TASK:lookbook上传，查询

app/service/lookbooks/query_service.py

@@ -0,0 +1,74 @@
+from app.service.lookbooks.config.config import DOCUMENT_COLLECTION  # 引入向量数据库
+from typing import Optional
+
+async def query_lookbooks_service(tag: Optional[str], year: Optional[str], n_results: int):
+    """
+    查询向量数据库，支持按 tag 和 year 过滤
+    :param tag: 查询过滤的标签
+    :param year: 查询过滤的年份
+    :param n_results: 返回的结果数量
+    :return: 查询结果列表
+    """
+    try:
+        # 选择一个主要过滤条件进行初步查询
+        primary_filter = {}
+        if tag:
+            primary_filter['tag'] = tag
+        elif year:
+            primary_filter['year'] = year
+        else:
+            primary_filter = None
+
+        # 如果没有任何条件，直接返回空列表
+        if not primary_filter:
+            return []
+
+        # 使用主过滤条件进行查询
+        query_result = DOCUMENT_COLLECTION.get(where=primary_filter)
+
+        # 打印 query_result 以检查数据结构是否符合预期
+        print(query_result)  # 或者使用 logger 记录以调试
+
+        # 检查 query_result 的结构
+        if not isinstance(query_result, dict) or 'documents' not in query_result:
+            raise ValueError("Expected query_result to be a dict containing a 'documents' key")
+
+        documents = query_result['documents']
+
+        # 确保 documents 是一个列表
+        if not isinstance(documents, list):
+            raise ValueError("Expected 'documents' to be a list")
+
+        # 对文档进行进一步过滤
+        filtered_results = []
+        for item in documents:
+            # 检查每个 item 是否是字典，如果不是字典，可能是简单文本描述
+            if isinstance(item, dict):
+                metadata = item.get('metadata', {})
+                if (not year or metadata.get('year') == year) and (not tag or metadata.get('tag') == tag):
+                    filtered_results.append(item)
+            elif isinstance(item, str):
+                # 如果 item 是字符串，假设它是描述文本，构造默认的 metadata
+                filtered_results.append({
+                    "text": item,
+                    "metadata": {}
+                })
+            else:
+                # 如果是其他数据类型，打印警告并跳过
+                print(f"Unexpected item type in documents: {type(item)}")
+
+        # 限制结果数量
+        limited_results = filtered_results[:n_results]
+
+        # 格式化结果
+        result_list = []
+        for item in limited_results:
+            result_list.append({
+                "description": item.get('text', ""),
+                "metadata": item.get('metadata', {})
+            })
+
+        return result_list
+
+    except Exception as e:
+        raise e  # 抛出异常，让接口层处理

app/service/lookbooks/service.py

@@ -45,6 +45,9 @@ def create_image_batch_requests(
     # 预处理 prompt：移除多余的空白和换行符
     prompt = ' '.join(prompt.split())
 
+    # 创建目录（如果目录不存在）
+    os.makedirs(output_path, exist_ok=True)
+
     completed_id = []
     if os.path.exists(os.path.join(output_path, "image_description_results.jsonl")):
         with open(os.path.join(output_path, "image_description_results.jsonl"), "r") as f:
@@ -141,7 +144,8 @@ async def process_lookbook_task(lookbook_list, tag, year):
     try:
         for look_book_path in tqdm.tqdm(lookbook_list):
             lookbook_name = os.path.splitext(os.path.basename(look_book_path))[0]
-            output_dir = os.path.join("fashion_documents/lookbook/images", lookbook_name)
+            output_dir = os.path.join("fashion_documents", "lookbook", "images", lookbook_name)
+            # output_dir = os.path.join("fashion_documents/lookbook/images", lookbook_name)
             os.makedirs(output_dir, exist_ok=True)
             if not os.listdir(output_dir):
                 from unstructured.partition.pdf import partition_pdf
@@ -159,8 +163,10 @@ async def process_lookbook_task(lookbook_list, tag, year):
                 current_images = os.listdir(output_dir)
                 image_list.extend([os.path.join(output_dir, x) for x in current_images])
 
+        output_path = os.path.join("fashion_documents", "lookbook", "results")
+
         # 1. 处理图片并生成批量请求
-        image_description_results_file = create_image_batch_requests(image_list, "fashion_documents/lookbook/results")
+        image_description_results_file = create_image_batch_requests(image_list, output_path)
 
         # 2. 保存结果到向量数据库
         if image_description_results_file: