HaHafeng
8eef9e0544
Features: - Backend statistics API (cloud-native Prisma aggregation) - Results page with hybrid solution (AI consensus + human final decision) - Excel export (frontend generation, zero disk write, cloud-native) - PRISMA-style exclusion reason analysis with bar chart - Batch selection and export (3 export methods) - Fixed logic contradiction (inclusion does not show exclusion reason) - Optimized table width (870px, no horizontal scroll) Components: - Backend: screeningController.ts - add getProjectStatistics API - Frontend: ScreeningResults.tsx - complete results page (hybrid solution) - Frontend: excelExport.ts - Excel export utility (40 columns full info) - Frontend: ScreeningWorkbench.tsx - add navigation button - Utils: get-test-projects.mjs - quick test tool Architecture: - Cloud-native: backend aggregation reduces network transfer - Cloud-native: frontend Excel generation (zero file persistence) - Reuse platform: global prisma instance, logger - Performance: statistics API < 500ms, Excel export < 3s (1000 records) Documentation: - Update module status guide (add Week 4 features) - Update task breakdown (mark Week 4 completed) - Update API design spec (add statistics API) - Update database design (add field usage notes) - Create Week 4 development plan - Create Week 4 completion report - Create technical debt list Test: - End-to-end flow test passed - All features verified - Performance test passed - Cloud-native compliance verified Ref: Week 4 Development Plan Scope: ASL Module MVP - Title Abstract Screening Results Cloud-Native: Backend aggregation + Frontend Excel generation
1.8 KiB
1.8 KiB
RAG引擎
能力定位： 通用能力层
复用率： 43% (3个模块依赖)
优先级： P1
状态： ✅ 已实现（基于Dify）
📋 能力概述
RAG引擎负责：
- 向量化存储（Embedding）
- 语义检索（Semantic Search）
- 检索增强生成（RAG）
- Rerank重排序
📊 依赖模块
3个模块依赖（43%复用率）：
- AIA - AI智能问答（@知识库问答）
- ASL - AI智能文献（文献内容检索）
- PKB - 个人知识库（RAG问答）
💡 核心功能
1. 向量化存储
- 基于Dify平台
- Qdrant向量数据库（Dify内置）
2. 语义检索
- Top-K检索
- 相关度评分
- 多知识库联合检索
3. RAG问答
- 检索 + 生成
- 智能引用系统（100%准确溯源）
🏗️ 技术架构
基于Dify平台：
// DifyClient封装
interface RAGEngine {
// 创建知识库
createDataset(name: string): Promise<string>;
// 上传文档
uploadDocument(datasetId: string, file: File): Promise<string>;
// 语义检索
search(datasetId: string, query: string, topK?: number): Promise<SearchResult[]>;
// RAG问答
chatWithRAG(datasetId: string, query: string): Promise<string>;
}
📈 优化成果
检索参数优化：
| 指标 | 优化前 | 优化后 | 提升 |
|---|---|---|---|
| 检索数量 | 3 chunks | 15 chunks | 5倍 |
| Chunk大小 | 500 tokens | 1500 tokens | 3倍 |
| 总覆盖 | 1,500 tokens | 22,500 tokens | 15倍 |
| 覆盖率 | ~5% | ~40-50% | 8-10倍 |
🔗 相关文档
最后更新： 2025-11-06
维护人： 技术架构师