Lao Zhang API

https://docs.laozhang.ai/
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### Verify Installation

Source: https://docs.laozhang.ai/scenarios/programming/codex-cli

Check that Git, Node.js, and npm are correctly installed in your environment.

```bash
git --version
node --version
npm --version
```

--------------------------------

### Parameter Tuning Examples

Source: https://docs.laozhang.ai/api-reference/openai

Configuration examples for temperature, token limits, and stop sequences.

```python
# 不同任务的温度设置
code_generation = {"temperature": 0.2}
casual_chat = {"temperature": 0.7}
creative_writing = {"temperature": 0.9}
```

```python
token_limits = {
    "简短回答": 100,
    "详细解释": 500,
    "代码生成": 2000,
    "长文档": 4000
}
```

```python
# 生成列表时的停止序列
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[...],
    stop=["6.", "\n\n", "总结："]
)
```

--------------------------------

### Install Go OpenAI Package

Source: https://docs.laozhang.ai/api-capabilities/openai-sdk

Use go get to install the sashabaranov/go-openai library.

```bash
go get github.com/sashabaranov/go-openai
```

--------------------------------

### Install OpenAI SDK

Source: https://docs.laozhang.ai/api-capabilities/openai-sdk

Install the required package via npm.

```bash
npm install openai
```

--------------------------------

### Install OpenAI Python SDK

Source: https://docs.laozhang.ai/api-capabilities/openai-sdk

Install the official OpenAI library via pip.

```bash
pip install openai
```

--------------------------------

### Install Python Dependencies

Source: https://docs.laozhang.ai/api-capabilities/veo/examples

Install the required requests library to execute the VEO client.

```bash
pip install requests
```

--------------------------------

### Multi-image Prompt Example

Source: https://docs.laozhang.ai/api-capabilities/flux-image-edit

Example prompt structure for combining multiple reference images into a single coherent output.

```text
Use image 1 as the character, image 2 as the background, and image 3 as the product.
Create one coherent commercial poster.
Preserve the character's clothing, the background lighting, and the product logo.
Do not create a collage or split-screen layout.
```

--------------------------------

### Install VEO API Dependencies

Source: https://docs.laozhang.ai/api-capabilities/veo/examples

Install the axios library to handle HTTP requests to the VEO API.

```bash
npm install axios
```

--------------------------------

### Go SDK Usage

Source: https://docs.laozhang.ai/api-capabilities/openai-sdk

Examples for configuring the Go OpenAI client and performing chat completions.

```APIDOC
## Go SDK

### Installation
`go get github.com/sashabaranov/go-openai`

### Basic Configuration
```go
config := openai.DefaultConfig("YOUR_API_KEY")
config.BaseURL = "https://api2.laozhang.ai/v1"
client := openai.NewClientWithConfig(config)

resp, err := client.CreateChatCompletion(
    context.Background(),
    openai.ChatCompletionRequest{
        Model: openai.GPT3Dot5Turbo,
        Messages: []openai.ChatCompletionMessage{
            {Role: openai.ChatMessageRoleUser, Content: "Hello!"},
        },
    },
)
```

### Streaming
```go
stream, err := client.CreateChatCompletionStream(
    context.Background(),
    openai.ChatCompletionRequest{
        Model: openai.GPT3Dot5Turbo,
        Messages: []openai.ChatCompletionMessage{
            {Role: openai.ChatMessageRoleUser, Content: "Write a haiku"},
        },
        Stream: true,
    },
)
```

--------------------------------

### Define Image Transition Prompts

Source: https://docs.laozhang.ai/api-capabilities/veo/veo-31-troubleshooting

Explicitly guide the model on how to transition between images.

```python
# ❌ 没有过渡指导
prompt = "两张图片"

# ✅ 明确过渡
prompt = "从第一张图平滑过渡到第二张图,采用淡入淡出效果,保持连贯性"
```

--------------------------------

### Install .NET OpenAI Package

Source: https://docs.laozhang.ai/api-capabilities/openai-sdk

Use the dotnet CLI to add the OpenAI package to your project.

```bash
dotnet add package OpenAI
```

--------------------------------

### Prompt Optimization Examples

Source: https://docs.laozhang.ai/api-capabilities/vision-understanding

Comparison between vague and specific prompt structures for better model performance.

```python
# ❌ 不推荐：模糊的提示
prompt = "看看这是什么"

# ✅ 推荐：具体明确的提示
prompt = """
请从以下几个方面分析这张图片：
1. 主要对象：识别图片中的主要物体或人物
2. 场景环境：描述拍摄地点和环境特征
3. 色彩构图：分析配色方案和构图特点
4. 情感氛围：图片传达的情绪或氛围
5. 可能用途：这张图片适合用于什么场景
"""
```

--------------------------------

### Generate Images Implementation

Source: https://docs.laozhang.ai/api-manual

Examples for generating images using cURL, Python SDK, and Node.js.

