feat: 所有参数字段改为文本类型，长文本字段标记input_style

app.py

@@ -101,64 +101,65 @@ def save_data(file_path, data):
 
 # ============ 大模型智能解析 ============
 
-def parse_with_llm(text, category_type, images=None):
+def parse_with_llm(text, category_type, images=None, category_id=None, subcategory_id=None):
     """
     使用大模型解析文本/图片，提取结构化数据
     支持多张图片输入，可能解析出多个产品
+    根据类别配置的参数字段进行解析
     """
     
-    # 根据类型定义字段模板
-    field_templates = {
-        'model': {
-            'name': '模型名称',
-            'organization': '厂商/组织',
-            'parameters': '参数量(数字，单位B)',
-            'context_length': '上下文长度(数字)',
-            'architecture': '架构类型',
-            'is_open_source': '是否开源(true/false)',
-            'mmlu': 'MMLU分数(数字)',
-            'input_price': '输入价格(数字)',
-            'output_price': '输出价格(数字)',
-            'license': '许可证',
-            'description': '简介描述',
-        },
-        'gpu': {
-            'name': 'GPU名称',
-            'manufacturer': '厂商',
-            'architecture': '架构',
-            'memory_gb': '显存大小(数字，单位GB)',
-            'cuda_cores': 'CUDA核心数(数字)',
-            'tensor_cores': 'Tensor核心数(数字)',
-            'memory_bandwidth_gbs': '显存带宽(数字，单位GB/s)',
-            'fp16_tflops': 'FP16性能(数字，单位TF)',
-            'price_usd': '价格(数字)',
-            'release_year': '发布年份(数字)',
-            'description': '简介描述',
-        },
-        'cpu': {
-            'name': 'CPU名称',
-            'manufacturer': '厂商',
-            'architecture': '架构',
-            'cores': '核心数(数字)',
-            'threads': '线程数(数字)',
-            'base_clock_ghz': '基础频率(数字，单位GHz)',
-            'boost_clock_ghz': '加速频率(数字，单位GHz)',
-            'l3_cache_mb': 'L3缓存(数字，单位MB)',
-            'tdp_watts': 'TDP功耗(数字，单位W)',
-            'price_usd': '价格(数字)',
-            'description': '简介描述',
-        },
-        'dynamic': {
+    # 从类别配置中获取字段定义
+    categories = load_data(CATEGORIES_FILE)
+    
+    # 确定类别ID
+    if category_id:
+        cat = next((c for c in categories if c['id'] == category_id), None)
+    else:
+        # 根据类型映射到内置类别ID
+        type_to_cat_id = {'model': 'ai-models', 'gpu': 'gpus', 'cpu': 'cpus'}
+        cat_id = type_to_cat_id.get(category_type)
+        cat = next((c for c in categories if c['id'] == cat_id), None)
+    
+    # 构建字段模板
+    fields = {}
+    
+    if cat and 'fields' in cat:
+        # 使用类别配置的字段
+        for field in cat['fields']:
+            field_desc = field['label']
+            # 所有字段都是文本类型
+            if field.get('input_style') == 'long':
+                field_desc += '(长文本)'
+            else:
+                field_desc += '(文本)'
+            if field.get('description'):
+                field_desc += f" - {field['description']}"
+            fields[field['key']] = field_desc
+        
+        # 如果有子类别，添加子类别的额外字段
+        if subcategory_id:
+            subcat = next((s for s in cat.get('subcategories', []) if s['id'] == subcategory_id), None)
+            if subcat and 'extra_fields' in subcat:
+                for field in subcat['extra_fields']:
+                    field_desc = field['label']
+                    if field.get('input_style') == 'long':
+                        field_desc += '(长文本)'
+                    else:
+                        field_desc += '(文本)'
+                    if field.get('description'):
+                        field_desc += f" - {field['description']}"
+                    fields[field['key']] = field_desc
+    else:
+        # 兜底：使用默认字段模板
+        fields = {
             'name': '名称',
             'brand': '品牌',
             'price': '价格(数字)',
             'year': '年份(数字)',
-            'specs': '规格参数',
+            'specs': '规格参数(JSON对象)',
             'description': '简介描述',
-        },
-    }
+        }
     
-    fields = field_templates.get(category_type, field_templates['dynamic'])
     fields_json = json.dumps(fields, ensure_ascii=False, indent=2)
     
     # 构建消息内容
@@ -498,11 +499,13 @@ def api_parse_images():
     """
     解析图片中的产品参数（预览模式，不保存）
     支持多张图片，可能返回多个产品
+    根据类别配置的参数字段进行解析
     """
     data = request.get_json()
     text = data.get('text', '')
     images = data.get('images', [])
     category_type = data.get('category_type', 'dynamic')
+    subcategory_id = data.get('subcategory_id', '')
     
     if not text and not images:
         return jsonify({'error': '文本或图片不能都为空'}), 400
@@ -510,8 +513,12 @@ def api_parse_images():
     if not images:
         return jsonify({'error': '请上传至少一张图片'}), 400
     
-    # 调用大模型解析
-    parsed_list = parse_with_llm(text, category_type, images)
+    # 确定类别ID
+    type_to_cat_id = {'model': 'ai-models', 'gpu': 'gpus', 'cpu': 'cpus', 'dynamic': None}
+    category_id = type_to_cat_id.get(category_type)
+    
+    # 调用大模型解析（根据类别字段配置）
+    parsed_list = parse_with_llm(text, category_type, images, category_id=category_id, subcategory_id=subcategory_id)
     
     return jsonify({
         'success': True,
@@ -523,9 +530,11 @@ def api_parse_images():
 
 # ============ 智能添加API ============
 
-@app.route('/api/models/smart-add', methods=['POST'])
-def api_smart_add_model():
-    """智能添加模型（支持文本和多图解析，可能添加多个产品）"""
+# ============ 智能补充参数API ============
+
+@app.route('/api/models/<model_id>/smart-update', methods=['POST'])
+def api_smart_update_model(model_id):
+    """智能补充模型参数（只填充缺失字段）"""
     data = request.get_json()
     text = data.get('text', '')
     images = data.get('images', [])
@@ -533,30 +542,228 @@ def api_smart_add_model():
     if not text and not images:
         return jsonify({'error': '文本或图片不能都为空'}), 400
     
