在 Node.js 后端中集成 AI 功能

在 Node.js 后端服务或云函数中使用 CloudBase AI 模型，支持文本生成、流式响应和图片生成

如何使用

查看如何使用Skill了解详细的使用方法。

测试 Skill

你可以使用以下提示词来测试：

• "在 CloudBase 云函数中集成 AI 模型，实现文本生成功能"
• "创建一个使用 CloudBase AI 模型生成图片的云函数"
• "在 Express 后端服务中使用 CloudBase AI 模型处理用户请求"

Select a prompt to start your AI-native development journey

Skill

rule.md

## When to use this skill

Use this skill for **calling AI models in Node.js backend or CloudBase cloud functions** using `@cloudbase/node-sdk`.

**Use it when you need to:**

- Integrate AI text generation in backend services
- Generate images with Hunyuan Image model
- Call AI models from CloudBase cloud functions
- Server-side AI processing

**Do NOT use for:**

- Browser/Web apps → use `ai-model-web` skill
- WeChat Mini Program → use `ai-model-wechat` skill
- HTTP API integration → use `http-api` skill

---

## Available Providers and Models

CloudBase provides these built-in providers and models:

| Provider | Models | Recommended |
|----------|--------|-------------|
| `hunyuan-exp` | `hunyuan-turbos-latest`, `hunyuan-t1-latest`, `hunyuan-2.0-thinking-20251109`, `hunyuan-2.0-instruct-20251111` | ✅ `hunyuan-2.0-instruct-20251111` |
| `deepseek` | `deepseek-r1-0528`, `deepseek-v3-0324`, `deepseek-v3.2` | ✅ `deepseek-v3.2` |

---

## Installation

```bash
npm install @cloudbase/node-sdk
```

⚠️ **AI feature requires version 3.16.0 or above.** Check with `npm list @cloudbase/node-sdk`.

---

## Initialization

### In Cloud Functions

```js
const tcb = require('@cloudbase/node-sdk');
const app = tcb.init({ env: '<YOUR_ENV_ID>' });

exports.main = async (event, context) => {
  const ai = app.ai();
  // Use AI features
};
```

### Cloud Function Configuration for AI Models

⚠️ **Important:** When creating cloud functions that use AI models (especially `generateImage()` and large language model generation), set a longer timeout as these operations can be slow.

**Using MCP Tool `createFunction`:**

Set the `timeout` parameter in the `func` object:

- **Parameter**: `func.timeout` (number)
- **Unit**: seconds
- **Range**: 1 - 900
- **Default**: 20 seconds (usually too short for AI operations)

**Recommended timeout values:**
- **Text generation (`generateText`)**: 60-120 seconds
- **Streaming (`streamText`)**: 60-120 seconds  
- **Image generation (`generateImage`)**: 300-900 seconds (recommended: 900s)
- **Combined operations**: 900 seconds (maximum allowed)

### In Regular Node.js Server

```js
const tcb = require('@cloudbase/node-sdk');
const app = tcb.init({
  env: '<YOUR_ENV_ID>',
  secretId: '<YOUR_SECRET_ID>',
  secretKey: '<YOUR_SECRET_KEY>'
});

const ai = app.ai();
```

---

## generateText() - Non-streaming

```js
const model = ai.createModel("hunyuan-exp");

const result = await model.generateText({
  model: "hunyuan-2.0-instruct-20251111",  // Recommended model
  messages: [{ role: "user", content: "你好，请你介绍一下李白" }],
});

console.log(result.text);           // Generated text string
console.log(result.usage);          // { prompt_tokens, completion_tokens, total_tokens }
console.log(result.messages);       // Full message history
console.log(result.rawResponses);   // Raw model responses
```

---

## streamText() - Streaming

```js
const model = ai.createModel("hunyuan-exp");

const res = await model.streamText({
  model: "hunyuan-2.0-instruct-20251111",  // Recommended model
  messages: [{ role: "user", content: "你好，请你介绍一下李白" }],
});

// Option 1: Iterate text stream (recommended)
for await (let text of res.textStream) {
  console.log(text);  // Incremental text chunks
}

// Option 2: Iterate data stream for full response data
for await (let data of res.dataStream) {
  console.log(data);  // Full response chunk with metadata
}

// Option 3: Get final results
const messages = await res.messages;  // Full message history
const usage = await res.usage;        // Token usage
```

---

## generateImage() - Image Generation

⚠️ **Image generation is only available in Node SDK**, not in JS SDK (Web) or WeChat Mini Program.

```js
const imageModel = ai.createImageModel("hunyuan-image");

const res = await imageModel.generateImage({
  model: "hunyuan-image",
  prompt: "一只可爱的猫咪在草地上玩耍",
  size: "1024x1024",
  version: "v1.9",
});

console.log(res.data[0].url);           // Image URL (valid 24 hours)
console.log(res.data[0].revised_prompt);// Revised prompt if revise=true
```

### Image Generation Parameters

```ts
interface HunyuanGenerateImageInput {
  model: "hunyuan-image";      // Required
  prompt: string;                       // Required: image description
  version?: "v1.8.1" | "v1.9";         // Default: "v1.8.1"
  size?: string;                        // Default: "1024x1024"
  negative_prompt?: string;             // v1.9 only
  style?: string;                       // v1.9 only
  revise?: boolean;                     // Default: true
  n?: number;                           // Default: 1
  footnote?: string;                    // Watermark, max 16 chars
  seed?: number;                        // Range: [1, 4294967295]
}

interface HunyuanGenerateImageOutput {
  id: string;
  created: number;
  data: Array<{
    url: string;                        // Image URL (24h valid)
    revised_prompt?: string;
  }>;
}
```

---

## Type Definitions

```ts
interface BaseChatModelInput {
  model: string;                        // Required: model name
  messages: Array<ChatModelMessage>;    // Required: message array
  temperature?: number;                 // Optional: sampling temperature
  topP?: number;                        // Optional: nucleus sampling
}

type ChatModelMessage =
  | { role: "user"; content: string }
  | { role: "system"; content: string }
  | { role: "assistant"; content: string };

interface GenerateTextResult {
  text: string;                         // Generated text
  messages: Array<ChatModelMessage>;    // Full message history
  usage: Usage;                         // Token usage
  rawResponses: Array<unknown>;         // Raw model responses
  error?: unknown;                      // Error if any
}

interface StreamTextResult {
  textStream: AsyncIterable<string>;    // Incremental text stream
  dataStream: AsyncIterable<DataChunk>; // Full data stream
  messages: Promise<ChatModelMessage[]>;// Final message history
  usage: Promise<Usage>;                // Final token usage
  error?: unknown;                      // Error if any
}

interface Usage {
  prompt_tokens: number;
  completion_tokens: number;
  total_tokens: number;
}
```