docs: complete documentation system (250+ files)
- System architecture and design documentation - Business module docs (ASL/AIA/PKB/RVW/DC/SSA/ST) - ASL module complete design (quality assurance, tech selection) - Platform layer and common capabilities docs - Development standards and API specifications - Deployment and operations guides - Project management and milestone tracking - Architecture implementation reports - Documentation templates and guides
This commit is contained in:
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docs/02-通用能力层/01-LLM大模型网关/[AI对接] LLM网关快速上下文.md
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docs/02-通用能力层/01-LLM大模型网关/[AI对接] LLM网关快速上下文.md
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# [AI对接] LLM网关快速上下文
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> **阅读时间：** 5分钟 | **Token消耗：** ~2000 tokens
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> **层级：** L2 | **优先级：** P0 ⭐⭐⭐⭐⭐
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> **前置阅读：** 02-通用能力层/[AI对接] 通用能力快速上下文.md
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---
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## 📋 能力定位
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**LLM大模型网关是整个平台的AI调用中枢，是商业模式的技术基础。**
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**为什么是P0优先级：**
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- 71%的业务模块依赖（5个模块：AIA、ASL、PKB、DC、RVW）
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- ASL模块开发的**前置条件**
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- 商业模式的**技术基础**（Feature Flag + 成本控制）
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**状态：** ❌ 待实现
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**建议时间：** ASL Week 1（Day 1-3）同步开发
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---
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## 🎯 核心功能
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### 1. 根据用户版本选择模型 ⭐⭐⭐⭐⭐
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**商业价值：**
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```
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专业版（¥99/月）→ DeepSeek-V3（¥1/百万tokens）
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高级版（¥299/月）→ DeepSeek + Qwen3-72B（¥5/百万tokens）
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旗舰版（¥999/月）→ 全部模型（含Claude/GPT）
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```
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**实现方式：**
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```typescript
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// 查询用户Feature Flag
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const userFlags = await featureFlagService.getUserFlags(userId);
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// 根据Feature Flag选择可用模型
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if (requestModel === 'claude-3.5' && !userFlags.includes('claude_access')) {
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throw new Error('您的套餐不支持Claude模型，请升级到旗舰版');
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}
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// 或自动降级
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if (!userFlags.includes('claude_access')) {
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model = 'deepseek-v3'; // 自动降级到DeepSeek
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}
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```
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---
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### 2. 统一调用接口 ⭐⭐⭐⭐⭐
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**问题：** 不同LLM厂商API格式不同
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- OpenAI格式
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- Anthropic格式
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- 国产大模型格式（DeepSeek、Qwen）
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**解决方案：** 统一接口 + 适配器模式
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```typescript
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// 业务模块统一调用
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const response = await llmGateway.chat({
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userId: 'user123',
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modelType: 'deepseek-v3', // 或 'qwen3', 'claude-3.5'
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messages: [
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{ role: 'user', content: '帮我分析这篇文献...' }
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],
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stream: false
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});
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// LLM网关内部：
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// 1. 检查用户权限（Feature Flag）
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// 2. 检查配额
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// 3. 选择对应的适配器
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// 4. 调用API
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// 5. 记录成本
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// 6. 返回统一格式
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```
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---
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### 3. 成本控制 ⭐⭐⭐⭐
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**核心需求：**
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- 每个用户有月度配额
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- 超出配额自动限流
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- 实时成本统计
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**实现：**
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```typescript
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// 调用前检查配额
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async function checkQuota(userId: string): Promise<boolean> {
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const usage = await getMonthlyUsage(userId);
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const quota = await getUserQuota(userId);
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if (usage.tokenCount >= quota.maxTokens) {
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throw new QuotaExceededError('您的月度配额已用完，请升级套餐');
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}
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return true;
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}
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// 调用后记录成本
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async function recordUsage(userId: string, usage: {
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modelType: string;
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tokenCount: number;
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cost: number;
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}) {
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await db.llmUsage.create({
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userId,
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modelType,