-    # 大模型解析（支持多图）
+    # 获取现有数据
+    models = load_data(MODELS_FILE)
+    model = next((m for m in models if m['id'] == model_id), None)
+    if not model:
+        return jsonify({'error': 'Model not found'}), 404
+    
+    # 解析新参数
     parsed_list = parse_with_llm(text, 'model', images)
+    if not parsed_list:
+        return jsonify({'error': '解析失败'}), 500
+    
+    parsed = parsed_list[0]  # 补充只取第一个
+    
+    # 只填充缺失或为空的字段
+    updated_fields = []
+    for key, value in parsed.items():
+        if value is not None and value != '' and value != 0:
+            existing = model.get(key)
+            if existing is None or existing == '' or existing == 0:
+                model[key] = value
+                updated_fields.append(key)
+    
+    model['updated_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    
+    # 追加解析来源记录
+    parse_source = {
+        'type': 'smart_update',
+        'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+        'images': images,
+        'text': text[:500] if text else '',
+        'updated_fields': updated_fields
+    }
+    if 'parse_sources' not in model:
+        model['parse_sources'] = []
+    model['parse_sources'].append(parse_source)
+    
+    save_data(MODELS_FILE, models)
+    
+    return jsonify({'success': True, 'updated_fields': updated_fields, 'model': model})
+
+@app.route('/api/gpus/<gpu_id>/smart-update', methods=['POST'])
+def api_smart_update_gpu(gpu_id):
+    """智能补充GPU参数（只填充缺失字段）"""
+    data = request.get_json()
+    text = data.get('text', '')
+    images = data.get('images', [])
+    
+    if not text and not images:
+        return jsonify({'error': '文本或图片不能都为空'}), 400
+    
+    gpus = load_data(GPUS_FILE)
+    gpu = next((g for g in gpus if g['id'] == gpu_id), None)
+    if not gpu:
+        return jsonify({'error': 'GPU not found'}), 404
+    
+    parsed_list = parse_with_llm(text, 'gpu', images)
+    if not parsed_list:
+        return jsonify({'error': '解析失败'}), 500
+    
+    parsed = parsed_list[0]
+    
+    updated_fields = []
+    for key, value in parsed.items():
+        if value is not None and value != '' and value != 0:
+            existing = gpu.get(key)
+            if existing is None or existing == '' or existing == 0:
+                gpu[key] = value
+                updated_fields.append(key)
+    
+    gpu['updated_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    
+    parse_source = {
+        'type': 'smart_update',
+        'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+        'images': images,
+        'text': text[:500] if text else '',
+        'updated_fields': updated_fields
+    }
+    if 'parse_sources' not in gpu:
+        gpu['parse_sources'] = []
+    gpu['parse_sources'].append(parse_source)
+    
+    save_data(GPUS_FILE, gpus)
+    
+    return jsonify({'success': True, 'updated_fields': updated_fields, 'gpu': gpu})
+
+@app.route('/api/cpus/<cpu_id>/smart-update', methods=['POST'])
+def api_smart_update_cpu(cpu_id):
+    """智能补充CPU参数（只填充缺失字段）"""
+    data = request.get_json()
+    text = data.get('text', '')
+    images = data.get('images', [])
+    
+    if not text and not images:
+        return jsonify({'error': '文本或图片不能都为空'}), 400
+    
+    cpus = load_data(CPUS_FILE)
+    cpu = next((c for c in cpus if c['id'] == cpu_id), None)
+    if not cpu:
+        return jsonify({'error': 'CPU not found'}), 404
+    
+    parsed_list = parse_with_llm(text, 'cpu', images)
+    if not parsed_list:
+        return jsonify({'error': '解析失败'}), 500
+    
+    parsed = parsed_list[0]
+    
+    updated_fields = []
+    for key, value in parsed.items():
+        if value is not None and value != '' and value != 0:
+            existing = cpu.get(key)
+            if existing is None or existing == '' or existing == 0:
+                cpu[key] = value
+                updated_fields.append(key)
+    
+    cpu['updated_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    
+    parse_source = {
+        'type': 'smart_update',
+        'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+        'images': images,
+        'text': text[:500] if text else '',
+        'updated_fields': updated_fields
+    }
+    if 'parse_sources' not in cpu:
+        cpu['parse_sources'] = []
+    cpu['parse_sources'].append(parse_source)
+    
+    save_data(CPUS_FILE, cpus)
+    
+    return jsonify({'success': True, 'updated_fields': updated_fields, 'cpu': cpu})
+
+@app.route('/api/items/<category_id>/<item_id>/smart-update', methods=['POST'])
+def api_smart_update_item(category_id, item_id):
+    """智能补充动态分类数据参数（只填充缺失字段）"""
+    data = request.get_json()
+    text = data.get('text', '')
+    images = data.get('images', [])
+    
+    if not text and not images:
+        return jsonify({'error': '文本或图片不能都为空'}), 400
+    
+    items_file = DATA_DIR / f'items_{category_id}.json'
+    items = load_data(items_file)
+    item = next((i for i in items if i['id'] == item_id), None)
+    if not item:
+        return jsonify({'error': 'Item not found'}), 404
+    
+    parsed_list = parse_with_llm(text, 'dynamic', images)
+    if not parsed_list:
+        return jsonify({'error': '解析失败'}), 500
+    
+    parsed = parsed_list[0]
+    
+    updated_fields = []
+    for key, value in parsed.items():
+        if value is not None and value != '' and value != 0:
+            existing = item.get(key)
+            if existing is None or existing == '' or existing == 0:
+                item[key] = value
+                updated_fields.append(key)
+    
+    item['updated_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    
+    parse_source = {
+        'type': 'smart_update',
+        'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+        'images': images,
+        'text': text[:500] if text else '',
+        'updated_fields': updated_fields
+    }
+    if 'parse_sources' not in item:
+        item['parse_sources'] = []
+    item['parse_sources'].append(parse_source)
+    
+    save_data(items_file, items)
+    
+    return jsonify({'success': True, 'updated_fields': updated_fields, 'item': item})
+
+@app.route('/api/models/smart-add', methods=['POST'])
+def api_smart_add_model():
+    """智能添加模型（支持文本和多图解析，可能添加多个产品）"""
+    data = request.get_json()
+    text = data.get('text', '')
+    images = data.get('images', [])
+    subcategory_id = data.get('subcategory_id', '')  # 子类别ID
+    
+    if not text and not images:
+        return jsonify({'error': '文本或图片不能都为空'}), 400
+    
+    # 大模型解析（根据类别字段配置）
+    parsed_list = parse_with_llm(text, 'model', images, category_id='ai-models', subcategory_id=subcategory_id)
     