```bash
curl -X POST "https://api2.laozhang.ai/v1/images/generations" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-2",
    "prompt": "一只可爱的橙色小猫坐在阳光明媚的花园里",
    "n": 1,
    "size": "1024x1024",
    "quality": "hd"
  }'
```

```python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api2.laozhang.ai/v1"
)

response = client.images.generate(
    model="gpt-image-2",  # 推荐使用gpt-image-2
    prompt="一只可爱的橙色小猫坐在阳光明媚的花园里",
    n=1,
    size="1024x1024",
    quality="hd"
)

# 获取图片URL
image_url = response.data[0].url
print(f"生成的图片: {image_url}")

# 下载图片
import requests
img_response = requests.get(image_url)
with open("generated_image.png", "wb") as f:
    f.write(img_response.content)
print("图片已保存为 generated_image.png")
```

```javascript
const OpenAI = require('openai');
const fs = require('fs');

const client = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://api2.laozhang.ai/v1'
});

async function generateImage() {
  try {
    const response = await client.images.generate({
      model: 'gpt-image-2',  // 推荐使用gpt-image-2
      prompt: '一只可爱的橙色小猫坐在阳光明媚的花园里',
      n: 1,
      size: '1024x1024',
      quality: 'hd'
    });

    const imageUrl = response.data[0].url;
    console.log('生成的图片:', imageUrl);

    // 下载图片
    const fetch = require('node-fetch');
    const imgResponse = await fetch(imageUrl);
    const buffer = await imgResponse.buffer();
    fs.writeFileSync('generated_image.png', buffer);
    console.log('图片已保存');

  } catch (error) {
    console.error('生成图片错误:', error);
  }
}

generateImage();
```

--------------------------------

### Prompt Optimization Example

Source: https://docs.laozhang.ai/api-reference/chat-completions

Use system roles to define assistant behavior and set temperature for output consistency.

```json
{
  "model": "gpt-4o-mini",
  "messages": [
    {
      "role": "system",
      "content": "你是一个专业的技术文档写作助手。请用清晰、准确的语言回答问题，如果涉及代码，请提供完整的示例。"
    },
    {
      "role": "user",
      "content": "请解释什么是RESTful API，并给出一个Python示例"
    }
  ],
  "temperature": 0.3
}
```

--------------------------------

### Main Execution Entry Point

Source: https://docs.laozhang.ai/api-capabilities/nano-banana-image

Demonstrates how to initialize the generator and execute an image generation request.

```python
def main():
    """
    主函数示例
    """
    # 配置参数
    API_KEY = "sk-YOUR_API_KEY"  # 请替换为你的实际API密钥（按次计费类型）
    PROMPT = "一只可爱的猫咪在花园里玩耍，阳光明媚，花朵盛开"

    print("="*60)
    print("Nano Banana (Gemini) 图片生成器")
    print("="*60)
    print(f"开始时间: {datetime.datetime.now()}")

    # 创建生成器实例
    generator = GeminiImageGenerator(API_KEY)

    # 生成图片
    success, message = generator.generate_image(PROMPT)

    print("\n" + "="*60)
    if success:
        print("🎉 执行成功！")
        print(f"✅ {message}")
    else:
        print("❌ 执行失败！")
        print(f"💥 {message}")

    print(f"结束时间: {datetime.datetime.now()}")
    print("="*60)

if __name__ == "__main__":
    main()
```

--------------------------------

### Abstract Concept Prompt Example

Source: https://docs.laozhang.ai/api-capabilities/veo/veo-31-best-practices

Example of a descriptive prompt for an abstract concept.

```text
金色能量波纹从中心向外扩散,粒子随着波纹旋转上升,深蓝色渐变背景,流体动画风格,柔和的光晕效果
```

--------------------------------

### Verify Configuration

Source: https://docs.laozhang.ai/scenarios/programming/codex-cli

Confirm that environment variables are set correctly and launch the CLI.

```bash
# 验证环境变量
echo $OPENAI_API_KEY
echo $OPENAI_BASE_URL

# 启动 Codex
codex
```

--------------------------------

### Character Scene Prompt Example

Source: https://docs.laozhang.ai/api-capabilities/veo/veo-31-best-practices

Example of a descriptive prompt for a character-focused scene.

```text
一位年轻女性在雨中漫步,穿着红色雨衣,手持透明雨伞,雨滴在伞面上跳动,霓虹灯光倒映在湿润的街道上,夜晚都市背景,电影感慢镜头
```

--------------------------------

### NanoBanana Pro Usage Examples

Source: https://docs.laozhang.ai/api-capabilities/nano-banana-pro-image-edit

Demonstrates initializing the editor and performing single-image editing or multi-image merging tasks.

