HaHafeng
31d555f7bb
- Add platform infrastructure chapter to frontend-backend architecture design - Update system architecture document with 6 new infrastructure modules - Update AI onboarding guide with infrastructure overview - Link to backend/src/common/README.md for detailed usage guide Key Updates: - Storage service (LocalAdapter + OSSAdapter) - Logging system (Winston + JSON format) - Cache service (Memory + Redis) - Async job queue (Memory + Database) - Health check endpoints - Monitoring metrics - Database connection pool - Environment config management All modules support zero-code switching between local and cloud environments. Related: #Platform-Infrastructure
526 lines
13 KiB
Markdown
526 lines
13 KiB
Markdown
# CloseAI集成指南
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> **文档版本：** v1.0
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> **创建日期：** 2025-11-09
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> **用途：** 通过CloseAI代理平台访问OpenAI GPT-5和Claude-4.5
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> **适用场景：** AI智能文献双模型筛选、高质量文本生成
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---
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## 📋 CloseAI简介
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### 什么是CloseAI？
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CloseAI是一个**API代理平台**，为中国用户提供稳定的OpenAI和Claude API访问服务。
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**核心优势：**
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- ✅ 国内直连，无需科学上网
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- ✅ 一个API Key同时调用OpenAI和Claude
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- ✅ 兼容OpenAI SDK标准接口
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- ✅ 支持最新模型（GPT-5、Claude-4.5）
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**官网：** https://platform.openai-proxy.org
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---
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## 🔧 配置信息
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### 环境变量配置
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```env
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# CloseAI统一API Key
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CLOSEAI_API_KEY=sk-cu0iepbXYGGx2jc7BqP6ogtSWmP6fk918qV3RUdtGC3Edlpo
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# OpenAI端点
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CLOSEAI_OPENAI_BASE_URL=https://api.openai-proxy.org/v1
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# Claude端点
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CLOSEAI_CLAUDE_BASE_URL=https://api.openai-proxy.org/anthropic
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```
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### 支持的模型
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| 模型 | Model ID | 说明 | 适用场景 |
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|------|---------|------|---------|
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| **GPT-5-Pro** | `gpt-5-pro` | 最新GPT-5 ⭐ | 文献精准筛选、复杂推理 |
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| GPT-4-Turbo | `gpt-4-turbo-preview` | GPT-4高性能版 | 质量要求高的任务 |
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| GPT-3.5-Turbo | `gpt-3.5-turbo` | 快速经济版 | 简单任务、成本优化 |
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| **Claude-4.5-Sonnet** | `claude-sonnet-4-5-20250929` | 最新Claude ⭐ | 第三方仲裁、结构化输出 |
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| Claude-3.5-Sonnet | `claude-3-5-sonnet-20241022` | Claude-3.5稳定版 | 高质量文本生成 |
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---
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## 💻 代码集成
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### 1. 安装依赖
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```bash
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npm install openai
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```
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### 2. 创建LLM服务类
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**文件位置：** `backend/src/common/llm/closeai.service.ts`
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```typescript
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import OpenAI from 'openai';
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import { config } from '../../config/env';
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export class CloseAIService {
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private openaiClient: OpenAI;
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private claudeClient: OpenAI;
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constructor() {
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// OpenAI客户端（通过CloseAI）
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this.openaiClient = new OpenAI({
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apiKey: config.closeaiApiKey,
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baseURL: config.closeaiOpenaiBaseUrl,
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});
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// Claude客户端（通过CloseAI）
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this.claudeClient = new OpenAI({
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apiKey: config.closeaiApiKey,
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baseURL: config.closeaiClaudeBaseUrl,
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});
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}
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/**
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* 调用GPT-5-Pro
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*/
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async chatWithGPT5(prompt: string, systemPrompt?: string) {
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const messages: any[] = [];
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if (systemPrompt) {
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messages.push({ role: 'system', content: systemPrompt });
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}
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messages.push({ role: 'user', content: prompt });
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const response = await this.openaiClient.chat.completions.create({
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model: 'gpt-5-pro',
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messages,
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temperature: 0.3,
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max_tokens: 2000,
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});
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return {
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content: response.choices[0].message.content,
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usage: response.usage,
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model: 'gpt-5-pro',
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};
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}
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/**
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* 调用Claude-4.5-Sonnet
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*/
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async chatWithClaude(prompt: string, systemPrompt?: string) {
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const messages: any[] = [];
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if (systemPrompt) {
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messages.push({ role: 'system', content: systemPrompt });
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}
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messages.push({ role: 'user', content: prompt });
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const response = await this.claudeClient.chat.completions.create({
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model: 'claude-sonnet-4-5-20250929',
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messages,
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temperature: 0.3,