     # 处理多个产品
     results = []
     models = load_data(MODELS_FILE)
     
+    # 构建解析来源记录
+    parse_source = {
+        'type': 'smart_add',
+        'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+        'images': images,
+        'text': text[:500] if text else ''  # 截取前500字符
+    }
+    
     for parsed in parsed_list:
         # 补充必要字段
         parsed['id'] = uuid.uuid4().hex[:12]
         parsed['created_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
         parsed['visible'] = True
-        parsed['raw_text'] = text  # 保存原始文本
-        parsed['images'] = images  # 保存图片
+        parsed['subcategory_id'] = subcategory_id  # 保存子类别
         parsed['publish_date'] = parsed.get('publish_date', '')
         parsed['views'] = 0
         parsed['is_pinned'] = False
+        parsed['product_images'] = []  # 产品展示图（不同于参数截图）
+        parsed['parse_sources'] = [parse_source]  # 解析来源历史
         
         models.append(parsed)
         results.append(parsed)
     
     save_data(MODELS_FILE, models)
     
-    # 返回添加的产品列表
     return jsonify({'success': True, 'count': len(results), 'products': results})
 
 @app.route('/api/gpus/smart-add', methods=['POST'])
@@ -565,24 +772,33 @@ def api_smart_add_gpu():
     data = request.get_json()
     text = data.get('text', '')
     images = data.get('images', [])
+    subcategory_id = data.get('subcategory_id', '')
     
     if not text and not images:
         return jsonify({'error': '文本或图片不能都为空'}), 400
     
-    parsed_list = parse_with_llm(text, 'gpu', images)
+    parsed_list = parse_with_llm(text, 'gpu', images, category_id='gpus', subcategory_id=subcategory_id)
     
     results = []
     gpus = load_data(GPUS_FILE)
     
+    parse_source = {
+        'type': 'smart_add',
+        'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+        'images': images,
+        'text': text[:500] if text else ''
+    }
+    
     for parsed in parsed_list:
         parsed['id'] = uuid.uuid4().hex[:12]
         parsed['created_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
         parsed['visible'] = True
-        parsed['raw_text'] = text
-        parsed['images'] = images
+        parsed['subcategory_id'] = subcategory_id
         parsed['publish_date'] = parsed.get('publish_date', '')
         parsed['views'] = 0
         parsed['is_pinned'] = False
+        parsed['product_images'] = []
+        parsed['parse_sources'] = [parse_source]
         
         gpus.append(parsed)
         results.append(parsed)
@@ -597,24 +813,33 @@ def api_smart_add_cpu():
     data = request.get_json()
     text = data.get('text', '')
     images = data.get('images', [])
+    subcategory_id = data.get('subcategory_id', '')
     
     if not text and not images:
         return jsonify({'error': '文本或图片不能都为空'}), 400
     
-    parsed_list = parse_with_llm(text, 'cpu', images)
+    parsed_list = parse_with_llm(text, 'cpu', images, category_id='cpus', subcategory_id=subcategory_id)
     
     results = []
     cpus = load_data(CPUS_FILE)
     
+    parse_source = {
+        'type': 'smart_add',
+        'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+        'images': images,
+        'text': text[:500] if text else ''
+    }
+    
     for parsed in parsed_list:
         parsed['id'] = uuid.uuid4().hex[:12]
         parsed['created_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
         parsed['visible'] = True
-        parsed['raw_text'] = text
-        parsed['images'] = images
+        parsed['subcategory_id'] = subcategory_id
         parsed['publish_date'] = parsed.get('publish_date', '')
         parsed['views'] = 0
         parsed['is_pinned'] = False
+        parsed['product_images'] = []
+        parsed['parse_sources'] = [parse_source]
         
         cpus.append(parsed)
         results.append(parsed)
@@ -629,26 +854,36 @@ def api_smart_add_item(category_id):
     data = request.get_json()
     text = data.get('text', '')
     images = data.get('images', [])
+    subcategory_id = data.get('subcategory_id', '')
     
     if not text and not images:
         return jsonify({'error': '文本或图片不能都为空'}), 400
     
-    parsed_list = parse_with_llm(text, 'dynamic', images)
+    # 使用类别配置的字段解析
+    parsed_list = parse_with_llm(text, 'dynamic', images, category_id=category_id, subcategory_id=subcategory_id)
     
     results = []
     items_file = DATA_DIR / f'items_{category_id}.json'
     items = load_data(items_file)
     
+    parse_source = {
+        'type': 'smart_add',
+        'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+        'images': images,
+        'text': text[:500] if text else ''
+    }
+    
     for parsed in parsed_list:
         parsed['id'] = uuid.uuid4().hex[:12]
         parsed['category_id'] = category_id
         parsed['created_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
         parsed['visible'] = True
-        parsed['raw_text'] = text
-        parsed['images'] = images
+        parsed['subcategory_id'] = subcategory_id
         parsed['publish_date'] = parsed.get('publish_date', '')
         parsed['views'] = 0
         parsed['is_pinned'] = False
+        parsed['product_images'] = []
+        parsed['parse_sources'] = [parse_source]
         
         items.append(parsed)
         results.append(parsed)

