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e785969e54
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feat(rvw): Implement RVW V2.0 Data Forensics Module - Day 6 StatValidator
Summary:
- Implement L2 Statistical Validator (CI-P consistency, T-test reverse)
- Implement L2.5 Consistency Forensics (SE Triangle, SD>Mean check)
- Add error/warning severity classification with tolerance thresholds
- Support 5+ CI formats parsing (parentheses, brackets, 95% CI prefix)
- Complete Python forensics service (types, config, validator, extractor)
V2.0 Development Progress (Week 2 Day 6):
- Day 1-5: Python service setup, Word table extraction, L1 arithmetic validator
- Day 6: L2 StatValidator + L2.5 consistency forensics (promoted from V2.1)
Test Results:
- Unit tests: 4/4 passed (CI-P, SE Triangle, SD>Mean, T-test)
- Real document tests: 5/5 successful, 2 reasonable WARNINGs
Status: Day 6 completed, ready for Day 7 (Skills Framework)
Co-authored-by: Cursor <cursoragent@cursor.com>
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2026-02-17 22:15:27 +08:00 |
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303dd78c54
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feat(aia): Protocol Agent MVP complete with one-click generation and Word export
- Add one-click research protocol generation with streaming output
- Implement Word document export via Pandoc integration
- Add dynamic dual-panel layout with resizable split pane
- Implement collapsible content for StatePanel stages
- Add conversation history management with title auto-update
- Fix scroll behavior, markdown rendering, and UI layout issues
- Simplify conversation creation logic for reliability
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2026-01-25 19:16:36 +08:00 |
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40c2f8e148
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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
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2026-01-21 20:24:29 +08:00 |
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