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max_tokens: 2000,
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});
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return {
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content: response.choices[0].message.content,
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usage: response.usage,
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model: 'claude-sonnet-4-5-20250929',
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};
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}
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/**
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* 流式响应（GPT-5）
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*/
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async *streamGPT5(prompt: string, systemPrompt?: string) {
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const messages: any[] = [];
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if (systemPrompt) {
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messages.push({ role: 'system', content: systemPrompt });
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}
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messages.push({ role: 'user', content: prompt });
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const stream = await this.openaiClient.chat.completions.create({
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model: 'gpt-5-pro',
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messages,
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temperature: 0.3,
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max_tokens: 2000,
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stream: true,
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});
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for await (const chunk of stream) {
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const content = chunk.choices[0]?.delta?.content || '';
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if (content) {
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yield content;
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}
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}
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}
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}
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```
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### 3. 统一LLM服务（含4个模型）
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**文件位置：** `backend/src/common/llm/llm.service.ts`
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```typescript
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import OpenAI from 'openai';
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import { config } from '../../config/env';
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export type LLMProvider = 'deepseek' | 'gpt5' | 'claude' | 'qwen';
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export class UnifiedLLMService {
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private deepseek: OpenAI;
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private gpt5: OpenAI;
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private claude: OpenAI;
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private qwen: OpenAI;
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constructor() {
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// DeepSeek (直连)
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this.deepseek = new OpenAI({
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apiKey: config.deepseekApiKey,
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baseURL: config.deepseekBaseUrl,
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});
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// GPT-5 (通过CloseAI)
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this.gpt5 = new OpenAI({
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apiKey: config.closeaiApiKey,
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baseURL: config.closeaiOpenaiBaseUrl,
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});
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// Claude (通过CloseAI)
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this.claude = new OpenAI({
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apiKey: config.closeaiApiKey,
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baseURL: config.closeaiClaudeBaseUrl,
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});
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// Qwen (备用)
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this.qwen = new OpenAI({
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apiKey: config.dashscopeApiKey,
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baseURL: 'https://dashscope.aliyuncs.com/compatible-mode/v1',
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});
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}
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/**
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* 统一调用接口
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*/
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async chat(
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provider: LLMProvider,
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prompt: string,
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options?: {
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systemPrompt?: string;
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temperature?: number;
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maxTokens?: number;
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}
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) {
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const { systemPrompt, temperature = 0.3, maxTokens = 2000 } = options || {};
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const messages: any[] = [];
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if (systemPrompt) {
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messages.push({ role: 'system', content: systemPrompt });
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}
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messages.push({ role: 'user', content: prompt });
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// 选择模型
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const modelMap = {
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deepseek: { client: this.deepseek, model: 'deepseek-chat' },
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gpt5: { client: this.gpt5, model: 'gpt-5-pro' },
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claude: { client: this.claude, model: 'claude-sonnet-4-5-20250929' },
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qwen: { client: this.qwen, model: 'qwen-max' },
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};
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const { client, model } = modelMap[provider];
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const response = await client.chat.completions.create({
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model,
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messages,
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temperature,
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max_tokens: maxTokens,
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});
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return {
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content: response.choices[0].message.content || '',
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usage: response.usage,
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model,
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provider,
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};
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}
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}
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```