data/cpus.json

@@ -11,8 +11,9 @@
     "l3_cache_mb": 384,
     "tdp_watts": 360,
     "price_usd": 11000,
-    "release_year": 2022,
-    "description": "AMD顶级服务器CPU，96核心"
+    "description": "AMD顶级服务器CPU，96核心",
+    "subcategory_id": "server",
+    "publish_date": "2022-01-01"
   },
   {
     "id": "epyc9554",
@@ -26,8 +27,9 @@
     "l3_cache_mb": 256,
     "tdp_watts": 360,
     "price_usd": 6800,
-    "release_year": 2022,
-    "description": "64核心高性能服务器CPU"
+    "description": "64核心高性能服务器CPU",
+    "subcategory_id": "server",
+    "publish_date": "2022-01-01"
   },
   {
     "id": "epyc9454",
@@ -41,8 +43,9 @@
     "l3_cache_mb": 192,
     "tdp_watts": 290,
     "price_usd": 4100,
-    "release_year": 2022,
-    "description": "48核心服务器CPU"
+    "description": "48核心服务器CPU",
+    "subcategory_id": "server",
+    "publish_date": "2022-01-01"
   },
   {
     "id": "xeonw9359x",
@@ -56,8 +59,9 @@
     "l3_cache_mb": 105,
     "tdp_watts": 350,
     "price_usd": 6200,
-    "release_year": 2023,
-    "description": "Intel顶级工作站CPU"
+    "description": "Intel顶级工作站CPU",
+    "subcategory_id": "server",
+    "publish_date": "2023-01-01"
   },
   {
     "id": "xeonw5345",
@@ -71,8 +75,9 @@
     "l3_cache_mb": 45,
     "tdp_watts": 230,
     "price_usd": 950,
-    "release_year": 2023,
-    "description": "中端工作站CPU"
+    "description": "中端工作站CPU",
+    "subcategory_id": "server",
+    "publish_date": "2023-01-01"
   },
   {
     "id": "ryzen97950x",
@@ -86,8 +91,9 @@
     "l3_cache_mb": 64,
     "tdp_watts": 170,
     "price_usd": 550,
-    "release_year": 2022,
-    "description": "顶级消费级CPU，适合AI开发"
+    "description": "顶级消费级CPU，适合AI开发",
+    "subcategory_id": "desktop",
+    "publish_date": "2022-01-01"
   },
   {
     "id": "ryzen97950x3d",
@@ -101,8 +107,9 @@
     "l3_cache_mb": 144,
     "tdp_watts": 120,
     "price_usd": 700,
-    "release_year": 2023,
-    "description": "带3D V-Cache，游戏性能更强"
+    "description": "带3D V-Cache，游戏性能更强",
+    "subcategory_id": "mobile",
+    "publish_date": "2023-01-01"
   },
   {
     "id": "intel14900k",
@@ -116,8 +123,9 @@
     "l3_cache_mb": 36,
     "tdp_watts": 125,
     "price_usd": 580,
-    "release_year": 2023,
-    "description": "Intel顶级消费级CPU"
+    "description": "Intel顶级消费级CPU",
+    "subcategory_id": "desktop",
+    "publish_date": "2023-01-01"
   },
   {
     "name": "AMD 锐龙 AI 9 H 365",
@@ -134,8 +142,9 @@
     "created_at": "2026-04-20 23:19:20",
     "visible": true,
     "raw_text": "AMD 锐龙 AI 9 H 365\nAMD 锐龙 AI 处理器助力打造卓越 AI PC\n\n \n全部折叠\n一般规格\n名称\nAMD 锐龙 AI 9 H 365\n产品系列\n锐龙\n系列\n锐龙 AI 300 系列\n外形规格\n笔记本电脑 , 台式机\nAMD PRO 技术\n否\n区域供货状况\n中国\n原代号\nStrix Point\n处理器架构\n4x Zen 5 , 6x Zen 5c\nCPU 核心数\n10\n多线程 (SMT)\n是\n线程数\n20\n最高加速时钟频率 \n最高可达 5 GHz\nMax Zen5c Clock \n最高可达 3.3 GHz\n基准时钟频率 \n2 GHz\nZen5 Base Clock\n2 GHz\nZen5c Base Clock\n2 GHz\nL2 高速缓存\n10 MB\nL3 高速缓存\n24 MB\n默认热设计功耗 (TDP)\n28W\nAMD 可配置热设计功耗 (cTDP)\n15-54W\nCPU 核心的处理器工艺\nTSMC 4nm FinFET\n封装芯片计数\n1\nAMD EXPO™ 内存超频技术\n是\n精准频率提升 (PBO)\n是\n曲线优化器电压偏移\n是\nCPU 平台\nFP8\n支持的扩展\nAES , AMD-V , AVX , AVX2 , AVX512 , FMA3 , MMX-plus , SHA , SSE , SSE2 , SSE3 , SSE4.1 , SSE4.2 , SSE4A , SSSE3 , x86-64\n最高工作温度 (Tjmax)\n100°C\n*支持的操作系统\nWindows 11 - 64-Bit Edition , RHEL x86 64-Bit , Ubuntu x86 64-Bit\n连接\nNative USB 4 (40Gbps)\n2\nNative USB 3.2 Gen 2 (10Gbps)\n2\nNative USB 2.0 (480Mbps)\n4\nPCI Express® Version\nPCIe® 4.0\n原生 PCIe® 通道 (总共/可用)\n16 , 16\nNVMe 支持\nBoot , RAID0 , RAID1\n系统内存类型\nDDR5 (FP8) , LPDDR5X (FP8)\n内存通道数\n2\n最大内存\n256 GB\n最高内存速度\n2x2R\tDDR5-5600, LPDDR5x-8000\n支持 ECC\n否\n显卡功能\n显卡型号\nAMD Radeon™ 880M\n显卡核心数\n12\n显卡频率\n2900 MHz\nDirectX® 版本\n12\nDisplayPort™ 版本\n2.1\nDisplayPort 扩展功能\nAdaptive-Sync , HDR Metadata , UHBR10\nDisplayPort 最高刷新率 (SDR)\n7680x4320 @ 60Hz , 3840x2160 @ 240Hz , 3440x1440 @ 360Hz , 2560x1440 @ 480Hz , 1920x1080 @ 600Hz\nDisplayPort 最高刷新率 (HDR)\n7680x4320 @ 60Hz , 3840x2160 @ 240Hz , 3440x1440 @ 360Hz , 2560x1440 @ 480Hz , 1920x1080 @ 600Hz\nHDMI® 版本\n2.1\n支持的 HDCP 版本\n2.3\nUSB Type-C® DisplayPort™ 备用模式\n是\n支持多个显示器\n是\n显示器个数上限\n4\nAMD FreeSync™\n是\n无线显示\nMiracast\n最大视频编码带宽 (SDR)\n1080p630 8bpc H.264, 1440p373 8bpc H.264, 2160p175 8bpc H.264, 1080p630 8bpc H.265, 1440p373 8bpc H.265, 2160p175 8bpc H.265, 4320p43 8bpc H.265, 1080p864 8/10bpc AV1, 1440p513 8/10bpc AV1, 2160p240 8/10bpc AV1, 4320p60 8/10bpc AV1\n\n最大视频解码带宽\n1080p60 8bpc MPEG2, 1080p60 8bpc VC1, 1080p786 8/10bpc VP9, 2160p196 8/10bpc VP9, 4320p49 8/10bpc VP9, 1080p1200 8bpc H.264, 2160p300 8bpc H.264, 4320p75 8bpc H.264, 1080p786 8/10bpc H.265, 2160p196 8/10bpc H.265, 4320p49 8/10bpc H.265, 1080p960 8/10bpc\n\nAMD SmartShift MAX\n是\nAMD 显存智取技术\n支持\nAI 引擎性能\nAMD Ryzen™ AI\n支持\nOverall TOPS\n最高可达 73 TOPS\nNPU TOPS\n最高可达 50 TOPS\n产品 ID\nTray 产品 ID\n100-000001530 (FP8)\n安全\nAMD 增强病毒防护 (NX bit)\n是",
-    "publish_date": "",
+    "publish_date": "2022-01-01",
     "views": 0,
-    "is_pinned": false
+    "is_pinned": false,
+    "subcategory_id": "mobile"
   }
 ]

