feat: 以图搜图功能升级（跨表UNION检索 + 拍照识图入口 + 批量向量初始化脚本）

inventory-backend/app/api/v1/common/image_search.py

@@ -71,19 +71,45 @@ def image_search():
                 print(f"⚠️ [ImageSearch] 临时文件删除失败: {e}")
 
     # ---------------------------------------------------------
-    # 5. pgvector 余弦相似度检索
+    # 5. pgvector 余弦相似度检索（跨表联合检索）
     # ---------------------------------------------------------
     try:
-        # 将 Python list 转为 PostgreSQL 向量格式: '[0.1, 0.2, ...]'
         query_vector_str = '[' + ','.join(str(v) for v in embedding) + ']'
 
         sql = text("""
-            SELECT id, name, spec_model, product_image,
-                   (1 - (img_embedding <=> :query_vector)) AS similarity
-            FROM material_base
-            WHERE img_embedding IS NOT NULL
-            ORDER BY img_embedding <=> :query_vector
-            LIMIT 5
+            SELECT id, name, spec_model, image_url,
+                   (1 - (vec <=> :query_vector)) AS similarity
+            FROM (
+                SELECT id,
+                       COALESCE(name, '') AS name,
+                       COALESCE(spec, '') AS spec_model,
+                       COALESCE(product_image, '') AS image_url,
+                       img_embedding AS vec
+                FROM material_base
+                WHERE img_embedding IS NOT NULL
+
+                UNION ALL
+
+                SELECT id,
+                       '采购入库' AS name,
+                       '到货照片' AS spec_model,
+                       COALESCE(arrival_photo, '') AS image_url,
+                       arrival_image_embedding AS vec
+                FROM stock_buy
+                WHERE arrival_image_embedding IS NOT NULL
+
+                UNION ALL
+
+                SELECT id,
+                       '采购入库' AS name,
+                       '质检报告' AS spec_model,
+                       COALESCE(qc_report, '') AS image_url,
+                       qc_report_image_embedding AS vec
+                FROM stock_buy
+                WHERE qc_report_image_embedding IS NOT NULL
+            ) AS combined
+            ORDER BY vec <=> :query_vector
+            LIMIT 10
         """)
 
         result = db.session.execute(sql, {"query_vector": query_vector_str})
@@ -91,30 +117,31 @@ def image_search():
 
         results = []
         for row in rows:
-            product_id = row[0]
-            product_name = row[1] or ""
+            item_id = row[0]
+            item_name = row[1] or ""
             spec_model = row[2] or ""
-            product_image = row[3]
+            raw_image = row[3]
 
             # 解析图片 URL 列表，取第一张
             image_url = ""
-            if product_image:
+            if raw_image:
                 try:
-                    image_list = json.loads(product_image)
+                    image_list = json.loads(raw_image)
                     if image_list and len(image_list) > 0:
                         image_url = image_list[0]
                 except Exception:
-                    image_url = str(product_image)
+                    # 纯字符串直接使用
+                    image_url = str(raw_image)
 
             results.append({
-                "product_id": product_id,
-                "product_name": product_name,
+                "id": item_id,
+                "name": item_name,
                 "spec_model": spec_model,
                 "image_url": image_url,
                 "similarity": round(float(row[4]), 4)
             })
 
-        print(f"✅ [ImageSearch] 检索完成，命中 {len(results)} 条结果")
+        print(f"✅ [ImageSearch] 跨表检索完成，命中 {len(results)} 条结果")
         return jsonify({
             "code": 200,
             "msg": "检索成功",

inventory-backend/app/utils/ai_vision.py

@@ -100,7 +100,7 @@ def get_image_embedding(image_path: str) -> list:
     提取图像的 512 维 CLIP embedding 向量
 
     参数:
-        image_path: 图像文件路径（支持本地路径或 URL）
+        image_path: 图像文件路径
 
     返回:
         list: 512 维浮点向量
@@ -108,25 +108,25 @@ def get_image_embedding(image_path: str) -> list:
     if ort_session is None:
         load_clip_model()
 
-    # 加载图像
-    try:
-        image = Image.open(image_path).convert('RGB')
-    except Exception as e:
-        raise ValueError(f"图像加载失败: {image_path}, 错误: {e}")
-
-    # 中心裁剪
+    # 1. 图片预处理
+    image = Image.open(image_path).convert('RGB')
     image = _center_crop_and_resize(image)
-
-    # 归一化
     input_data = _normalize(np.array(image))
+    input_data = np.expand_dims(input_data, axis=0)  # [1, 3, 224, 224]
 
-    # 添加 batch 维度: (C, H, W) -> (1, C, H, W)
-    input_data = np.expand_dims(input_data, axis=0)
+    # 2. 构造占位符输入 (关键修复)
+    dummy_ids = np.zeros((1, 77), dtype=np.int64)
+    dummy_mask = np.zeros((1, 77), dtype=np.int64)
 
-    # 推理
-    outputs = ort_session.run(None, {'images': input_data.astype(np.float32)})
-
-    # 输出通常是 (1, 512) 的向量，取第一项并展平为 list
-    embedding = outputs[0][0].tolist()
-
-    return embedding
+    # 3. 传入模型进行推理
+    # 注意: 模型输入名在你的模型里必须叫 'pixel_values', 'input_ids', 'attention_mask'
+    # 如果报错找不到输入名，请打印 ort_session.get_inputs()[0].name 确认
+    outputs = ort_session.run(
+        ['image_embeds'],
+        {
+            'input_ids': dummy_ids,
+            'pixel_values': input_data.astype(np.float32),
+            'attention_mask': dummy_mask
+        }
+    )
+    return outputs[0][0].tolist()