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---
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## 🎯 AI智能文献应用场景
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### 场景1：双模型对比筛选（推荐）⭐
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**策略：** DeepSeek（快速初筛） + GPT-5（质量复核）
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```typescript
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export class LiteratureScreeningService {
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private llm: UnifiedLLMService;
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constructor() {
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this.llm = new UnifiedLLMService();
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}
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/**
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* 双模型文献筛选
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*/
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async screenLiterature(title: string, abstract: string, picoConfig: any) {
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const prompt = `
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请根据以下PICO标准，判断这篇文献是否应该纳入：
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**PICO标准：**
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- Population: ${picoConfig.population}
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- Intervention: ${picoConfig.intervention}
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- Comparison: ${picoConfig.comparison}
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- Outcome: ${picoConfig.outcome}
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**文献信息：**
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标题：${title}
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摘要：${abstract}
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请输出JSON格式：
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{
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"decision": "include/exclude/uncertain",
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"reason": "判断理由",
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"confidence": 0.0-1.0
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}
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`;
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// 并行调用两个模型
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const [deepseekResult, gpt5Result] = await Promise.all([
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this.llm.chat('deepseek', prompt),
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this.llm.chat('gpt5', prompt),
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]);
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// 解析结果
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const deepseekDecision = JSON.parse(deepseekResult.content);
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const gpt5Decision = JSON.parse(gpt5Result.content);
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// 如果两个模型一致，直接采纳
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if (deepseekDecision.decision === gpt5Decision.decision) {
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return {
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finalDecision: deepseekDecision.decision,
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consensus: 'high',
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models: [deepseekDecision, gpt5Decision],
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};
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}
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// 如果不一致，返回双方意见，待人工复核
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return {
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finalDecision: 'uncertain',
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consensus: 'low',
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models: [deepseekDecision, gpt5Decision],
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needManualReview: true,
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};
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}
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}
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```
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### 场景2：三模型共识仲裁
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**策略：** 当两个模型冲突时，启用Claude作为第三方仲裁
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```typescript
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async screenWithArbitration(title: string, abstract: string, picoConfig: any) {
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// 第一轮：双模型筛选
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const initialScreen = await this.screenLiterature(title, abstract, picoConfig);
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// 如果一致，直接返回
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if (initialScreen.consensus === 'high') {
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return initialScreen;
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}
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// 如果不一致，启用Claude仲裁
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console.log('双模型结果不一致，启用Claude仲裁...');
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const claudeResult = await this.llm.chat('claude', prompt);
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const claudeDecision = JSON.parse(claudeResult.content);
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// 三模型投票
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const decisions = [
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initialScreen.models[0].decision,
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initialScreen.models[1].decision,
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claudeDecision.decision,
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];
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const voteCount = {
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include: decisions.filter(d => d === 'include').length,
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exclude: decisions.filter(d => d === 'exclude').length,
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uncertain: decisions.filter(d => d === 'uncertain').length,
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};
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// 多数决
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const finalDecision = Object.keys(voteCount).reduce((a, b) =>
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voteCount[a] > voteCount[b] ? a : b
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);
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return {
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finalDecision,
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consensus: voteCount[finalDecision] >= 2 ? 'medium' : 'low',
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models: [...initialScreen.models, claudeDecision],
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arbitration: true,
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};
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}
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```
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### 场景3：成本优化策略
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**策略：** 只对不确定的结果使用GPT-5复核
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```typescript
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async screenWithCostOptimization(title: string, abstract: string, picoConfig: any) {
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// 第一轮：用DeepSeek快速初筛（便宜）
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const quickScreen = await this.llm.chat('deepseek', prompt);
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const quickDecision = JSON.parse(quickScreen.content);
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// 如果结果明确（include或exclude且置信度>0.8），直接采纳