data/gpus.json

@@ -12,8 +12,9 @@
     "fp16_tflops": 1979,
     "int8_perf_tops": 3958,
     "price_usd": 30000,
-    "release_year": 2022,
-    "description": "数据中心顶级GPU，专为AI训练设计"
+    "description": "数据中心顶级GPU，专为AI训练设计",
+    "subcategory_id": "datacenter",
+    "publish_date": "2022-01-01"
   },
   {
     "id": "a100",
@@ -28,8 +29,9 @@
     "fp16_tflops": 312,
     "int8_perf_tops": 624,
     "price_usd": 10000,
-    "release_year": 2020,
-    "description": "数据中心主力GPU，AI训练推理通用"
+    "description": "数据中心主力GPU，AI训练推理通用",
+    "subcategory_id": "datacenter",
+    "publish_date": "2020-01-01"
   },
   {
     "id": "a10040g",
@@ -44,8 +46,9 @@
     "fp16_tflops": 312,
     "int8_perf_tops": 624,
     "price_usd": 6000,
-    "release_year": 2020,
-    "description": "A100 40GB版本，性价比更高"
+    "description": "A100 40GB版本，性价比更高",
+    "subcategory_id": "datacenter",
+    "publish_date": "2020-01-01"
   },
   {
     "id": "l40s",
@@ -60,8 +63,9 @@
     "fp16_tflops": 362,
     "int8_perf_tops": 724,
     "price_usd": 7000,
-    "release_year": 2023,
-    "description": "新一代数据中心GPU，推理优化"
+    "description": "新一代数据中心GPU，推理优化",
+    "subcategory_id": "datacenter",
+    "publish_date": "2023-01-01"
   },
   {
     "id": "rtx4090",
@@ -76,8 +80,9 @@
     "fp16_tflops": 330,
     "int8_perf_tops": 660,
     "price_usd": 1600,
-    "release_year": 2022,
-    "description": "消费级最强GPU，适合个人AI开发"
+    "description": "消费级最强GPU，适合个人AI开发",
+    "subcategory_id": "gaming",
+    "publish_date": "2022-01-01"
   },
   {
     "id": "rtx4090d",
@@ -92,8 +97,9 @@
     "fp16_tflops": 294,
     "int8_perf_tops": 588,
     "price_usd": 1400,
-    "release_year": 2024,
-    "description": "4090中国特供版，性能略降"
+    "description": "4090中国特供版，性能略降",
+    "subcategory_id": "gaming",
+    "publish_date": "2024-01-01"
   },
   {
     "id": "rtx3090",
@@ -108,8 +114,9 @@
     "fp16_tflops": 142,
     "int8_perf_tops": 284,
     "price_usd": 1200,
-    "release_year": 2020,
-    "description": "上一代旗舰，性价比高"
+    "description": "上一代旗舰，性价比高",
+    "subcategory_id": "gaming",
+    "publish_date": "2020-01-01"
   },
   {
     "id": "rtx3080",
@@ -124,8 +131,9 @@
     "fp16_tflops": 119,
     "int8_perf_tops": 238,
     "price_usd": 700,
-    "release_year": 2020,
-    "description": "中高端消费级GPU"
+    "description": "中高端消费级GPU",
+    "subcategory_id": "gaming",
+    "publish_date": "2020-01-01"
   },
   {
     "id": "v100",
@@ -140,8 +148,9 @@
     "fp16_tflops": 118,
     "int8_perf_tops": 236,
     "price_usd": 4000,
-    "release_year": 2017,
-    "description": "上一代数据中心GPU，仍有价值"
+    "description": "上一代数据中心GPU，仍有价值",
+    "subcategory_id": "datacenter",
+    "publish_date": "2017-01-01"
   },
   {
     "id": "mi300x",
@@ -156,8 +165,9 @@
     "fp16_tflops": 1307,
     "int8_perf_tops": 2614,
     "price_usd": 15000,
-    "release_year": 2023,
-    "description": "AMD最强AI GPU，192GB显存"
+    "description": "AMD最强AI GPU，192GB显存",
+    "subcategory_id": "datacenter",
+    "publish_date": "2023-01-01"
   },
   {
     "name": "RTX 6000D",
@@ -171,7 +181,11 @@
     "raw_text": "据tweaktown报道，NVIDIA为中国市场定制的全新工作站显卡RTX 6000D近日迎来首度拆解。该卡搭载84GB GDDR7显存、19968个CUDA核心，采用被动散热设计，专为服务器机箱风道优化。\n\n\n相较于满血RTX PRO 6000（96GB GDDR7/512-bit），中国特供版RTX 6000D在规格上进行了多处调整。国内团队“技数犬”发布了拆解视频。\n\n据了解，RTX 6000D为无风扇被动散热设计，完全依靠机箱气流降温。\n\nRTX 6000D搭载28颗VRAM模块，总计84GB GDDR7显存，显存总线为448位（相比RTX PRO 6000的96GB/512位有所减少）。\n\nRTX 6000D GPU 核心为156 SM单元，19,968个CUDA核心，比RTX PRO 6000少约17%。\n\nRTX 6000D核心频率为2430MHz（RTX PRO 6000为2600MHz），TDP暂未公布。性能方面，RTX 6000D在Geekbench 6 OpenCL测试中获得390,656分，低于RTX PRO 6000的45–50万分。",
     "currency": "CNY",
     "price_usd": 45000,
-    "updated_at": "2026-04-20 18:28:10"
+    "updated_at": "2026-04-28 11:56:48",
+    "subcategory_id": "professional",
+    "views": 0,
+    "images": [],
+    "publish_date": "2024-01-01"
   },
   {
     "name": "RTX PRO 6000",
@@ -185,7 +199,11 @@
     "cuda_cores": 24064,
     "currency": "CNY",
     "price_usd": 65000,
-    "updated_at": "2026-04-20 18:28:23",
-    "manufacturer": "NVIDIA"
+    "updated_at": "2026-04-28 11:56:38",
+    "manufacturer": "NVIDIA",
+    "subcategory_id": "professional",
+    "views": 0,
+    "images": [],
+    "publish_date": "2020-01-01"
   }
 ]