```python
def main():
    """主函数 - 使用示例"""

    API_KEY = "sk-YOUR_API_KEY"

    editor = NanaBananaProEditor(API_KEY)

    # 示例1: 单图编辑
    success, message = editor.edit_image(
        image_path="./input.jpg",
        prompt="Add a rainbow in the sky",
        aspect_ratio="16:9",
        image_size="2K"
    )
    print(message)

    # 示例2: 多图合并
    success, message = editor.merge_images(
        image_paths=["./cat.jpg", "./dog.jpg"],
        prompt="Combine these two pets into one happy family portrait",
        aspect_ratio="1:1",
        image_size="2K"
    )
    print(message)


if __name__ == "__main__":
    main()
```

--------------------------------

### Natural Scene Prompt Example

Source: https://docs.laozhang.ai/api-capabilities/veo/veo-31-best-practices

Example of a descriptive prompt for a natural scene.

```text
一只橙色的小猫在绿色草地上追逐蝴蝶,阳光透过树叶洒下斑驳光影,微风吹动草叶,背景是模糊的森林,电影般的景深效果
```

--------------------------------

### Manage Multiple Service Providers

Source: https://docs.laozhang.ai/api-capabilities/openai-sdk

Demonstrates switching between LaoZhang and OpenAI clients within a single class.

```python
class MultiProviderClient:
    def __init__(self):
        self.laozhang_client = OpenAI(
            api_key="LaoZhang_KEY",
            base_url="https://api2.laozhang.ai/v1"
        )
        self.openai_client = OpenAI(api_key="OPENAI_KEY")
    
    def chat(self, message: str, provider: str = "laozhang"):
        client = self.laozhang_client if provider == "laozhang" else self.openai_client
        return client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": message}]
        )
```

--------------------------------

### Configure Go Client

Source: https://docs.laozhang.ai/api-capabilities/openai-sdk

Set up the Go client with a custom BaseURL and execute a chat completion request.

```go
package main

import (
    "context"
    "fmt"
    "github.com/sashabaranov/go-openai"
)

func main() {
    config := openai.DefaultConfig("YOUR_API_KEY")
    config.BaseURL = "https://api2.laozhang.ai/v1"
    
    client := openai.NewClientWithConfig(config)
    
    resp, err := client.CreateChatCompletion(
        context.Background(),
        openai.ChatCompletionRequest{
            Model: openai.GPT3Dot5Turbo,
            Messages: []openai.ChatCompletionMessage{
                {
                    Role:    openai.ChatMessageRoleUser,
                    Content: "Hello!",
                },
            },
        },
    )
    
    if err != nil {
        fmt.Printf("Error: %v\n", err)
        return
    }
    
    fmt.Println(resp.Choices[0].Message.Content)
}
```

--------------------------------

### Python Minimal Client Implementation

Source: https://docs.laozhang.ai/api-capabilities/wan-video-generation

A complete script demonstrating how to submit a generation request, poll for completion, and download the resulting video.

```python
import json
import time
import urllib.request
from urllib.error import HTTPError

BASE_URL = "https://api2.laozhang.ai"
API_KEY = "YOUR_API_KEY"


def request(method, path, body=None, extra_headers=None):
    headers = {"Authorization": f"Bearer {API_KEY}"}
    if extra_headers:
        headers.update(extra_headers)
    data = None
    if body is not None:
        headers["Content-Type"] = "application/json"
        data = json.dumps(body, ensure_ascii=False).encode("utf-8")
    req = urllib.request.Request(BASE_URL + path, data=data, method=method, headers=headers)
    try:
        with urllib.request.urlopen(req, timeout=60) as resp:
            return json.loads(resp.read().decode("utf-8"))
    except HTTPError as exc:
        error_body = exc.read().decode("utf-8", errors="replace")
        raise RuntimeError(f"HTTP {exc.code}: {error_body}") from exc


created = request(
    "POST",
    "/wan/api/v1/services/aigc/video-generation/video-synthesis",
    {
        "model": "wan2.7-t2v",
        "input": {"prompt": "黄昏海边的灯塔，电影级光影，镜头缓慢推进"},
        "parameters": {"resolution": "720P", "duration": 5, "prompt_extend": True},
    },
    {"X-DashScope-Async": "enable"},
)

task_id = created["output"]["task_id"]

while True:
    task = request("GET", f"/v1/tasks/{task_id}")
    if task["status"] == "completed":
        # result_url 是签名下载地址，不要附加 Authorization 头。
        urllib.request.urlretrieve(task["result_url"], "wan-output.mp4")
        break
    if task["status"] == "failed":
        raise RuntimeError(task.get("error") or task.get("fail_reason"))
    time.sleep(10)
```

--------------------------------

### Python API Integration

Source: https://docs.laozhang.ai/getting-started

Complete examples for initializing the OpenAI client and using environment variables for secure key management.

