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AIclinicalresearch/docs/08-项目管理/03-每周计划
HaHafeng 40c2f8e148 feat(rag): Complete RAG engine implementation with pgvector
Major Features:
- Created ekb_schema (13th schema) with 3 tables: KB/Document/Chunk
- Implemented EmbeddingService (text-embedding-v4, 1024-dim vectors)
- Implemented ChunkService (smart Markdown chunking)
- Implemented VectorSearchService (multi-query + hybrid search)
- Implemented RerankService (qwen3-rerank)
- Integrated DeepSeek V3 QueryRewriter for cross-language search
- Python service: Added pymupdf4llm for PDF-to-Markdown conversion
- PKB: Dual-mode adapter (pgvector/dify/hybrid)

Architecture:
- Brain-Hand Model: Business layer (DeepSeek) + Engine layer (pgvector)
- Cross-language support: Chinese query matches English documents
- Small Embedding (1024) + Strong Reranker strategy

Performance:
- End-to-end latency: 2.5s
- Cost per query: 0.0025 RMB
- Accuracy improvement: +20.5% (cross-language)

Tests:
- test-embedding-service.ts: Vector embedding verified
- test-rag-e2e.ts: Full pipeline tested
- test-rerank.ts: Rerank quality validated
- test-query-rewrite.ts: Cross-language search verified
- test-pdf-ingest.ts: Real PDF document tested (Dongen 2003.pdf)

Documentation:
- Added 05-RAG-Engine-User-Guide.md
- Added 02-Document-Processing-User-Guide.md
- Updated system status documentation

Status: Production ready
2026-01-21 20:24:29 +08:00
..

每周计划与进度跟踪

用途: 记录每周的工作计划、进度和总结
更新频率: 每周五下午更新


📋 使用说明

每周五下午做什么?

  1. 回顾本周

    • 完成了哪些任务?
    • 遇到了什么问题?
    • 学到了什么?
  2. 记录进度

    • 更新任务完成状态
    • 记录关键决策
    • 记录技术难点
  3. 规划下周

    • 下周要做什么?
    • 需要哪些资源?
    • 有什么风险?

📊 进度报告列表

周次 时间范围 核心任务 完成度 报告链接
W45 2025-11-04 至 11-10 文档重构 100% 查看报告
W46 2025-11-11 至 11-17 代码重构+LLM网关 0% 📋 待创建
W47 2025-11-18 至 11-24 ASL标题摘要初筛 0% 📋 待创建

维护者: 项目管理组
最后更新: 2025-11-07