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inputTokens: usage.tokenCount,
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cost: usage.cost,
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timestamp: new Date()
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});
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}
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```
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---
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### 4. 流式/非流式统一处理 ⭐⭐⭐
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**场景：**
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- AIA智能问答 → 需要流式输出（实时显示）
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- ASL文献筛选 → 非流式（批量处理）
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**统一接口：**
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```typescript
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interface ChatOptions {
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userId: string;
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modelType: ModelType;
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messages: Message[];
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stream: boolean; // 是否流式输出
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temperature?: number;
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maxTokens?: number;
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}
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// 流式
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const stream = await llmGateway.chat({ ...options, stream: true });
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for await (const chunk of stream) {
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console.log(chunk.content);
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}
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// 非流式
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const response = await llmGateway.chat({ ...options, stream: false });
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console.log(response.content);
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```
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---
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## 🏗️ 技术架构
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### 目录结构
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```
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backend/src/modules/llm-gateway/
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├── controllers/
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│ └── llmController.ts # HTTP接口
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├── services/
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│ ├── llmGatewayService.ts # 核心服务 ⭐
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│ ├── featureFlagService.ts # Feature Flag查询
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│ ├── quotaService.ts # 配额管理
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│ └── usageService.ts # 使用统计
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├── adapters/ # 适配器模式 ⭐
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│ ├── baseAdapter.ts
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│ ├── deepseekAdapter.ts
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│ ├── qwenAdapter.ts
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│ ├── claudeAdapter.ts
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│ └── openaiAdapter.ts
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├── types/
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│ └── llm.types.ts
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└── routes/
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└── llmRoutes.ts
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```
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---
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### 核心类设计
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#### 1. LLMGatewayService（核心）
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```typescript
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class LLMGatewayService {
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private adapters: Map<ModelType, BaseLLMAdapter>;
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async chat(options: ChatOptions): Promise<ChatResponse | AsyncIterator> {
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// 1. 验证用户权限（Feature Flag）
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await this.checkAccess(options.userId, options.modelType);
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// 2. 检查配额
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await quotaService.checkQuota(options.userId);
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// 3. 选择适配器
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const adapter = this.adapters.get(options.modelType);
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// 4. 调用LLM API
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const response = await adapter.chat(options);
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// 5. 记录使用量
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await usageService.record({
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userId: options.userId,
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modelType: options.modelType,
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tokenCount: response.tokenUsage,
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cost: this.calculateCost(options.modelType, response.tokenUsage)
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});
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// 6. 返回结果
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return response;
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}
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private calculateCost(modelType: ModelType, tokens: number): number {
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const prices = {
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'deepseek-v3': 0.000001, // ¥1/百万tokens
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'qwen3-72b': 0.000005, // ¥5/百万tokens
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'claude-3.5': 0.00003 // $15/百万tokens ≈ ¥0.0003/千tokens
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};
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return tokens * prices[modelType];
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}
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}
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```
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#### 2. BaseLLMAdapter（适配器基类）
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```typescript
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abstract class BaseLLMAdapter {
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abstract chat(options: ChatOptions): Promise<ChatResponse>;
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abstract chatStream(options: ChatOptions): AsyncIterator<ChatChunk>;