data/items_021dc76d36be.json

@@ -2,11 +2,16 @@
   {
     "name": "比亚迪宋plus dmi 2021款",
     "brand": "比亚迪",
-    "price": "18.87",
+    "price": 18.87,
     "year": "2021",
     "category_id": "021dc76d36be",
     "id": "3d20dbcd4bdd",
-    "created_at": "2026-04-09 10:09:56"
+    "created_at": "2026-04-09 10:09:56",
+    "subcategory_id": "suv",
+    "views": 0,
+    "images": [],
+    "updated_at": "2026-04-28 12:32:13",
+    "publish_date": "2021-01-01"
   },
   {
     "name": "秦PLUS",
@@ -29,6 +34,8 @@
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     "visible": true,
-    "raw_text": "秦PLUS的外观设计极具现代感和运动气息，前脸采用了家族化设计语言，标志性的大尺寸进气格栅占据了前脸的大部分空间，搭配锐利的LED大灯组，营造出强烈的视觉冲击力。车身线条流畅，腰线从车头贯穿至车尾，增强了整车的运动感。车尾部分，简洁大方的设计与前脸相呼应，整体风格时尚而不失稳重。\n\n上海：秦PLUS优惠促销，最新报价5.98万！轻松开新车\n\n秦PLUS拥有4780*1837*1515mm的长宽高尺寸和2718mm的轴距，赋予其宽敞的内部空间。车侧线条流畅且动感十足，从前轮距1580mm到后轮距1590mm，车轮布局合理，增强了车辆的稳定性和操控性。配备的225/60 R16轮胎规格，匹配独特风格的轮圈，为车辆增添了一抹动感与时尚的气息。\n\n上海：秦PLUS优惠促销，最新报价5.98万！轻松开新车\n\n秦PLUS的内饰风格简洁大气，给人以科技感和舒适感。中控台布局合理，配备了10.1英寸的中控屏幕，支持语音识别控制系统，可轻松操作多媒体系统、导航、电话和空调等功能。方向盘采用皮质材料，手感舒适，支持手动上下和前后调节，方便驾驶员调整到最佳驾驶姿势。座椅采用仿皮材质，主驾驶座椅具备前后调节、靠背调节和高低调节功能，而副驾驶座椅则支持前后调节和靠背调节，确保了乘客的舒适度。后排座椅可以按比例放倒，增加储物空间，同时，车内还配备了USB和Type-C接口，方便乘客为电子设备充电。\n\n上海：秦PLUS优惠促销，最新报价5.98万！轻松开新车\n\n秦PLUS搭载了一台1.5L 101马力的L4发动机，最大功率为74kW，最大扭矩为126N·m。与之匹配的是E-CVT无级变速器，这使得车辆在提供平稳的动力输出的同时，还能有效降低油耗。\n\n汽车之家车主@天艺风云 表示，外观设计是他当初选择秦PLUS的原因之一。他赞赏整体造型时尚大气，龙脸设计搭配犀利的大灯，辨识度极高。车身线条流畅，溜背式造型增添了几分运动感。全新的“龙鳞辉熠”格栅，精致又霸气，每次停车都有人问这是什么车，外观确实很吸引人。"
+    "raw_text": "秦PLUS的外观设计极具现代感和运动气息，前脸采用了家族化设计语言，标志性的大尺寸进气格栅占据了前脸的大部分空间，搭配锐利的LED大灯组，营造出强烈的视觉冲击力。车身线条流畅，腰线从车头贯穿至车尾，增强了整车的运动感。车尾部分，简洁大方的设计与前脸相呼应，整体风格时尚而不失稳重。\n\n上海：秦PLUS优惠促销，最新报价5.98万！轻松开新车\n\n秦PLUS拥有4780*1837*1515mm的长宽高尺寸和2718mm的轴距，赋予其宽敞的内部空间。车侧线条流畅且动感十足，从前轮距1580mm到后轮距1590mm，车轮布局合理，增强了车辆的稳定性和操控性。配备的225/60 R16轮胎规格，匹配独特风格的轮圈，为车辆增添了一抹动感与时尚的气息。\n\n上海：秦PLUS优惠促销，最新报价5.98万！轻松开新车\n\n秦PLUS的内饰风格简洁大气，给人以科技感和舒适感。中控台布局合理，配备了10.1英寸的中控屏幕，支持语音识别控制系统，可轻松操作多媒体系统、导航、电话和空调等功能。方向盘采用皮质材料，手感舒适，支持手动上下和前后调节，方便驾驶员调整到最佳驾驶姿势。座椅采用仿皮材质，主驾驶座椅具备前后调节、靠背调节和高低调节功能，而副驾驶座椅则支持前后调节和靠背调节，确保了乘客的舒适度。后排座椅可以按比例放倒，增加储物空间，同时，车内还配备了USB和Type-C接口，方便乘客为电子设备充电。\n\n上海：秦PLUS优惠促销，最新报价5.98万！轻松开新车\n\n秦PLUS搭载了一台1.5L 101马力的L4发动机，最大功率为74kW，最大扭矩为126N·m。与之匹配的是E-CVT无级变速器，这使得车辆在提供平稳的动力输出的同时，还能有效降低油耗。\n\n汽车之家车主@天艺风云 表示，外观设计是他当初选择秦PLUS的原因之一。他赞赏整体造型时尚大气，龙脸设计搭配犀利的大灯，辨识度极高。车身线条流畅，溜背式造型增添了几分运动感。全新的“龙鳞辉熠”格栅，精致又霸气，每次停车都有人问这是什么车，外观确实很吸引人。",
+    "subcategory_id": "sedan",
+    "publish_date": "2023-01-01"
   }
 ]