```python
# 安装：pip install openai
from openai import OpenAI

# 初始化客户端
client = OpenAI(
    api_key="您的API密钥",  # 在老张API获取
    base_url="https://api2.laozhang.ai/v1"  # 接入地址
)

# 调用不同模型的示例
def test_models():
    models = [
        "gemini-3.5-flash-lite",  # 低延迟、高吞吐
        "claude-sonnet-5",  # 编程和 Agent
        "gemini-3.6-flash"  # 通用多模态
    ]

    for model in models:
        try:
            response = client.chat.completions.create(
                model=model,
                messages=[
                    {"role": "system", "content": "你是一个友好的AI助手"},
                    {"role": "user", "content": "用一句话介绍你自己"}
                ],
                temperature=0.7,
                max_tokens=100
            )
            print(f"{model}: {response.choices[0].message.content}")
            print(f"使用Token数: {response.usage.total_tokens}\n")
        except Exception as e:
            print(f"{model} 调用失败: {e}\n")

if __name__ == "__main__":
    test_models()
```

```python
import os
client = OpenAI(
    api_key=os.getenv("LAOZHANG_API_KEY"),
    base_url="https://api2.laozhang.ai/v1"
)
```

--------------------------------

### Abstract Concept Avoidance Example

Source: https://docs.laozhang.ai/api-capabilities/veo/veo-31-best-practices

Example of a poor, overly simplistic prompt for an abstract concept.

```text
能量波动
```

--------------------------------

### Character Scene Avoidance Example

Source: https://docs.laozhang.ai/api-capabilities/veo/veo-31-best-practices

Example of a poor, overly simplistic prompt for a character scene.

```text
女人走路
```

--------------------------------

### OpenAI SDK Integration for Veo 3.1

Source: https://docs.laozhang.ai/api-capabilities/veo/official-forward

Demonstrates how to initialize the client, create a video generation task, poll for completion, and download the resulting video file.

```APIDOC
## OpenAI SDK Integration

### Description
Use the OpenAI SDK to interact with Veo 3.1 models. The `base_url` must be set to `https://api2.laozhang.ai/v1`.

### SDK Methods
- **client.videos.create(model, prompt, seconds, size, input_reference)**: Initiates a video generation task.
- **client.videos.retrieve(video_id)**: Retrieves the status of a video generation task.
- **client.videos.download_content(video_id)**: Downloads the generated video content.

### Usage Example
```python
import time
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api2.laozhang.ai/v1",
)

# Create video
video = client.videos.create(
    model="veo-3.1-fast-generate-preview",
    prompt="A rainy neon street, a delivery robot driving through reflections, cinematic camera pan",
    seconds="4",
    size="1280x720",
)

video_id = video.id

# Poll for status
while True:
    video = client.videos.retrieve(video_id)
    if video.status == "completed":
        break
    if video.status == "failed":
        raise RuntimeError(video.error)
    time.sleep(10)

# Download
content = client.videos.download_content(video_id)
content.write_to_file("veo-output.mp4")
```
```

--------------------------------

### Natural Scene Avoidance Example

Source: https://docs.laozhang.ai/api-capabilities/veo/veo-31-best-practices

Example of a poor, overly simplistic prompt for a natural scene.

```text
猫玩耍
```

--------------------------------

### Model Configuration Examples

Source: https://docs.laozhang.ai/api-reference/openai

Configuration snippets for different task types including chat, coding, reasoning, and image analysis.

```python
# 简单对话可先选轻量模型
model = "gpt-4o-mini"
temperature = 0.7      # 自然对话
```

```python
# 代码生成和调试
model = "gpt-5.5"      # 最新代码能力
temperature = 0.2      # 精确性优先
max_tokens = 2000      # 足够长的代码
```

```python
# 数学、逻辑、科学问题
model = "o3-pro"   # 最强推理能力
# 注意：推理模型可能不支持temperature等参数
```

```python
# 图像分析和理解
model = "gpt-5.5"      # 最新多模态能力
detail = "high"        # 高清晰度分析
```

--------------------------------

### Character Creation Response Example

Source: https://docs.laozhang.ai/api-capabilities/sora2/character

Example JSON response returned after successfully creating a character.