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protected abstract buildRequest(options: ChatOptions): any;
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protected abstract parseResponse(response: any): ChatResponse;
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}
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```
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#### 3. DeepSeekAdapter（实现示例）
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```typescript
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class DeepSeekAdapter extends BaseLLMAdapter {
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private apiKey: string;
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private baseUrl = 'https://api.deepseek.com/v1';
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async chat(options: ChatOptions): Promise<ChatResponse> {
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const request = this.buildRequest(options);
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const response = await fetch(`${this.baseUrl}/chat/completions`, {
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method: 'POST',
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headers: {
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'Authorization': `Bearer ${this.apiKey}`,
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'Content-Type': 'application/json'
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},
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body: JSON.stringify(request)
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});
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const data = await response.json();
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return this.parseResponse(data);
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}
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protected buildRequest(options: ChatOptions) {
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return {
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model: 'deepseek-chat',
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messages: options.messages,
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temperature: options.temperature || 0.7,
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max_tokens: options.maxTokens || 4096,
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stream: options.stream || false
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};
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}
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protected parseResponse(response: any): ChatResponse {
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return {
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content: response.choices[0].message.content,
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tokenUsage: response.usage.total_tokens,
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finishReason: response.choices[0].finish_reason
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};
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}
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}
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```
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---
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## 📊 数据库设计
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### platform_schema.llm_usage
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```sql
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CREATE TABLE platform_schema.llm_usage (
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id SERIAL PRIMARY KEY,
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user_id INTEGER REFERENCES platform_schema.users(id),
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model_type VARCHAR(50) NOT NULL, -- 'deepseek-v3', 'qwen3', 'claude-3.5'
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input_tokens INTEGER NOT NULL,
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output_tokens INTEGER NOT NULL,
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total_tokens INTEGER NOT NULL,
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cost DECIMAL(10, 6) NOT NULL, -- 实际成本（人民币）
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request_id VARCHAR(100), -- LLM API返回的request_id
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module VARCHAR(50), -- 哪个模块调用的：'AIA', 'ASL', 'PKB'等
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created_at TIMESTAMP DEFAULT NOW(),
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INDEX idx_user_created (user_id, created_at),
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INDEX idx_module (module)
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);
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```
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### platform_schema.llm_quotas
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```sql
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CREATE TABLE platform_schema.llm_quotas (
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id SERIAL PRIMARY KEY,
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user_id INTEGER REFERENCES platform_schema.users(id) UNIQUE,
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monthly_token_limit INTEGER NOT NULL, -- 月度token配额
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monthly_cost_limit DECIMAL(10, 2), -- 月度成本上限（可选）
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reset_day INTEGER DEFAULT 1, -- 每月重置日期（1-28）
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created_at TIMESTAMP DEFAULT NOW(),
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updated_at TIMESTAMP DEFAULT NOW()
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);
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```
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---
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## 📋 API端点
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### 1. 聊天接口（非流式）
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```
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POST /api/v1/llm/chat
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Request:
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{
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"modelType": "deepseek-v3",
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"messages": [
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{ "role": "user", "content": "分析这篇文献..." }
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],
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"temperature": 0.7,
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"maxTokens": 4096
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}
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Response:
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{
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"content": "根据文献内容分析...",
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"tokenUsage": {
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"input": 150,
