# 原始输出格式分析报告

## 概述
本报告分析了各个AI模型提供商的原始输出格式，为设计统一返回体提供参考。

## 分析结果

### direct_api_calls

- **返回类型**: `dict`
- **数据结构**: 
```json
{
  "modelscope_direct": {
    "status_code": "int",
    "headers": {
      "Date": "str",
      "Content-Type": "str",
      "Content-Length": "str",
      "Connection": "str",
      "Set-Cookie": "str",
      "Strict-Transport-Security": "str"
    },
    "data": "str"
  },
  "zhipu_direct": {
    "status_code": "int",
    "headers": {
      "Date": "str",
      "Content-Type": "str",
      "Transfer-Encoding": "str",
      "Connection": "str",
      "Set-Cookie": "str",
      "Last-Modified": "str",
      "Vary": "str",
      "ETag": "str",
      "Content-Encoding": "str",
      "Strict-Transport-Security": "str"
    },
    "html_length": "int",
    "sample_html": "str"
  }
}
```
- **数据样本**: 
```json
{
  "modelscope_direct": "dict (keys: ['status_code', 'headers', 'data']...)",
  "zhipu_direct": "dict (keys: ['status_code', 'headers', 'html_length']...)"
}
```

### iflow

- **返回类型**: `list`
- **数据结构**: 
```json
{
  "list_of": "str",
  "count": 15
}
```
- **数据样本**: 
```json
[
  "tstars2.0",
  "qwen3-coder-plus",
  "qwen3-max",
  "...还有12个元素"
]
```

### modelscope

- **返回类型**: `list`
- **数据结构**: 
```json
{
  "list_of": "str",
  "count": 65
}
```
- **数据样本**: 
```json
[
  "deepseek-ai/DeepSeek-R1-0528",
  "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
  "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
  "...还有62个元素"
]
```

### openrouter

- **返回类型**: `list`
- **数据结构**: 
```json
{
  "list_of": "str",
  "count": 335
}
```
- **数据样本**: 
```json
[
  "openai/gpt-5.1-codex-max",
  "amazon/nova-2-lite-v1:free",
  "amazon/nova-2-lite-v1",
  "...还有332个元素"
]
```

### siliconflow

- **返回类型**: `list`
- **数据结构**: 
```json
{
  "list_of": "str",
  "count": 126
}
```
- **数据样本**: 
```json
[
  "deepseek-ai/DeepSeek-V3.2",
  "Pro/deepseek-ai/DeepSeek-V3.2",
  "deepseek-ai/DeepSeek-V3.1-Terminus",
  "...还有123个元素"
]
```

### zhipu

- **返回类型**: `dict`
- **数据结构**: 
```json
{
  "文本模型": {
    "list_of": "str",
    "count": 12
  },
  "视觉模型": {
    "list_of": "str",
    "count": 7
  },
  "图像生成模型": {
    "list_of": "str",
    "count": 2
  },
  "视频生成模型": {
    "list_of": "str",
    "count": 5
  },
  "音视频模型": {
    "list_of": "str",
    "count": 3
  },
  "向量模型": {
    "list_of": "str",
    "count": 2
  },
  "其他模型": {
    "list_of": "str",
    "count": 4
  },
  "即将弃用模型": {
    "list_of": "str",
    "count": 2
  }
}
```
- **数据样本**: 
```json
{
  "文本模型": "list (length: 12)",
  "视觉模型": "list (length: 7)",
  "图像生成模型": "list (length: 2)",
  "...还有5个键": "..."
}
```

## 统一格式设计建议

### 当前问题
1. **格式不一致**: 有的返回字典，有的返回列表
2. **字段不统一**: 相同信息使用不同字段名
3. **信息缺失**: 有的提供商缺少分类、描述等信息
4. **嵌套层次不同**: 数据结构深度不一致

### 建议的统一格式
```json
{
  "success": true,
  "provider": "provider_name",
  "timestamp": "2025-12-08T19:55:35",
  "data": {
    "models": [
      {
        "id": "model-id",
        "name": "Model Display Name",
        "provider": "provider_name",
        "category": "text/vision/audio",
        "capabilities": ["chat", "vision"],
        "context_length": 128000,
        "description": "Model description"
      }
    ],
    "categories": {
      "text": ["model-1", "model-2"],
      "vision": ["model-3"]
    }
  },
  "metadata": {
    "total_count": 50,
    "source": "api",
    "raw_format": "list/dict"
  }
}
```

### 各提供商转换方案

1. **智谱AI**
   - 输入: 字典，按分类组织
   - 转换: 展平为模型列表，保留分类信息

2. **OpenRouter**
   - 输入: 模型对象列表
   - 转换: 提取id、name等字段，补充缺失信息

3. **ModelScope**
   - 输入: 模型ID字符串列表
   - 转换: 将字符串转换为模型对象，补充name字段

4. **SiliconFlow**
   - 输入: 模型对象列表
   - 转换: 类似OpenRouter，字段映射

5. **IFlow**
   - 输入: 模型对象列表
   - 转换: 提取modelName/showName/id字段

### 实施步骤
1. 创建统一返回体类/数据结构
2. 修改各个提供商的`fetch_models`方法，返回统一格式
3. 更新测试用例验证统一格式
4. 添加格式转换工具函数
5. 文档更新

---
*报告生成时间: 2025-12-08 20:02:21*