```json
{
  "id": "ch_6940d0068884819191a38537b5f48e10",
  "username": "ae27a25bd.dairymuse",
  "display_name": "Daisy the Dairy Muse",
  "permalink": "https://sora.chatgpt.com/profile/ae27a25bd.dairymuse",
  "profile_picture_url": "https://mycdn-gg.oss-us-west-1.aliyuncs.com/sora/95f24b0e-ffd6-43ce-83b8-5575b07d2efc.jpg"
}
```

--------------------------------

### Initialize Nano Banana Pro API Client

Source: https://docs.laozhang.ai/api-capabilities/nano-banana-pro-image-edit

Constructor for the API client, setting up the base URL and authentication headers.

```python
def __init__(self, api_key: str):
    self.api_key = api_key
    self.api_url = "https://api2.laozhang.ai/v1beta/models/gemini-3-pro-image:generateContent"
    self.headers = {
        "Content-Type": "application/json",
        "x-goog-api-key": api_key
    }
```

--------------------------------

### Install Codex CLI via npm

Source: https://docs.laozhang.ai/scenarios/programming/codex-cli

Install the CLI globally using the npm package manager.

```bash
npm install -g @openai/codex
```

--------------------------------

### Python SDK Implementation

Source: https://docs.laozhang.ai/api-capabilities/sora2/official-forward

Complete Python implementation for creating, polling, and downloading videos.

```python
import requests
import time

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api2.laozhang.ai"

headers = {
    "Authorization": f"Bearer {API_KEY}"
}

# 文生视频
def create_video(prompt, model="sora-2", seconds="8", size="1280x720"):
    response = requests.post(
        f"{BASE_URL}/v1/videos",
        headers=headers,
        data={
            "model": model,
            "prompt": prompt,
            "seconds": seconds,
            "size": size
        }
    )
    response.raise_for_status()
    return response.json()

# 图生视频
def create_video_from_image(prompt, image_path, model="sora-2", seconds="4", size="1280x720"):
    with open(image_path, "rb") as f:
        response = requests.post(
            f"{BASE_URL}/v1/videos",
            headers=headers,
            data={
                "model": model,
                "prompt": prompt,
                "seconds": seconds,
                "size": size
            },
            files={
                "input_reference": (image_path.split("/")[-1], f, "image/jpeg")
            }
        )
    response.raise_for_status()
    return response.json()

# 轮询任务状态
def poll_status(video_id, timeout=600, interval=15):
    start_time = time.time()
    while time.time() - start_time < timeout:
        response = requests.get(
            f"{BASE_URL}/v1/videos/{video_id}",
            headers=headers
        )
        response.raise_for_status()
        data = response.json()

        status = data.get("status")
        progress = data.get("progress", 0)
        print(f"状态: {status}, 进度: {progress}%")

        if status == "completed":
            return data
        elif status == "failed":
            raise Exception(f"视频生成失败: {data.get('error')}")

        time.sleep(interval)

    raise TimeoutError("视频生成超时")

# 下载视频
def download_video(video_id, output_path="output.mp4"):
    response = requests.get(
        f"{BASE_URL}/v1/videos/{video_id}/content",
        headers=headers
    )
    response.raise_for_status()

    with open(output_path, "wb") as f:
        f.write(response.content)
    print(f"视频已保存到: {output_path}")

# 完整流程示例
if __name__ == "__main__":
    # 文生视频
    job = create_video(
        prompt="A golden retriever playing fetch on a sunny beach",
        model="sora-2",
        seconds="8",
        size="1280x720"
    )
    print(f"任务已创建: {job['id']}")

    # 等待完成
    result = poll_status(job["id"])
    print("视频生成完成!")

    # 下载视频
    download_video(job["id"], "my_video.mp4")

    # 图生视频示例
    # job2 = create_video_from_image(
    #     prompt="The scene comes alive with gentle movement",
    #     image_path="reference.jpg",
    #     model="sora-2",
    #     seconds="4",
    #     size="1280x720"
    # )
```

--------------------------------

### Task Status Response Example

Source: https://docs.laozhang.ai/api-capabilities/seedance2-video-generation

Example JSON response returned when querying a successful video generation task.

```json
{
  "id": "cgt-example-task-id",
  "model": "doubao-seedance-2-0-fast-260128",
  "status": "succeeded",
  "ratio": "16:9",
  "duration": 4,
  "resolution": "720p",
  "content": {
    "video_url": "https://example.com/generated-video.mp4",
    "last_frame_url": "https://example.com/last-frame.jpeg"
  },
  "usage": {
    "completion_tokens": 87300,
    "total_tokens": 87300
  }
}
```

--------------------------------

### Configure via Environment Variables

Source: https://docs.laozhang.ai/api-capabilities/openai-sdk

Set API credentials via process.env to allow the client to initialize without explicit arguments.