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"output": 500,
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"total": 650
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},
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"cost": 0.00065,
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"modelType": "deepseek-v3"
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}
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```
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### 2. 聊天接口（流式）
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```
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POST /api/v1/llm/chat/stream
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Request: 同上 + "stream": true
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Response: Server-Sent Events (SSE)
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data: {"chunk": "根据", "tokenUsage": 1}
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data: {"chunk": "文献", "tokenUsage": 1}
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...
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data: {"done": true, "totalTokens": 650, "cost": 0.00065}
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```
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### 3. 查询配额
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```
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GET /api/v1/llm/quota
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Response:
|
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{
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"monthlyLimit": 1000000,
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"used": 245000,
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"remaining": 755000,
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"resetDate": "2025-12-01"
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}
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```
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### 4. 使用统计
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```
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GET /api/v1/llm/usage?startDate=2025-11-01&endDate=2025-11-30
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Response:
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{
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"totalTokens": 245000,
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"totalCost": 1.23,
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"byModel": {
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"deepseek-v3": { "tokens": 200000, "cost": 0.20 },
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"qwen3-72b": { "tokens": 45000, "cost": 0.23 }
|
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},
|
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"byModule": {
|
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"AIA": 100000,
|
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"ASL": 120000,
|
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"PKB": 25000
|
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}
|
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}
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```
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|
||||
---
|
||||
|
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## ⚠️ 关键技术难点
|
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|
||||
### 1. 流式输出的实现
|
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**技术方案：** Server-Sent Events (SSE)
|
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|
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```typescript
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// 后端（Fastify）
|
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app.post('/api/v1/llm/chat/stream', async (req, reply) => {
|
||||
reply.raw.setHeader('Content-Type', 'text/event-stream');
|
||||
reply.raw.setHeader('Cache-Control', 'no-cache');
|
||||
reply.raw.setHeader('Connection', 'keep-alive');
|
||||
|
||||
const stream = await llmGateway.chatStream(req.body);
|
||||
|
||||
for await (const chunk of stream) {
|
||||
reply.raw.write(`data: ${JSON.stringify(chunk)}\n\n`);
|
||||
}
|
||||
|
||||
reply.raw.end();
|
||||
});
|
||||
|
||||
// 前端（React）
|
||||
const eventSource = new EventSource('/api/v1/llm/chat/stream');
|
||||
eventSource.onmessage = (event) => {
|
||||
const data = JSON.parse(event.data);
|
||||
setMessages(prev => [...prev, data.chunk]);
|
||||
};
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 2. 错误处理和重试
|
||||
```typescript
|
||||
async function chatWithRetry(options: ChatOptions, maxRetries = 3) {
|
||||
for (let i = 0; i < maxRetries; i++) {
|
||||
try {
|
||||
return await llmGateway.chat(options);
|
||||
} catch (error) {
|
||||
if (error.code === 'RATE_LIMIT' && i < maxRetries - 1) {
|
||||
await sleep(2000 * (i + 1)); // 指数退避
|
||||
continue;
|
||||
}
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 3. Token计数（精确计费）
|
||||
**问题：** 不同模型的tokenizer不同
|
||||
|
||||
**解决方案：**
|
||||
- 使用各厂商提供的API返回值（最准确）
|
||||
- 备用方案：tiktoken库（OpenAI tokenizer）
|
||||
|
||||
```typescript
|
||||
import { encoding_for_model } from 'tiktoken';
|
||||
|
||||
function estimateTokens(text: string, model: string): number {
|
||||
const encoder = encoding_for_model(model);
|
||||
const tokens = encoder.encode(text);
|
||||
encoder.free();
|
||||
return tokens.length;
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📅 开发计划（3天）
|
||||
|
||||
### Day 1：基础架构（6-8小时）
|
||||
- [ ] 创建目录结构
|
||||
- [ ] 实现BaseLLMAdapter抽象类
|
||||
- [ ] 实现DeepSeekAdapter
|
||||
- [ ] 数据库表创建（llm_usage, llm_quotas）
|
||||
- [ ] 基础API端点（非流式）
|
||||
|
||||
### Day 2：核心功能（6-8小时）
|
||||
- [ ] Feature Flag集成
|
||||
- [ ] 配额检查和记录
|
||||
- [ ] 实现QwenAdapter
|
||||
- [ ] 错误处理和重试机制
|
||||
- [ ] 单元测试
|
||||
|
||||
### Day 3：流式输出 + 优化（6-8小时）
|
||||
- [ ] 实现流式输出（SSE）
|
||||
- [ ] 前端SSE接收处理
|
||||
- [ ] 成本统计API
|
||||
- [ ] 配额查询API
|
||||
- [ ] 集成测试
|
||||
- [ ] 文档完善
|
||||
|
||||
---
|
||||
|
||||
## ✅ 开发检查清单
|
||||
|
||||
**开始前确认：**
|
||||
- [ ] Feature Flag表已创建（platform_schema.feature_flags）
|
||||
- [ ] 用户表已有version字段（professional/premium/enterprise）
|
||||
- [ ] 各LLM厂商API Key已配置
|
||||
- [ ] Prisma Schema已更新
|
||||
|
||||
**开发中：**
|
||||
- [ ] 每个适配器都有完整的错误处理
|
||||
- [ ] 所有LLM调用都记录到llm_usage表
|
||||
- [ ] 配额检查在每次调用前执行
|
||||
- [ ] 流式和非流式都已测试
|
||||
|
||||
**完成后：**
|
||||
- [ ] ASL模块可以成功调用LLM网关
|
||||
- [ ] ADMIN可以查看用户LLM使用统计
|
||||
- [ ] 配额超限会正确拒绝请求
|
||||
|
||||
---
|
||||
|
||||
## 🔗 相关文档
|
||||
|
||||
**依赖：**
|
||||
- [用户与权限中心(UAM)](../../01-平台基础层/01-用户与权限中心(UAM)/README.md) - Feature Flag
|
||||
- [运营管理端](../../03-业务模块/ADMIN-运营管理端/README.md) - LLM模型管理
|
||||
|
||||
**被依赖：**
|
||||
- [ASL-AI智能文献](../../03-业务模块/ASL-AI智能文献/README.md) ⭐ P0
|
||||
- [AIA-AI智能问答](../../03-业务模块/AIA-AI智能问答/README.md)
|
||||
- [PKB-个人知识库](../../03-业务模块/PKB-个人知识库/README.md)
|
||||
|
||||
---
|
||||
|
||||
**最后更新：** 2025-11-06
|
||||
**维护人：** 技术架构师
|
||||
**优先级：** P0 ⭐⭐⭐⭐⭐
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user