Use this skill when developing browser/Web applications (React/Vue/Angular, static websites, SPAs) that need AI capabilities. Features text generation (generateText) and streaming (streamText) via @cloudbase/js-sdk. Built-in models include Hunyuan (hunyuan-2.0-instruct-20251111 recommended) and DeepSeek (deepseek-v3.2 recommended). NOT for Node.js backend (use ai-model-nodejs), WeChat Mini Program (use ai-model-wechat), or image generation (Node SDK only).
Content & Writing
989 Stars
126 Forks
Updated Apr 13, 2026, 10:29 AM
Why Use This
This skill provides specialized capabilities for TencentCloudBase's codebase.
Use Cases
Developing new features in the TencentCloudBase repository
Refactoring existing code to follow TencentCloudBase standards
Understanding and working with TencentCloudBase's codebase structure
---
name: ai-model-web
description: Use this skill when developing browser/Web applications (React/Vue/Angular, static websites, SPAs) that need AI capabilities. Features text generation (generateText) and streaming (streamText) via @cloudbase/js-sdk. Built-in models include Hunyuan (hunyuan-2.0-instruct-20251111 recommended) and DeepSeek (deepseek-v3.2 recommended). NOT for Node.js backend (use ai-model-nodejs), WeChat Mini Program (use ai-model-wechat), or image generation (Node SDK only).
version: 2.17.1
alwaysApply: false
---
## 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-web/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 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:
| Provider | Models | Recommended |
|----------|--------|-------------|
| `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` |
---
## Installation
```bash
npm install @cloudbase/js-sdk
```
## Initialization
```js
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
```js
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
```js
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
```
---
## Error Handling Pattern
```js
const model = ai.createModel("deepseek");
try {
const result = await model.generateText({
model: "deepseek-v3.2",
messages: [{ role: "user", content: "Generate a concise onboarding checklist" }],
});
console.log(result.text);
} catch (error) {
console.error("Failed to call CloudBase AI from Web", error);
}
```
---
## Type Definitions
```ts
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