Integrate AI in Node.js Backend
Use CloudBase AI models in Node.js backend services or cloud functions for text generation, streaming, and image generation.
How to use
See How to use Skill for detailed usage.
Test the Skill
You can test with these example prompts:
- "Integrate AI model in CloudBase cloud function for text generation"
- "Create a cloud function that uses CloudBase AI model to generate images"
- "Use CloudBase AI model in an Express backend to handle user requests"
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;
}
```