用 ChromaDB：只查询这一个 ID 的详细信息
+    try:
+        final_results = coll.get(
+            ids=[random_single_id],
+        )
+
+        # 提取结果
+        if final_results['ids']:
+            return {
+                "id": final_results['ids'][0],
+                "metadata": final_results['metadatas'][0]
+            }
+        else:
+            return None
+
+    except Exception as e:
+        print(f"❌ 获取最终记录时发生错误: {e}")
+        return None
+
+
+# --- 执行并打印结果 (可以多次调用，每次都很快) ---
+print("\n--- 随机获取 1 ---")
+start_time = time.time()
+random_data_1 = get_random_record_from_cache(collection, CACHED_FILTERED_IDS)
+print(time.time() - start_time)
+if random_data_1:
+    print(f"  ID: {random_data_1}")
+
+print("\n--- 随机获取 2 ---")
+start_time = time.time()
+random_data_2 = get_random_record_from_cache(collection, CACHED_FILTERED_IDS)
+print(time.time() - start_time)
+if random_data_2:
+    print(f"  ID: {random_data_2}")

app/test/chromadb/type_list.py

@@ -0,0 +1,101 @@
+import chromadb
+from typing import Set, List, Dict, Union, Any
+
+# --- 你的配置 ---
+DB_PATH = "/workspace/lc_stylist_agent/db"
+COLLECTION_NAME = 'lc_clothing_embedding'
+# 设置一个足够大的限制来获取所有记录，或者使用分页（如果记录数非常庞大）
+MAX_LIMIT = 1000000
+
+client = chromadb.PersistentClient(path=DB_PATH)
+try:
+    collection = client.get_collection(name=COLLECTION_NAME)
+    print(f"✅ 连接到 Collection: {COLLECTION_NAME}")
+except ValueError:
+    print(f"⚠️ Collection '{COLLECTION_NAME}' 不存在。")
+    # 如果 collection 不存在，我们将跳过后续操作
+    collection = None
+
+
+def get_category_item_group_map(
+        coll: chromadb.api.models.Collection.Collection
+) -> Dict[str, Set[str]]:
+    """
+    获取 Collection 中所有记录的 'category' 和 'item_group_id' 字段，
+    并返回一个 Category 到其所有唯一 Item Group ID 集合的映射。
+    """
+    print("\n--- 正在获取所有记录的元数据 (category 和 item_group_id)... ---")
+
+    # 1. 获取所有记录的元数据
+    try:
+        # 使用 .get() 方法获取所有 metadatas，不包含 embeddings 和 documents
+        results = coll.get(
+            limit=MAX_LIMIT,
+            include=["metadatas"]
+        )
+
+        all_metadatas: List[Dict[str, Any]] = results.get('metadatas', [])
+
+    except Exception as e:
+        print(f"❌ 获取元数据时发生错误: {e}")
+        return {}
+
+    if not all_metadatas:
+        print("❌ 集合中没有元数据记录。")
+        return {}
+
+    # 2. 构建 Category 到 Item Group ID 集合的映射
+    # 结构: { 'CategoryA': {'group_id_1', 'group_id_2'}, 'CategoryB': {'group_id_3'} }
+    category_item_group_map: Dict[str, Set[str]] = {}
+
+    for metadata in all_metadatas:
+        category_value: Union[str, None] = metadata.get('category')
+        item_group_id_value: Union[str, None] = metadata.get('item_group_id')
+
+        # 确保两个元数据字段都存在
+        if category_value is not None and item_group_id_value is not None:
+            category = str(category_value)
+            item_group_id = str(item_group_id_value)
+
+            # 使用 setdefault 来确保 category 键存在，并初始化为一个空的 set
+            # 然后将 item_group_id 添加到对应的 set 中
+            category_item_group_map.setdefault(category, set()).add(item_group_id)
+
+    return category_item_group_map
+
+
+# --- 执行并打印结果 ---
+if collection:
+    category_item_group_mapping = get_category_item_group_map(collection)
+
+    if category_item_group_mapping:
+
+        # 统计总共发现多少种唯一 Category
+        print(f"\n🎉 发现 {len(category_item_group_mapping)} 种唯一 Category 及其对应的 Item Group IDs:")
+
+        # 对类别名称进行排序，便于查看
+        sorted_categories = sorted(category_item_group_mapping.keys())
+
+        # 打印详细结果
+        for category in sorted_categories:
+            item_group_ids = category_item_group_mapping[category]
+
+            # 打印类别名称和唯一的 Item Group ID 数量
+            print(f"\n--- 👕 Category: **{category}** ---")
+            print(f"**Item Group 总数:** {len(item_group_ids)} 个唯一 Item Group ID")
+
+            # 将 Item Group ID 转换为列表并排序，以便展示
+            sorted_item_group_ids = sorted(list(item_group_ids))
+
+            # 打印前 10 个 Item Group ID 作为示例
+            print(f"**部分 Item Group IDs (示例):**")
+
+            # 使用列表展示
+            for i, item_group_id in enumerate(sorted_item_group_ids[:10]):
+                print(f"* {item_group_id}")
+
+            if len(sorted_item_group_ids) > 10:
+                print(f"* ... (还有 {len(sorted_item_group_ids) - 10} 个 Item Group ID 未列出)")
+
+    else:
+        print("没有找到任何 Category 或 Item Group ID 数据。请检查元数据字段名称是否正确。")