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

@@ -55,7 +55,7 @@ class AsyncStylistAgent:
         self.gcs_bucket = "lc_stylist_agent_outfit_items"
         self.minio_bucket = "lanecarford"
 
-    def _load_style_guide(self, path: str) -> str:
+    def _load_style_guide(self, path: str):
         """加载 markdown 风格指南内容。"""
         parts = path.split('/', 1)
         if len(parts) != 2:
@@ -63,18 +63,24 @@ class AsyncStylistAgent:
 
         bucket_name, object_name = parts
         try:
-            # 1. 获取对象
+            #  获取对象 读取内容
             response = minio_client.get_object(bucket_name, object_name)
-
-            # 2. 读取内容
             content_bytes = response.read()
 
-            # 3. 关闭连接
+            json_response = minio_client.get_object(bucket_name, object_name.replace('.md', '.json'))
-            response.close()
+            json_data = json_response.data
-            response.release_conn()
 
-            # 4. 解码并返回
+            #  关闭连接
-            return content_bytes.decode('utf-8')
+            response.close()
+            json_response.close()
+            response.release_conn()
+            json_response.release_conn()
+
+            # 4. 解析 JSON 字符串
+            json_string = json_data.decode('utf-8')
+            json_content = json.loads(json_string)
+
+            return content_bytes.decode('utf-8'), json_content
 
         except Exception as e:
             raise Exception(f"Failed to load style guide from {path}: {e}")
@@ -214,6 +220,33 @@ class AsyncStylistAgent:
             # 返回一个停止信号以防止循环继续
             return json.dumps({"action": "stop", "reason": f"API_ERROR: {str(e)}"})
 
+    async def _merge_images(self, user_id: str):
+        """
+        实际调用 Gemini API 的函数，接受文本和可选的图片路径列表。
+
+        Args:
+            user_input: 发送给模型的主文本内容。
+            image_paths: 待发送图片的本地路径列表。
+
+        Returns:
+            模型的响应文本（预期为 JSON 字符串）。
+        """
+        minio_path = ""
+        if self.outfit_items:
+            merged_image = merge_images_to_square(self.outfit_items, max_len=9, add_text=False)
+            image_bytes_io = io.BytesIO()
+            image_format = 'JPEG'
+
+            merged_image.save(image_bytes_io, format=image_format)
+            image_bytes = image_bytes_io.getvalue()
+
+            file_name = uuid.uuid4()
+            blob_name = f"lc_stylist_agent_outfit_items/{user_id}/{file_name}.jpg"
+            responses = oss_upload_image(oss_client=minio_client, bucket=self.minio_bucket, object_name=blob_name, image_bytes=image_bytes)
+            minio_path = f"{responses.bucket_name}/{responses.object_name}"
+
+        return minio_path
+
     def _parse_gemini_response(self, response_text: str) -> Optional[Dict[str, Any]]:
         """安全解析 Gemini 的 JSON 响应。"""
         try:
@@ -260,12 +293,27 @@ class AsyncStylistAgent:
             print(f"An error occurred during item retrieval: {e}")
             return None
 
-    async def _get_random_accessories(self):
+    async def _get_random_accessories(self, stylist):
-        results = self.local_db.random_get_accessories()
+        stylist_item = []
+        stylist_item_ids = []
+        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]
 
-        # 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)
+        accessories_count = 2
-        best_meta = results['metadatas'][0]
+
-        return {
+        # 2. 在配饰池中过滤掉已经选中的item ，然后抽两个item
+        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)
+        random_items = self.local_db.random_get_accessories(random_single_ids)['metadatas']
+        all_items = stylist_item + random_items
+
+        items_data = []
+
+        for best_meta in all_items:
+            items_data.append({
                 "item_id": best_meta['item_id'],  # 从 metadata 字典中安全获取
                 "category": best_meta['category'],
                 "gpt_description": best_meta['description'],
@@ -273,7 +321,9 @@ class AsyncStylistAgent:
                 # 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")
-        }
+            })
+
+        return items_data
 
     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。"""
@@ -294,7 +344,7 @@ class AsyncStylistAgent:
         """主流程控制循环。"""
         print(f"--- Starting Agent (Outfit ID: {self.outfit_id}) ---")
 
-        self.style_guide = self._load_style_guide(stylist_path)
+        self.style_guide, self.style_accessories_guide = self._load_style_guide(stylist_path)
         self.system_prompt = self._build_system_prompt(request_summary, gender)
         response_data = {"status": "",
                          "message": "",
@@ -342,12 +392,13 @@ class AsyncStylistAgent:
                 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
+                # 根据stylist要求随机增加配饰 3-4个配饰
-                new_item = await self._get_random_accessories()
+                new_item = await self._get_random_accessories(self.style_accessories_guide)
-                self.outfit_items.append(new_item)
+                for item in new_item:
-                user_input = self._build_user_input()
+                    self.outfit_items.append(item)
-                gemini_response_text, response_data['path'] = await self._call_gemini(user_input, user_id)
+                    response_data['items'].append({"item_id": item.get('item_id'), "category": item.get('category')})
-                response_data['items'].append({"item_id": new_item.get('item_id'), "category": new_item.get('category')})
+
+                response_data['path'] = await self._merge_images(user_id)
 
