ai-model-nodejs

name	ai-model-nodejs
description	Use this skill when developing Node.js backend services or CloudBase cloud functions (Express/Koa/NestJS, serverless, backend APIs) that need AI capabilities. Features text generation (generateText), streaming (streamText), AND image generation (generateImage) via @cloudbase/node-sdk ≥3.16.0. Built-in models include Hunyuan (hunyuan-2.0-instruct-20251111 recommended), DeepSeek (deepseek-v3.2 recommended), and hunyuan-image for images. This is the ONLY SDK that supports image generation. NOT for browser/Web apps (use ai-model-web) or WeChat Mini Program (use ai-model-wechat).
alwaysApply	false

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

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

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 manageFunctions(action="createFunction"):

Legacy compatibility: if an older prompt still says createFunction, keep the same payload shape but execute it through manageFunctions(action="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

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

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

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.

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

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

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  {
  : <>;    
  : <>; 
  : <[]>;
  : <>;                
  ?: ;                      
}

  {
  : ;
  : ;
  : ;
}