ai-model-wechat

Original：🇺🇸 English
Not Translated

Use this skill when developing WeChat Mini Programs (小程序, 企业微信小程序, wx.cloud-based apps) that need AI capabilities. Features text generation (generateText) and streaming (streamText) with callback support (onText, onEvent, onFinish) via wx.cloud.extend.AI. Built-in models include Hunyuan (hunyuan-2.0-instruct-20251111 recommended) and DeepSeek (deepseek-v3.2 recommended). API differs from JS/Node SDK - streamText requires data wrapper, generateText returns raw response. NOT for browser/Web apps (use ai-model-web), Node.js backend (use ai-model-nodejs), or image generation (not supported).

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Sourcetencentcloudbase/awesome-cloudbase-examples
Added on

NPX Install

npx skill4agent add tencentcloudbase/awesome-cloudbase-examples ai-model-wechat

SKILL.md Content

When to use this skill

Use this skill for calling AI models in WeChat Mini Program using 
wx.cloud.extend.AI
.
Use it when you need to:
  • Integrate AI text generation in a Mini Program
  • Stream AI responses with callback support
  • Call Hunyuan models from WeChat environment
Do NOT use for:
  • Browser/Web apps → use 
    ai-model-web
    skill
  • Node.js backend or cloud functions → use 
    ai-model-nodejs
    skill
  • Image generation → use 
    ai-model-nodejs
    skill (not available in Mini Program)
  • HTTP API integration → use 
    http-api
    skill

Available Providers and Models

CloudBase provides these built-in providers and models:
ProviderModelsRecommended
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

Prerequisites

  • WeChat base library 3.7.1+
  • No extra SDK installation needed

Initialization

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

const res = await model.generateText({
  model: "hunyuan-2.0-instruct-20251111",  // Recommended model
  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.
js
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-2.0-instruct-20251111",  // Recommended model
    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

FeatureJS/Node SDKWeChat Mini Program
Namespace
app.ai()
wx.cloud.extend.AI
generateText paramsDirect objectDirect object
generateText return
{ text, usage, messages }
Raw: 
{ choices, usage }
streamText paramsDirect object⚠️ Wrapped in 
data: {...}
streamText return
{ textStream, dataStream }
{ textStream, eventStream }
CallbacksNot supported
onText
, 
onEvent
, 
onFinish
Image generationNode SDK onlyNot available

Type Definitions

streamText() Input

ts
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
}

streamText() Return

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

generateText() Return

ts
// 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;
  };
}

Best Practices

  1. Check base library version - Ensure 3.7.1+ for AI support
  2. Use callbacks for UI updates - 
    onText
    is great for real-time display
  3. Check for [DONE] - When using 
    eventStream
    , check 
    event.data === "[DONE]"
    to stop
  4. Handle errors gracefully - Wrap AI calls in try/catch
  5. Remember the 
    data
    wrapper     - streamText params must be wrapped in 
    data: {...}