ai-model-nodejs: Скилл для Node.js и AI генерации

--- name: ai-model-nodejs description: Use this skill when developing Node.js backend services or CloudBase cloud functions (Express/Koa/NestJS, serverless, backend APIs) that need AI capabilities. Features text generation (generateText), streaming (streamText), AND image generation (generateImage) via @cloudbase/node-sdk ≥3.16.0. Built-in models include Hunyuan (hunyuan-2.0-instruct-20251111 recommended), DeepSeek (deepseek-v3.2 recommended), and hunyuan-image for images. This is the ONLY SDK that supports image generation. NOT for browser/Web apps (use ai-model-web) or WeChat Mini Program (use ai-model-wechat). alwaysApply: false --- ## When to use this skill Use this skill for **calling AI models in Node.js backend or CloudBase cloud functions** using `@cloudbase/node-sdk`. **Use it when you need to:** - Integrate AI text generation in backend services - Generate images with Hunyuan Image model - Call AI models from CloudBase cloud functions - Server-side AI processing **Do NOT use for:** - Browser/Web apps → use `ai-model-web` skill - WeChat Mini Program → use `ai-model-wechat` skill - 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/node-sdk ``` ⚠️ **AI feature requires version 3.16.0 or above.** Check with `npm list @cloudbase/node-sdk`. --- ## Initialization ### In Cloud Functions ```js const tcb = require('@cloudbase/node-sdk'); const app = tcb.init({ env: '<YOUR_ENV_ID>' }); exports.main = async (event, context) => { const ai = app.ai(); // Use AI features }; ``` ### Cloud Function Configuration for AI Models ⚠️ **Important:** When creating cloud functions that use AI models (especially `generateImage()` and large language model generation), set a longer timeout as these operations can be slow. **Using MCP Tool `manageFunctions(action="createFunction")`:** Legacy compatibility: if an older prompt still says `createFunction`, keep the same payload shape but execute it through `manageFunctions(action="createFunction")`. Set the `timeout` parameter in the `func` object: - **Parameter**: `func.timeout` (number) - **Unit**: seconds - **Range**: 1 - 900 - **Default**: 20 seconds (usually too short for AI operations) **Recommended timeout values:** - **Text generation (`generateText`)**: 60-120 seconds - **Streaming (`streamText`)**: 60-120 seconds - **Image generation (`generateImage`)**: 300-900 seconds (recommended: 900s) - **Combined operations**: 900 seconds (maximum allowed) ### In Regular Node.js Server ```js const tcb = require('@cloudbase/node-sdk'); const app = tcb.init({ env: '<YOUR_ENV_ID>', secretId: '<YOUR_SECRET_ID>', secretKey: '<YOUR_SECRET_KEY>' }); const ai = app.ai(); ``` --- ## 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 ``` --- ## generateImage() - Image Generation ⚠️ **Image generation is only available in Node SDK**, not in JS SDK (Web) or WeChat Mini Program. ```js const imageModel = ai.createImageModel("hunyuan-image"); const res = await imageModel.generateImage({ model: "hunyuan-image", prompt: "一只可爱的猫咪在草地上玩耍", size: "1024x1024", version: "v1.9", }); console.log(res.data[0].url); // Image URL (valid 24 hours) console.log(res.data[0].revised_prompt);// Revised prompt if revise=true ``` ### Image Generation Parameters ```ts interface HunyuanGenerateImageInput { model: "hunyuan-image"; // Required prompt: string; // Required: image description version?: "v1.8.1" | "v1.9"; // Default: "v1.8.1" size?: string; // Default: "1024x1024" negative_prompt?: string; // v1.9 only style?: string; // v1.9 only revise?: boolean; // Default: true n?: number; // Default: 1 footnote?: string; // Watermark, max 16 chars seed?: number; // Range: [1, 4294967295] } interface HunyuanGenerateImageOutput { id: string; created: number; data: Array<{ url: string; // Image URL (24h valid) revised_prompt?: string; }>; } ``` --- ## 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; } ```