ai-model-wechat

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name: ai-model-wechat description: 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). alwaysApply: false

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

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Available Providers and Models

CloudBase provides these built-in providers and models:

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Prerequisites

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

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Initialization

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

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generateText() - Non-streaming

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

Copyconst 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

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streamText() - Streaming

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

Copyconst 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;
  }
}

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API Comparison: JS/Node SDK vs WeChat Mini Program

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Type Definitions

streamText() Input

Copyinterface 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

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

generateText() Return

Copy// 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;
  };
}

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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: {...}