ai-model-wechat（ai 模型微信）

Standalone Install Note

If this environment only installed the current skill, start from the CloudBase main entry and use the published cloudbase/references/... paths for sibling skills.

• CloudBase main entry: https://cnb.cool/tencent/cloud/cloudbase/cloudbase-skills/-/git/raw/main/skills/cloudbase/SKILL.md
• Current skill raw source: https://cnb.cool/tencent/cloud/cloudbase/cloudbase-skills/-/git/raw/main/skills/cloudbase/references/ai-model-wechat/SKILL.md

Keep 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.

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:

Prerequisites

• WeChat base library 3.7.1+
• No extra SDK installation 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-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.

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;
  }
}

Error Handling Pattern

const model = wx.cloud.extend.AI.createModel("deepseek");

try {
  const res = await model.generateText({
    model: "deepseek-v3.2",
    messages: [{ role: "user", content: "生成一段欢迎文案" }],
  });

  console.log(res.choices[0].message.content);
} catch (error) {
  console.error("Mini Program AI request failed", error);
}

API Comparison: JS/Node SDK vs WeChat Mini Program

Type Definitions

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
}

streamText() Return

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

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;
  };
}

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: {...}