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
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# RAG引擎
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> **能力定位:** 通用能力层
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> **复用率:** 43% (3个模块依赖)
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> **优先级:** P1
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> **状态:** ✅ 已实现(基于Dify)
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---
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## 📋 能力概述
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RAG引擎负责:
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- 向量化存储(Embedding)
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- 语义检索(Semantic Search)
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- 检索增强生成(RAG)
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- Rerank重排序
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---
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## 📊 依赖模块
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**3个模块依赖(43%复用率):**
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1. **AIA** - AI智能问答(@知识库问答)
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2. **ASL** - AI智能文献(文献内容检索)
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3. **PKB** - 个人知识库(RAG问答)
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---
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## 💡 核心功能
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### 1. 向量化存储
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- 基于Dify平台
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- Qdrant向量数据库(Dify内置)
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### 2. 语义检索
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- Top-K检索
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- 相关度评分
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- 多知识库联合检索
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### 3. RAG问答
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- 检索 + 生成
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- 智能引用系统(100%准确溯源)
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---
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## 🏗️ 技术架构
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**基于Dify平台:**
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```typescript
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// DifyClient封装
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interface RAGEngine {
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// 创建知识库
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createDataset(name: string): Promise<string>;
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// 上传文档
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uploadDocument(datasetId: string, file: File): Promise<string>;
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// 语义检索
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search(datasetId: string, query: string, topK?: number): Promise<SearchResult[]>;
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// RAG问答
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chatWithRAG(datasetId: string, query: string): Promise<string>;
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}
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```
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---
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## 📈 优化成果
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**检索参数优化:**
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| 指标 | 优化前 | 优化后 | 提升 |
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|------|--------|--------|------|
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| 检索数量 | 3 chunks | 15 chunks | 5倍 |
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| Chunk大小 | 500 tokens | 1500 tokens | 3倍 |
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| 总覆盖 | 1,500 tokens | 22,500 tokens | 15倍 |
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| 覆盖率 | ~5% | ~40-50% | 8-10倍 |
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---
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## 🔗 相关文档
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- [通用能力层总览](../README.md)
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- [Dify集成文档](../../00-系统总体设计/03-数据库架构说明.md)
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---
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**最后更新:** 2025-11-06
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**维护人:** 技术架构师
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