```javascript
// 设置环境变量
process.env.OPENAI_API_KEY = 'YOUR_API_KEY';
process.env.OPENAI_BASE_URL = 'https://api2.laozhang.ai/v1';

import OpenAI from 'openai';

// 使用默认配置
const openai = new OpenAI();

const response = await openai.chat.completions.create({
  model: 'gpt-4',
  messages: [{ role: 'user', content: 'Explain AI to a 5-year-old' }]
});
```

--------------------------------

### .NET SDK Usage

Source: https://docs.laozhang.ai/api-capabilities/openai-sdk

Examples for configuring the OpenAI .NET client and performing chat completions.

```APIDOC
## .NET SDK

### Installation
`dotnet add package OpenAI`

### Basic Configuration
```csharp
using OpenAI;
using OpenAI.Chat;

var client = new OpenAIClient("YOUR_API_KEY", new OpenAIClientOptions
{
    Endpoint = new Uri("https://api2.laozhang.ai/v1")
});

var chatClient = client.GetChatClient("gpt-3.5-turbo");
var response = await chatClient.CompleteChatAsync("Hello!");
```

### Streaming
```csharp
await foreach (var update in chatClient.CompleteChatStreamingAsync("Tell me a story"))
{
    if (update.ContentUpdate.Count > 0)
    {
        Console.Write(update.ContentUpdate[0].Text);
    }
}
```

--------------------------------

### Chat Completions Request Examples

Source: https://docs.laozhang.ai/api-reference/chat-completions

Examples showing how to send a chat completion request to the Laozhang AI API using various programming languages and tools.

```bash
curl https://api2.laozhang.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [
      {
        "role": "system",
        "content": "你是一个专业的AI助手，请用中文回答问题。"
      },
      {
        "role": "user",
        "content": "请介绍一下老张API平台的特色功能"
      }
    ],
    "temperature": 0.7,
    "max_tokens": 500
  }'
```

```javascript
import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://api2.laozhang.ai/v1'
});

async function chatCompletion() {
  const completion = await openai.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [
      {
        role: 'system',
        content: '你是一个专业的AI助手，请用中文回答问题。'
      },
      {
        role: 'user',
        content: '请介绍一下老张API平台的特色功能'
      }
    ],
    temperature: 0.7,
    max_tokens: 500
  });

  console.log(completion.choices[0].message.content);
}

chatCompletion();
```

```python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api2.laozhang.ai/v1"
)

completion = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {
            "role": "system",
            "content": "你是一个专业的AI助手，请用中文回答问题。"
        },
        {
            "role": "user",
            "content": "请介绍一下老张API平台的特色功能"
        }
    ],
    temperature=0.7,
    max_tokens=500
)

print(completion.choices[0].message.content)
```

```go
package main

import (
    "context"
    "fmt"
    "github.com/sashabaranov/go-openai"
)

func main() {
    config := openai.DefaultConfig("YOUR_API_KEY")
    config.BaseURL = "https://api2.laozhang.ai/v1"
    
    client := openai.NewClientWithConfig(config)
    
    resp, err := client.CreateChatCompletion(
        context.Background(),
        openai.ChatCompletionRequest{
            Model: openai.GPT4oMini,
            Messages: []openai.ChatCompletionMessage{
                {
                    Role:    openai.ChatMessageRoleSystem,
                    Content: "你是一个专业的AI助手，请用中文回答问题。",
                },
                {
                    Role:    openai.ChatMessageRoleUser,
                    Content: "请介绍一下老张API平台的特色功能",
                },
            },
            Temperature: 0.7,
            MaxTokens:   500,
        },
    )
    
    if err != nil {
        fmt.Printf("Error: %v\n", err)
        return
    }
    
    fmt.Println(resp.Choices[0].Message.Content)
}
```

--------------------------------

### Create a Start and End Frame Video

Source: https://docs.laozhang.ai/api-capabilities/veo/official-forward

Generates a video transition between a start and end frame using a JSON request body. Data URIs are required for the images.

```bash
curl -X POST "https://api2.laozhang.ai/v1/videos" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "veo-3.1-fast-generate-preview",
    "prompt": "Create an 8-second cinematic transition with smooth camera motion. Do not add captions or on-screen text.",
    "size": "1920x1080",
    "duration": "8",
    "images": ["data:image/jpeg;base64,BASE64_FIRST_FRAME"],
    "metadata": {
      "lastFrame": "data:image/jpeg;base64,BASE64_LAST_FRAME"
    }
  }'
```

--------------------------------

### Install Codex CLI from Source

Source: https://docs.laozhang.ai/scenarios/programming/codex-cli

Clone the repository and link the package locally for development or custom builds.

```bash
# 克隆仓库
git clone https://github.com/openai/codex.git
cd codex

# 安装依赖
npm install

# 全局链接
npm link
```