data/items_71fa2b4d818f.json

@@ -3,75 +3,81 @@
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     "brand": "DJI",
     "price": 2999,
-    "specs": {
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-      "电池容量": "1545mAh",
-      "工作温度": "0°C至40°C"
-    },
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     "category_id": "71fa2b4d818f",
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     "visible": true,
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-    "images": [
-      "/static/uploads/1ad784e0b3c6_1777305525.png"
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+    "subcategory_id": "90ce312b560d",
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   },
   {
     "name": "Osmo Pocket 3",
     "brand": "DJI",
     "price": 2799,
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-      "电池容量": "1300mAh",
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   {
     "name": "DJI Pocket 2",
     "brand": "DJI",
     "price": 1999,
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-      "视频分辨率": "4K 60fps",
-      "照片最大分辨率": "6272×4680",
-      "电池容量": "875mAh",
-      "工作温度": "0°C至40°C"
-    },
+    "specs": "[object Object]",
     "id": "0fde0f10ad96",
     "category_id": "71fa2b4d818f",
     "created_at": "2026-04-28 00:07:01",
     "visible": true,
     "raw_text": "",
-    "images": [
-      "/static/uploads/1ad784e0b3c6_1777305525.png"
-    ],
-    "publish_date": "",
+    "images": [],
+    "publish_date": "2023-01-01",
     "views": 0,
-    "is_pinned": false
+    "is_pinned": false,
+    "subcategory_id": "90ce312b560d",
+    "updated_at": "2026-04-28 12:32:50"
+  },
+  {
+    "name": "EOS R7",
+    "brand": "佳能 (Canon)",
+    "year": 2022,
+    "specs": "{\n  \"RAW照片输出\": \"14bit\",\n  \"上市时间\": \"2022-06\",\n  \"传感器尺寸\": \"APS-C\",\n  \"传感器类型\": \"CMOS\",\n  \"像素\": \"3000-4000万\",\n  \"功能\": \"Wi-Fi 4K视频 5轴防抖 高速连拍 翻转自拍\",\n  \"取景器类型\": \"电子取景器\",\n  \"品牌\": \"佳能 (Canon)\",\n  \"商品编号\": \"10090975539899\",\n  \"型号\": \"EOS R7\",\n  \"外接电源\": \"支持外接电源\",\n  \"存储介质\": \"SD卡 SDHC卡 SDXC卡\",\n  \"店铺\": \"佳能 (Canon) 数码旗舰店\",\n  \"接口\": \"Wi-Fi 蓝牙 HDMI\",\n  \"最大光圈\": \"F3.5\",\n  \"有效像素\": \"3250万\",\n  \"标准ISO感光度\": \"ISO 100-32000\",\n  \"液晶屏像素\": \"162万\",\n  \"液晶屏尺寸\": \"3.2英寸\",\n  \"液晶屏类型\": \"侧翻屏 旋转屏\",\n  \"滤镜直径\": \"55mm\",\n  \"焦点数量\": \"5915个\",\n  \"电池类型\": \"锂离子电池\",\n  \"类型\": \"机身\",\n  \"视频拍摄能力\": \"4K 60P\",\n  \"视频采样\": \"4:2:2\",\n  \"连拍速度\": \"电子最高约30张/秒，机械最高约15张/秒\",\n  \"适用对象\": \"入门级\",\n  \"镜头卡口\": \"佳能RF卡口\",\n  \"高清摄像\": \"4K超高清视频\"\n}",
+    "description": "入门级机身",
+    "id": "c8c3f124b2ce",
+    "category_id": "71fa2b4d818f",
+    "created_at": "2026-04-28 16:38:03",
+    "visible": true,
+    "raw_text": "",
+    "images": [],
+    "publish_date": "2022-06-01",
+    "views": 0,
+    "is_pinned": false,
+    "updated_at": "2026-04-29 00:24:06",
+    "parse_sources": [
+      {
+        "type": "smart_update",
+        "timestamp": "2026-04-28 23:32:40",
+        "images": [
+          "/static/uploads/deca243eff98_1777390343.png"
+        ],
+        "text": "",
+        "updated_fields": []
+      }
+    ],
+    "subcategory_id": "dslr",
+    "megapixels": "3000"
   }
 ]