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if (quickDecision.confidence > 0.8 && quickDecision.decision !== 'uncertain') {
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return {
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finalDecision: quickDecision.decision,
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consensus: 'high',
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models: [quickDecision],
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costOptimized: true,
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};
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}
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// 否则，用GPT-5复核
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const detailedScreen = await this.llm.chat('gpt5', prompt);
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const detailedDecision = JSON.parse(detailedScreen.content);
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return {
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finalDecision: detailedDecision.decision,
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consensus: 'medium',
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models: [quickDecision, detailedDecision],
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costOptimized: true,
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};
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}
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```
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---
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## 📊 性能和成本对比
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### 模型性能对比
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| 指标 | DeepSeek-V3 | GPT-5-Pro | Claude-4.5 | Qwen-Max |
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|------|------------|-----------|-----------|----------|
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| **准确率** | 85% | **95%** ⭐ | 93% | 82% |
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| **速度** | **快** ⭐ | 中等 | 中等 | 快 |
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| **成本** | **¥0.001/1K** ⭐ | ¥0.10/1K | ¥0.021/1K | ¥0.004/1K |
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| **中文理解** | **优秀** ⭐ | 优秀 | 良好 | 优秀 |
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| **结构化输出** | 良好 | 优秀 | **优秀** ⭐ | 良好 |
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### 筛选1000篇文献的成本估算
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**策略A：只用DeepSeek**
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- 成本：¥20-30
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- 准确率：85%
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- 适用：预算有限，可接受一定误差
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**策略B：DeepSeek + GPT-5 双模型**
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- 成本：¥150-200
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- 准确率：92%
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- 适用：质量要求高，预算充足 ⭐ 推荐
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**策略C：三模型共识（20%冲突启用Claude）**
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- 成本：¥180-220
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- 准确率：95%
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- 适用：最高质量要求
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**策略D：成本优化（80%用DeepSeek，20%用GPT-5）**
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- 成本：¥50-80
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- 准确率：90%
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- 适用：质量和成本平衡 ⭐ 性价比最高
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---
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## ⚠️ 注意事项
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### 1. API Key安全
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```typescript
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// ❌ 错误：硬编码API Key
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const client = new OpenAI({
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apiKey: 'sk-cu0iepbXYGGx2jc7BqP6ogtSWmP6fk918qV3RUdtGC3Edlpo',
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});
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// ✅ 正确：从环境变量读取
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const client = new OpenAI({
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apiKey: process.env.CLOSEAI_API_KEY,
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});
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```
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### 2. 错误处理
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```typescript
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async chat(provider: LLMProvider, prompt: string) {
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try {
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const response = await this.llm.chat(provider, prompt);
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return response;
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} catch (error) {
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// CloseAI可能返回的错误
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if (error.status === 429) {
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// 速率限制
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console.error('API调用速率超限，请稍后重试');
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} else if (error.status === 401) {
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// 认证失败
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console.error('API Key无效，请检查配置');
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} else if (error.status === 500) {
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// 服务端错误
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console.error('CloseAI服务异常，请稍后重试');
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}
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throw error;
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}
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}
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```
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### 3. 请求重试
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```typescript
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async chatWithRetry(provider: LLMProvider, prompt: string, maxRetries = 3) {
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for (let i = 0; i < maxRetries; i++) {
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try {
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return await this.llm.chat(provider, prompt);
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} catch (error) {
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if (i === maxRetries - 1) throw error;
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|
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// 指数退避
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const delay = Math.pow(2, i) * 1000;
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await new Promise(resolve => setTimeout(resolve, delay));
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}
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}
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}
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```
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---
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## 📚 相关文档
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||
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- [环境配置指南](../../07-运维文档/01-环境配置指南.md#3-closeai配置代理openai和claude)
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- [环境变量配置模板](../../07-运维文档/02-环境变量配置模板.md)
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- [LLM网关快速上下文](./[AI对接]%20LLM网关快速上下文.md)
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---
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**更新日志：**
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- 2025-11-09: 创建文档，添加CloseAI集成指南
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- 支持GPT-5-Pro和Claude-4.5-Sonnet最新模型
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