--------------------------------

### GET /v1/models

Source: https://docs.laozhang.ai/api-reference/models

获取当前可用的模型列表及其详细信息。

```APIDOC
## GET /v1/models

### Description
获取系统中所有可用 AI 模型的列表，包括模型 ID 和所有者信息。

### Method
GET

### Endpoint
https://api2.laozhang.ai/v1/models

### 错误处理
- 401: API Key无效
- 403: 权限不足
- 404: 模型不存在
- 429: 请求过于频繁
- 500: 服务器错误
```

--------------------------------

### Execute complete video-to-character workflow in Python

Source: https://docs.laozhang.ai/api-capabilities/sora2/character

Demonstrates the full sequence of generating a video, polling for completion, creating a character from the result, and using that character in a new prompt.

```python
import requests
import openai
import time

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api2.laozhang.ai"

# 步骤 1：生成初始视频（异步 API）
print("步骤 1：生成初始视频...")
video_response = requests.post(
    f"{BASE_URL}/v1/videos",
    headers={
        "Content-Type": "application/json",
        "Authorization": f"Bearer {API_KEY}"
    },
    json={
        "model": "sora-2",
        "prompt": "一个可爱的卡通角色在跳舞",
        "size": "1280x720",
        "seconds": "10"
    }
)
task = video_response.json()
task_id = task["id"]
print(f"视频任务 ID: {task_id}")

# 步骤 2：等待视频生成完成
print("步骤 2：等待视频生成...")
while True:
    status_response = requests.get(
        f"{BASE_URL}/v1/videos/{task_id}",
        headers={"Authorization": f"Bearer {API_KEY}"}
    )
    status = status_response.json()
    if status["status"] == "completed":
        print("视频生成完成！")
        break
    elif status["status"] == "failed":
        print("视频生成失败")
        exit(1)
    time.sleep(10)

# 步骤 3：从视频创建角色
print("步骤 3：创建角色...")
character_response = requests.post(
    f"{BASE_URL}/sora/v1/characters",
    headers={
        "Content-Type": "application/json",
        "Authorization": f"Bearer {API_KEY}"
    },
    json={
        "model": "sora-2-character",
        "from_task": task_id,
        "timestamps": "1,3"
    }
)
character = character_response.json()
username = character["username"]
print(f"角色创建成功！Username: @{username}")

# 步骤 4：使用角色生成新视频
print("步骤 4：使用角色生成新视频...")
new_video_response = requests.post(
    f"{BASE_URL}/v1/videos",
    headers={
        "Content-Type": "application/json",
        "Authorization": f"Bearer {API_KEY}"
    },
    json={
        "model": "sora-2",
        "prompt": f"@{username} 在舞台上表演，观众欢呼",
        "size": "1280x720",
        "seconds": "10"
    }
)
new_task = new_video_response.json()
print(f"新视频任务 ID: {new_task['id']}")
```

--------------------------------

### GET /v1/videos/{id}

Source: https://docs.laozhang.ai/api-capabilities/sora2/async-api

查询指定任务的当前生成进度和状态。

```APIDOC
## GET /v1/videos/{id}

### Description
通过任务 ID 定期轮询查询生成进度。

### Method
GET

### Endpoint
https://api2.laozhang.ai/v1/videos/{id}

### Parameters
#### Path Parameters
- **id** (string) - Required - 任务 ID

### Response
#### Success Response (200)
- **id** (string) - 任务 ID
- **status** (string) - 任务状态（in_progress, completed 等）
- **progress** (integer) - 完成百分比
- **url** (string) - 任务完成后返回的视频下载路径

### Response Example
{
  "id": "video_abc123",
  "status": "completed",
  "progress": 100,
  "url": "/v1/videos/video_abc123/content"
}
```

--------------------------------

### Generate Video with OpenAI SDK

Source: https://docs.laozhang.ai/api-capabilities/veo/official-forward

Demonstrates the full workflow for text-to-video generation, including client initialization, video creation, status polling, and content downloading.

```python
import time
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api2.laozhang.ai/v1",
)

video = client.videos.create(
    model="veo-3.1-fast-generate-preview",
    prompt="A rainy neon street, a delivery robot driving through reflections, cinematic camera pan",
    seconds="4",
    size="1280x720",
)

video_id = video.id

while True:
    video = client.videos.retrieve(video_id)
    if video.status == "completed":
        break
    if video.status == "failed":
        raise RuntimeError(video.error)
    time.sleep(10)

content = client.videos.download_content(video_id)
content.write_to_file("veo-output.mp4")
```

--------------------------------

### GET /v1/videos/{id}/content

Source: https://docs.laozhang.ai/api-capabilities/seedance2-video-generation

兼容下载已成功任务的视频文件。

```APIDOC
## GET /v1/videos/{id}/content

### Description
直接下载已成功任务的视频文件。

### Method
GET

### Endpoint
https://api2.laozhang.ai/v1/videos/{id}/content

### Parameters
#### Path Parameters
- **id** (string) - Required - 任务唯一标识符
```

