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).
If this environment only installed the current skill, start from the CloudBase main entry and use the published cloudbase/references/... paths for sibling skills.
https://cnb.cool/tencent/cloud/cloudbase/cloudbase-skills/-/git/raw/main/skills/cloudbase/SKILL.mdhttps://cnb.cool/tencent/cloud/cloudbase/cloudbase-skills/-/git/raw/main/skills/cloudbase/references/ai-model-nodejs/SKILL.mdKeep local references/... paths for files that ship with the current skill directory. When this file points to a sibling skill such as auth-tool or web-development, use the standalone fallback URL shown next to that reference.
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:
Do NOT use for:
ai-model-web skillai-model-wechat skillhttp-api skillCloudBase 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 |
npm install @cloudbase/node-sdk
⚠️ AI feature requires version 3.16.0 or above. Check with npm list @cloudbase/node-sdk.
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
};
⚠️ 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:
func.timeout (number)Recommended timeout values:
generateText): 60-120 secondsstreamText): 60-120 secondsgenerateImage): 300-900 seconds (recommended: 900s)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();
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
const model = ai.createModel("deepseek");
try {
const result = await model.generateText({
model: "deepseek-v3.2",
messages: [{ role: "user", content: "Summarize today's deployment logs" }],
});
console.log(result.text);
} catch (error) {
console.error("AI request failed", error);
}
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
⚠️ 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
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;
}>;
}
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;
}