Files
AIclinicalresearch/docs/02-通用能力层
HaHafeng 4c6eaaecbf feat(dc): Implement Postgres-Only async architecture and performance optimization
Summary:
- Implement async file upload processing (Platform-Only pattern)
- Add parseExcelWorker with pg-boss queue
- Implement React Query polling mechanism
- Add clean data caching (avoid duplicate parsing)
- Fix pivot single-value column tuple issue
- Optimize performance by 99 percent

Technical Details:

1. Async Architecture (Postgres-Only):
   - SessionService.createSession: Fast upload + push to queue (3s)
   - parseExcelWorker: Background parsing + save clean data (53s)
   - SessionController.getSessionStatus: Status query API for polling
   - React Query Hook: useSessionStatus (auto-serial polling)
   - Frontend progress bar with real-time feedback

2. Performance Optimization:
   - Clean data caching: Worker saves processed data to OSS
   - getPreviewData: Read from clean data cache (0.5s vs 43s, -99 percent)
   - getFullData: Read from clean data cache (0.5s vs 43s, -99 percent)
   - Intelligent cleaning: Boundary detection + ghost column/row removal
   - Safety valve: Max 3000 columns, 5M cells

3. Bug Fixes:
   - Fix pivot column name tuple issue for single value column
   - Fix queue name format (colon to underscore: asl:screening -> asl_screening)
   - Fix polling storm (15+ concurrent requests -> 1 serial request)
   - Fix QUEUE_TYPE environment variable (memory -> pgboss)
   - Fix logger import in PgBossQueue
   - Fix formatSession to return cleanDataKey
   - Fix saveProcessedData to update clean data synchronously

4. Database Changes:
   - ALTER TABLE dc_tool_c_sessions ADD COLUMN clean_data_key VARCHAR(1000)
   - ALTER TABLE dc_tool_c_sessions ALTER COLUMN total_rows DROP NOT NULL
   - ALTER TABLE dc_tool_c_sessions ALTER COLUMN total_cols DROP NOT NULL
   - ALTER TABLE dc_tool_c_sessions ALTER COLUMN columns DROP NOT NULL

5. Documentation:
   - Create Postgres-Only async task processing guide (588 lines)
   - Update Tool C status document (Day 10 summary)
   - Update DC module status document
   - Update system overview document
   - Update cloud-native development guide

Performance Improvements:
- Upload + preview: 96s -> 53.5s (-44 percent)
- Filter operation: 44s -> 2.5s (-94 percent)
- Pivot operation: 45s -> 2.5s (-94 percent)
- Concurrent requests: 15+ -> 1 (-93 percent)
- Complete workflow (upload + 7 ops): 404s -> 70.5s (-83 percent)

Files Changed:
- Backend: 15 files (Worker, Service, Controller, Schema, Config)
- Frontend: 4 files (Hook, Component, API)
- Docs: 4 files (Guide, Status, Overview, Spec)
- Database: 4 column modifications
- Total: ~1388 lines of new/modified code

Status: Fully tested and verified, production ready
2025-12-22 21:30:31 +08:00
..

通用能力层

层级定义: 跨业务模块共享的核心技术能力
核心原则: 可复用、高内聚、独立部署


📋 能力清单

能力 说明 复用率 优先级 状态
01-LLM大模型网关 统一管理LLM调用、成本控制、模型切换 71% (5/7) P0 待实现
02-文档处理引擎 PDF/Docx/Txt提取、OCR、表格提取 86% (6/7) P0 已实现
03-RAG引擎 向量检索、语义搜索、RAG问答 43% (3/7) P1 已实现
04-数据ETL引擎 Excel JOIN、数据清洗、数据转换 29% (2/7) P2 待实现
05-医学NLP引擎 医学实体识别、术语标准化 14% (1/7) P2 待实现

🎯 设计原则

1. 可复用性

  • 多个业务模块共享
  • 避免重复开发

2. 独立部署

  • 可以独立为微服务
  • 支持独立扩展

3. 高内聚

  • 每个能力职责单一
  • 接口清晰

4. 领域知识

  • 包含业务领域知识
  • 不是纯技术组件

📊 复用率分析

LLM网关 - 71%复用率(最高优先级)

  • AIAAI智能问答
  • ASLAI智能文献
  • PKB个人知识库
  • DC数据清洗
  • RVW稿件审查

文档处理引擎 - 86%复用率(已实现)

  • ASL、PKB、DC、SSA、ST、RVW

RAG引擎 - 43%复用率(已实现)

  • AIA、ASL、PKB

📚 快速导航

快速上下文

  • [AI对接] 通用能力快速上下文.md - 2-3分钟了解通用能力层

核心能力

  1. LLM大模型网关 - P0优先级
  2. 文档处理引擎 - 已实现
  3. RAG引擎 - 已实现
  4. 数据ETL引擎
  5. 医学NLP引擎

🔗 相关文档


最后更新: 2025-11-06
维护人: 技术架构师