--------------------------------

### GET /v1/models

Source: https://docs.laozhang.ai/api-manual

Retrieves a list of available models.

```APIDOC
## GET /v1/models

### Description
Retrieves a list of available models.

### Method
GET

### Endpoint
/v1/models

### Response
#### Response Example
{
  "object": "list",
  "data": [
    {
      "id": "gemini-3.6-flash",
      "object": "model",
      "created": 1677610602,
      "owned_by": "google"
    },
    {
      "id": "claude-sonnet-5",
      "object": "model",
      "created": 1687882411,
      "owned_by": "anthropic"
    }
  ]
}
```

--------------------------------

### Configure OpenAI Client

Source: https://docs.laozhang.ai/api-capabilities/openai-sdk

Initialize the OpenAI client with a custom base URL.

```javascript
import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://api2.laozhang.ai/v1'
});

const response = await openai.chat.completions.create({
  model: 'gpt-3.5-turbo',
  messages: [{ role: 'user', content: 'Hello!' }]
});

console.log(response.choices[0].message.content);
```

--------------------------------

### GET /v1/models/{model}

Source: https://docs.laozhang.ai/api-reference/models

获取指定模型 ID 的详细信息。

```APIDOC
## GET /v1/models/{model}

### Description
获取特定模型的详细信息，包括模型 ID、创建时间、所有者及权限列表。

### Method
GET

### Endpoint
https://api2.laozhang.ai/v1/models/{model}

### Parameters
#### Path Parameters
- **model** (string) - Required - 模型的唯一标识符

### Response
#### Success Response (200)
- **id** (string) - 模型的唯一标识符
- **object** (string) - 对象类型，固定为 "model"
- **created** (integer) - 模型创建时间戳
- **owned_by** (string) - 模型所有者
- **permission** (array) - 模型权限列表
- **root** (string) - 根模型名称
- **parent** (string) - 父模型名称（如果有）

#### Response Example
{
  "id": "gpt-4o",
  "object": "model",
  "created": 1687882411,
  "owned_by": "openai",
  "permission": [
    {
      "id": "modelperm-xxx",
      "object": "model_permission",
      "created": 1687882411,
      "allow_create_engine": false,
      "allow_sampling": true,
      "allow_logprobs": true,
      "allow_search_indices": false,
      "allow_view": true,
      "allow_fine_tuning": false,
      "organization": "*",
      "group": null,
      "is_blocking": false
    }
  ],
  "root": "gpt-4o",
  "parent": null
}
```

--------------------------------

### GET /veo/v1/api/video/status/{taskId}

Source: https://docs.laozhang.ai/api-capabilities/veo/quick-start

根据任务 ID 查询视频生成任务的当前状态及结果。

```APIDOC
## GET /veo/v1/api/video/status/{taskId}

### Description
使用任务 ID 查询视频生成任务的进度或获取最终生成的视频 URL。

### Method
GET

### Endpoint
https://api2.laozhang.ai/veo/v1/api/video/status/{taskId}

### Parameters
#### Path Parameters
- **taskId** (string) - Required - 提交任务时返回的任务唯一标识符

### Response
#### Success Response (200)
- **success** (boolean) - 请求是否成功
- **data** (object) - 包含 taskId, status, 以及完成后的 result 对象

#### Response Example
{
  "success": true,
  "data": {
    "taskId": "veo3:b873872e-7358-4c2f-8d55-bd23f000e14e",
    "status": "completed",
    "result": {
      "video_url": "https://filesystem.site/cdn/20250705/BFvep0SngrqIrN3yDSFChIXAz2mU0M.mp4",
      "video_media_id": "CAUSJGRlNzE5MzRhLTljMDgtNDE1Mi05NWVlLThjOTlhMTZlODUyYxokY2U4ZjNiYmUtNGFmZS00NTExLWI4ZDEtMGM1MTFkM2ZlNGIxIgNDQUUqJDJlNTY3OWUyLWQ2YmQtNGVlZS05ZTUwLWMyMWE0MWU3ZDYzNw"
    }
  }
}
```

--------------------------------

### GET /contents/generations/tasks/{id}

Source: https://docs.laozhang.ai/api-capabilities/seedance2-video-generation

根据任务 ID 查询视频生成任务的当前状态与结果。

```APIDOC
## GET /contents/generations/tasks/{id}

### Description
查询任务状态与结果。成功后可从返回的 content.video_url 读取签名下载地址。

### Method
GET

### Endpoint
https://api2.laozhang.ai/seedance/api/v3/contents/generations/tasks/{id}

### Parameters
#### Path Parameters
- **id** (string) - Required - 任务唯一标识符
```
Tokens:
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