When to use this skill

Use this skill for calling AI models using CloudBase across all platforms.

Supported platforms:

How to use this skill (for a coding agent)

1. Identify the target platform - Ask user which platform they're developing for
2. Confirm CloudBase environment - Get env (environment ID) and credentials
3. Pick the appropriate section - Part 1 for JS/Node SDK, Part 3 for WeChat Mini Program
4. Follow CloudBase API shapes exactly - Do not invent new APIs

Part 1: CloudBase JS SDK & Node SDK

JS SDK and Node SDK share the same AI API. Only initialization differs.

Installation

# For Web (Browser)
npm install @cloudbase/js-sdk

# For Node.js (Server/Cloud Functions)
npm install @cloudbase/node-sdk

⚠️ Node SDK AI feature requires version 3.16.0 or above. Check your version with npm list @cloudbase/node-sdk.

Initialization - Web (JS SDK)

import cloudbase from "@cloudbase/js-sdk";

const app = cloudbase.init({
  env: "<YOUR_ENV_ID>",
  accessKey: "<YOUR_PUBLISHABLE_KEY>"  // Get from CloudBase console
});

const auth = app.auth();
await auth.signInAnonymously();

const ai = app.ai();

Initialization - Node.js (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 - same API as JS SDK
};

generateText() - Non-streaming

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

const result = await model.generateText({
  model: "hunyuan-lite",
  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-turbos-latest",
  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 currently only available in Node SDK, not in JS SDK (Web) or WeChat Mini Program.

// Node SDK only
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

Part 2: CloudBase HTTP API

API Endpoint

https://<ENV_ID>.api.tcloudbasegateway.com/v1/ai/<PROVIDER>/v1/chat/completions

cURL - Non-streaming

curl -X POST 'https://<ENV_ID>.api.tcloudbasegateway.com/v1/ai/deepseek/v1/chat/completions' \
  -H 'Authorization: Bearer <YOUR_API_KEY>' \
  -H 'Content-Type: application/json' \
  -d '{"model": "deepseek-r1", "messages": [{"role": "user", "content": "你好"}], "stream": false}'

cURL - Streaming

curl -X POST 'https://<ENV_ID>.api.tcloudbasegateway.com/v1/ai/deepseek/v1/chat/completions' \
  -H 'Authorization: Bearer <YOUR_API_KEY>' \
  -H 'Content-Type: application/json' \
  -H 'Accept: text/event-stream' \
  -d '{"model": "deepseek-r1", "messages": [{"role": "user", "content": "你好"}], "stream": true}'

OpenAI SDK Compatible

const OpenAI = require("openai");

const client = new OpenAI({
  apiKey: "<YOUR_API_KEY>",
  baseURL: "https://<ENV_ID>.api.tcloudbasegateway.com/v1/ai/deepseek/v1",
});

const completion = await client.chat.completions.create({
  model: "deepseek-r1",
  messages: [{ role: "user", content: "你好" }],
  stream: true,
});

for await (const chunk of completion) {
  console.log(chunk);
}

Part 3: WeChat Mini Program

⚠️ WeChat Mini Program API is DIFFERENT from JS/Node SDK. Pay attention to the parameter structure.

Requires base library 3.7.1+. No extra SDK needed.

Initialization

// app.js
App({
  onLaunch: function() {
    wx.cloud.init({ env: "<YOUR_ENV_ID>" });
  }
})

generateText() - Non-streaming

⚠️ Different from JS/Node SDK: Return value is raw model response.

const model = wx.cloud.extend.AI.createModel("hunyuan-exp");

const res = await model.generateText({
  model: "hunyuan-lite",
  messages: [{ role: "user", content: "你好" }],
});

// ⚠️ Return value is RAW model response, NOT wrapped like JS/Node SDK
console.log(res.choices[0].message.content);  // Access via choices array
console.log(res.usage);                        // Token usage

streamText() - Streaming

⚠️ Different from JS/Node SDK: Must wrap parameters in data object, supports callbacks.

const model = wx.cloud.extend.AI.createModel("hunyuan-exp");

// ⚠️ Parameters MUST be wrapped in `data` object
const res = await model.streamText({
  data: {                              // ⚠️ Required wrapper
    model: "hunyuan-lite",
    messages: [{ role: "user", content: "hi" }]
  },
  onText: (text) => {                  // Optional: incremental text callback
    console.log("New text:", text);
  },
  onEvent: ({ data }) => {             // Optional: raw event callback
    console.log("Event:", data);
  },
  onFinish: (fullText) => {            // Optional: completion callback
    console.log("Done:", fullText);
  }
});

// Async iteration also available
for await (let str of res.textStream) {
  console.log(str);
}

// Check for completion with eventStream
for await (let event of res.eventStream) {
  console.log(event);
  if (event.data === "[DONE]") {       // ⚠️ Check for [DONE] to stop
    break;
  }
}

API Comparison: JS/Node SDK vs WeChat Mini Program

Type Definitions

JS/Node SDK - BaseChatModelInput

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 };

JS/Node SDK - generateText() Return

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

JS/Node SDK - streamText() Return

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 DataChunk {
  choices: Array<{
    finish_reason: string;
    delta: ChatModelMessage;
  }>;
  usage: Usage;
  rawResponse: unknown;
}

WeChat Mini Program - streamText() Input

interface WxStreamTextInput {
  data: {                              // ⚠️ Required wrapper object
    model: string;
    messages: Array<{
      role: "user" | "system" | "assistant";
      content: string;
    }>;
  };
  onText?: (text: string) => void;     // Incremental text callback
  onEvent?: (prop: { data: string }) => void;  // Raw event callback
  onFinish?: (text: string) => void;   // Completion callback
}

WeChat Mini Program - streamText() Return

interface WxStreamTextResult {
  textStream: AsyncIterable<string>;   // Incremental text stream
  eventStream: AsyncIterable<{         // Raw event stream
    event?: unknown;
    id?: unknown;
    data: string;                      // "[DONE]" when complete
  }>;
}

WeChat Mini Program - generateText() Return

// Raw model response (OpenAI-compatible format)
interface WxGenerateTextResponse {
  id: string;
  object: "chat.completion";
  created: number;
  model: string;
  choices: Array<{
    index: number;
    message: {
      role: "assistant";
      content: string;
    };
    finish_reason: string;
  }>;
  usage: {
    prompt_tokens: number;
    completion_tokens: number;
    total_tokens: number;
  };
}

HunyuanGenerateImageInput (JS/Node SDK only)

interface HunyuanGenerateImageInput {
  model: "hunyuan-image" | string;      // 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;
  }>;
}

Best Practices

1. Use streaming for long responses - Better user experience
2. Handle errors gracefully - Wrap AI calls in try/catch
3. Keep API Keys secure - Never expose in client-side code
4. Initialize early - Initialize SDK/cloud in app entry point
5. Check for [DONE] - In WeChat Mini Program streaming, check event.data === "[DONE]" to stop