                 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')
@@ -424,12 +475,13 @@ class AsyncStylistAgent:
                 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
+                # 根据stylist要求随机增加配饰 3-4个配饰
-                new_item = await self._get_random_accessories()
+                new_item = await self._get_random_accessories(self.style_accessories_guide)
-                self.outfit_items.append(new_item)
+                for item in new_item:
-                user_input = self._build_user_input()
+                    self.outfit_items.append(item)
-                gemini_response_text, response_data['path'] = await self._call_gemini(user_input, user_id)
+                    response_data['items'].append({"item_id": item.get('item_id'), "category": item.get('category')})
-                response_data['items'].append({"item_id": new_item.get('item_id'), "category": new_item.get('category')})
+                response_data['path'] = await self._merge_images(user_id)
+
                 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

@@ -69,7 +69,7 @@ class VectorDatabase():
             "$and": [
                 {"item_group_id": {"$ne": "Clothing"}},
                 {"item_group_id": {"$ne": "Shoes"}},  # 新增：过滤 Shoes
-                {"modality": "image"},
+                {"modality": "image"}
             ]
         }
         MAX_LIMIT = 100000
@@ -89,12 +89,11 @@ class VectorDatabase():
             print(f"❌ 初始化失败：获取 ID 列表时发生错误: {e}")
             return []
 
-    def random_get_accessories(self):
+    def random_get_accessories(self, ids):
-        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],
+                ids=ids,
                 include=["metadatas"]  # 你只需要元数据
             )
 
@@ -110,5 +109,25 @@ class VectorDatabase():
 
 
 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")
+    stylist = {
-    print(db.random_get_accessories())
+        'text': "gold necklace",
+        '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")
+    # print(db.random_get_accessories())
+
+    query_embedding = local_db.get_clip_embedding(stylist['text'], is_image=False)
+
+    results = local_db.query_local_db(query_embedding, stylist['category'], n_results=10)
+    # 2. 从结果集中抽 stylist['count'] 个item
+    stylist_item = random.choices(results['metadatas'][0], k=stylist['count'])
+    stylist_item_ids = [item_id['item_id'] for item_id in stylist_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
+    accessories_count = 9 - max_len - stylist['count']
+
+    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

app/server/utils/minio_client.py

@@ -114,12 +114,19 @@ if __name__ == '__main__':
     # url = "lanecarford/lc_stylist_agent_outfit_items/string/7fed1c7b-9efd-41fa-a335-182c310ea611.jpg"
     # url = "lanecarford/lc_stylist_agent_outfit_items/string/5de155d0-56a6-43e8-a2f1-7538fce86220.jpg"
     # url = "lanecarford/lc_stylist_agent_outfit_items/string/1cd1803c-5f51-4961-a4f2-2acd3e0d8294.jpg"
-    url = 'lanecarford/lc_stylist_agent_outfit_items/string/99cd8cc0-856a-487d-bb21-5684855ef48f.jpg'
+    url = [
+        'lanecarford/lc_stylist_agent_outfit_items/zhhtest20251120/d9df7c48-c7e5-47f9-be67-07f0d175d202.jpg',
+        'lanecarford/lc_stylist_agent_outfit_items/zhhtest20251120/ddf39b9c-69f0-4b28-95ed-9d823fa82e35.jpg',
+        'lanecarford/lc_stylist_agent_outfit_items/zhhtest20251120/112194a0-dc1d-4151-8c58-82642142a553.jpg',
+        'lanecarford/lc_stylist_agent_outfit_items/zhhtest20251120/788007f1-e44b-4390-ad9e-a2d4ba406379.jpg'
+    ]
     read_type = "1"
-    img = oss_get_image(oss_client=minio_client, path=url, data_type=read_type)
+    for id, i in enumerate(url):
+        img = oss_get_image(minio_client, i, read_type)
+        img = oss_get_image(oss_client=minio_client, path=i, data_type=read_type)
         if read_type == "cv2":
             cv2.imshow("", img)
             cv2.waitKey(0)
         else:
             img.show()
-        img.save("4.png")
+            img.save(f"{time.time()}.png")

requirements.txt

@@ -17,3 +17,4 @@ open-clip-torch==2.24.0
 pytorch-fid==0.3.0
 litserve
 # pip install git+https://github.com/openai/CLIP.git
+# pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128