HaHafeng
88cc049fb3
- Implement 5 core API endpoints (create task, get progress, get results, update decision, export Excel) - Add FulltextScreeningController with Zod validation (652 lines) - Implement ExcelExporter service with 4-sheet report generation (352 lines) - Register routes under /api/v1/asl/fulltext-screening - Create 31 REST Client test cases - Add automated integration test script - Fix PDF extraction fallback mechanism in LLM12FieldsService - Update API design documentation to v3.0 - Update development plan to v1.2 - Create Day 5 development record - Clean up temporary test files
115 lines
1.8 KiB
Markdown
115 lines
1.8 KiB
Markdown
# 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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