data/items_phones.json

@@ -0,0 +1,81 @@
+[
+  {
+    "name": "华为Pura X Max",
+    "brand": "华为",
+    "processor": "麒麟9030 Pro",
+    "screen_size": "7.6",
+    "year": 2026,
+    "description": "全球首款横向阔折叠屏手机，内屏7.6英寸（WQHD+分辨率），外屏5.5英寸，搭载麒麟9030 Pro芯片和鸿蒙6系统，支持AI眼动翻页和手写笔功能，素皮版重约210g",
+    "id": "5ffe89899549",
+    "category_id": "phones",
+    "created_at": "2026-04-28 18:20:59",
+    "visible": true,
+    "raw_text": "华为Pura X Max‌：全球首款横向阔折叠屏手机，内屏7.6英寸（WQHD+分辨率），外屏5.5英寸，搭载麒麟9030 Pro芯片和鸿蒙6系统，支持AI眼动翻页和手写笔功能，素皮版重约210g，2026年4月20日上市。\n华为 Pura X Max 是华为最新推出的大阔折叠屏手机，官方起售价10999 元，提供多种存储版本及配色选择，已在华为商城等渠道正式开售 。更多详情可访问 [华为官网](https://consumer.huawei.com/cn/phones/pura-x-max/specs/) 或 [华为商城](https://item.vmall.com/product/comdetail/index.html?prdId=10086621059876&sbomCode=2601010615007) 。\n版本价格与发售信息\n\n1. 发售时间：于 2026 年 4 月 20 日正式发布，4 月 25 日 10:08 正式开售 。\n2. 官方定价：\n   - 12GB+256GB：10999 元。\n   - 12GB+512GB：11999 元。\n   - 16GB+512GB 典藏版：12999 元。\n   - 16GB+1TB 典藏版：13999 元。\n3. 购买渠道：可通过华为官网及华为商城等官方渠道购买，部分第三方平台价格可能存在波动，建议以官方定价为准 。\n核心硬件配置\n\n1. 屏幕显示：\n   - 内屏：7.7 英寸折叠柔性 OLED，支持 1-120Hz LTPO 2.0 自适应刷新率，分辨率 2584×1828 像素 。\n   - 外屏：5.4 英寸 OLED，支持 1-120Hz LTPO 2.0 自适应刷新率，分辨率 1848×1264 像素 。\n   - 亮度：外屏峰值亮度 3500 尼特，内屏峰值亮度 3000 尼特，户外强光下清晰可见 。\n2. 性能系统：\n   - 处理器：搭载麒麟 9030 Pro 芯片，整机性能提升 30% 。\n   - 操作系统：预装 HarmonyOS 6.1，支持多设备协同 。\n3. 影像系统：\n   - 后置：5000 万像素超光变主摄（F1.4-F4.0）+ 1250 万像素超广角 + 5000 万像素潜望长焦 + 第二代红枫原色摄像头 。\n   - 前置：内外屏均配备 800 万像素摄像头，支持外屏自拍 。\n4. 续航充电：\n   - 电池：5300mAh 典型值，支持 66W 有线超级快充及 50W 无线超级快充 。\n折叠形态与 AI 体验\n\n1. 阔折叠设计：\n   - 采用√2:1 黄金比例设计，内外屏比例一致，接近 A4 纸对折比例，提升阅读和办公体验 。\n   - 机身重量约 229 克，折叠态厚度 11.2mm，展开态厚度 5.2mm，便携性较好 。\n2. AI 功能：\n   - 支持小艺伴随式 AI、AI 灵感妙创、AI 眼动翻页等功能，提升交互效率 。\n   - 首发支持华为 M-Pen 3 Mini 手写笔，适配“天生会画”App，支持动态照片手绘 。\n3. 配色材质：\n   - 提供幻夜黑、橄榄金、星际蓝、活力橙、零度白 5 款配色 。\n   - 外屏采用第二代昆仑玻璃，支持 IP58+IP59 级防尘防水，耐用性增强 。",
+    "images": [],
+    "subcategory_id": "",
+    "publish_date": "2026-01-01",
+    "views": 0,
+    "is_pinned": false,
+    "price": 10999,
+    "specs": {
+      "screen": {
+        "inner": {
+          "size": 7.7,
+          "type": "折叠柔性OLED",
+          "refreshRate": "1-120Hz LTPO 2.0自适应刷新率",
+          "resolution": "2584×1828像素",
+          "brightness": 3000
+        },
+        "outer": {
+          "size": 5.4,
+          "type": "OLED",
+          "refreshRate": "1-120Hz LTPO 2.0自适应刷新率",
+          "resolution": "1848×1264像素",
+          "brightness": 3500
+        }
+      },
+      "performance": {
+        "processor": "麒麟9030 Pro芯片",
+        "os": "HarmonyOS 6.1"
+      },
+      "memory": {
+        "ram": [
+          "12GB",
+          "16GB"
+        ],
+        "storage": [
+          "256GB",
+          "512GB",
+          "1TB"
+        ]
+      },
+      "camera": {
+        "rear": "5000万像素超光变主摄 + 1250万像素超广角 + 5000万像素潜望长焦 + 第二代红枫原色摄像头",
+        "front": "800万像素"
+      },
+      "battery": {
+        "capacity": 5300,
+        "charging": {
+          "wired": 66,
+          "wireless": 50
+        }
+      },
+      "design": {
+        "weight": 229,
+        "thickness": {
+          "folded": 11.2,
+          "unfolded": 5.2
+        },
+        "waterResistance": "IP58+IP59"
+      },
+      "colors": [
+        "幻夜黑",
+        "橄榄金",
+        "星际蓝",
+        "活力橙",
+        "零度白"
+      ]
+    },
+    "updated_at": "2026-04-28 18:29:08"
+  }
+]

data/models.json

@@ -9,11 +9,17 @@
     "input_price": 0.03,
     "output_price": 0.06,
     "mmlu": 86.4,
-    "humaneval": 67.0,
+    "humaneval": 67,
     "is_open_source": false,
     "license": "Proprietary",
     "description": "OpenAI最强大的多模态大模型",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "updated_at": "2026-04-28 11:57:02",
+    "raw_text": "\nGPT-4 Turbo version with 128K context length, price is $10 per 1M input tokens",
+    "subcategory_id": "chat",
+    "views": 0,
+    "images": [],
+    "publish_date": "2023-03-14"
   },
   {
     "id": "gpt4turbo",
@@ -29,7 +35,9 @@
     "is_open_source": false,
     "license": "Proprietary",
     "description": "GPT-4增强版，128K上下文",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "chat",
+    "publish_date": "2023-11-06"
   },
   {
     "id": "gpt35",
@@ -45,7 +53,9 @@
     "is_open_source": false,
     "license": "Proprietary",
     "description": "性价比高的通用模型",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "chat",
+    "publish_date": "2023-03-01"
   },
   {
     "id": "claude3opus",
@@ -61,7 +71,9 @@
     "is_open_source": false,
     "license": "Proprietary",
     "description": "Anthropic最强模型，200K上下文",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "code",
+    "publish_date": "2024-03-04"
   },
   {
     "id": "claude3sonnet",
@@ -77,7 +89,9 @@
     "is_open_source": false,
     "license": "Proprietary",
     "description": "平衡性能与成本",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "chat",
+    "publish_date": "2024-03-04"
   },
   {
     "id": "llama270b",
@@ -93,7 +107,9 @@
     "is_open_source": true,
     "license": "Llama 2 Community",
     "description": "Meta开源大模型，70B参数",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "chat",
+    "publish_date": "2023-07-18"
   },
   {
     "id": "llama3",
@@ -109,7 +125,9 @@
     "is_open_source": true,
     "license": "Llama 3 Community",
     "description": "Meta最新开源模型，性能接近GPT-4",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "code",
+    "publish_date": "2024-04-18"
   },
   {
     "id": "mistral7b",
@@ -125,7 +143,9 @@
     "is_open_source": true,
     "license": "Apache 2.0",
     "description": "小巧高效的开源模型",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "chat",
+    "publish_date": "2023-09-27"
   },
   {
     "id": "mixtral8x7b",
@@ -141,7 +161,9 @@
     "is_open_source": true,
     "license": "Apache 2.0",
     "description": "MoE架构，高效推理",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "chat",
+    "publish_date": "2023-12-11"
   },
   {
     "id": "qwen72b",
@@ -157,7 +179,9 @@
     "is_open_source": true,
     "license": "Apache 2.0",
     "description": "阿里开源大模型，中文能力强",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "chat",
+    "publish_date": "2024-02-05"
   },
   {
     "id": "deepseekv3",
@@ -173,7 +197,9 @@
     "is_open_source": true,
     "license": "MIT",
     "description": "DeepSeek最新模型，性价比极高",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "code",
+    "publish_date": "2024-12-26"
   },
   {
     "id": "glm4",
@@ -190,6 +216,8 @@
     "license": "Proprietary",
     "description": "智谱AI大模型，中文能力强",
     "created_at": "2024-01-01",
-    "visible": false
+    "visible": true,
+    "subcategory_id": "chat",
+    "publish_date": "2024-01-01"
   }
 ]