腾讯云CloudBase AI模型Web技能：前端调用混元/DeepSeek模型，实现流式文本生成

🇺🇸English

When to use this skill

Use this skill for calling AI models in browser/Web applications using @cloudbase/js-sdk.

Use it when you need to:

• Integrate AI text generation in a frontend Web app
• Stream AI responses for better user experience
• Call Hunyuan or DeepSeek models from browser

Do NOT use for:

• Node.js backend or cloud functions → use ai-model-nodejs skill
• WeChat Mini Program → use ai-model-wechat skill
• Image generation → use ai-model-nodejs skill (Node SDK only)
• HTTP API integration → use http-api skill

Available Providers and Models

CloudBase provides these built-in providers and models:

Installation

npm install @cloudbase/js-sdk

Initialization

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();

Important notes:

• Always use synchronous initialization with top-level import
• User must be authenticated before using AI features
• Get accessKey from CloudBase console

generateText() - Non-streaming

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

streamText() - Streaming

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

Type Definitions

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;
}

Best Practices

1. Use streaming for long responses - Better user experience
2. Handle errors gracefully - Wrap AI calls in try/catch
3. Keep accessKey secure - Use publishable key, not secret key
4. Initialize early - Initialize SDK in app entry point
5. Ensure authentication - User must be signed in before AI calls

Weekly Installs

536

Repository

tencentcloudbase/skills

GitHub Stars

38

First Seen

Jan 22, 2026

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