b64896a307
feat(deploy): Complete PostgreSQL migration and Docker image build
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Summary:
- PostgreSQL database migration to RDS completed (90MB SQL, 11 schemas)
- Frontend Nginx Docker image built and pushed to ACR (v1.0, ~50MB)
- Python microservice Docker image built and pushed to ACR (v1.0, 1.12GB)
- Created 3 deployment documentation files
Docker Configuration Files:
- frontend-v2/Dockerfile: Multi-stage build with nginx:alpine
- frontend-v2/.dockerignore: Optimize build context
- frontend-v2/nginx.conf: SPA routing and API proxy
- frontend-v2/docker-entrypoint.sh: Dynamic env injection
- extraction_service/Dockerfile: Multi-stage build with Aliyun Debian mirror
- extraction_service/.dockerignore: Optimize build context
- extraction_service/requirements-prod.txt: Production dependencies (removed Nougat)
Deployment Documentation:
- docs/05-部署文档/00-部署进度总览.md: One-stop deployment status overview
- docs/05-部署文档/07-前端Nginx-SAE部署操作手册.md: Frontend deployment guide
- docs/05-部署文档/08-PostgreSQL数据库部署操作手册.md: Database deployment guide
- docs/00-系统总体设计/00-系统当前状态与开发指南.md: Updated with deployment status
Database Migration:
- RDS instance: pgm-2zex1m2y3r23hdn5 (2C4G, PostgreSQL 15.0)
- Database: ai_clinical_research
- Schemas: 11 business schemas migrated successfully
- Data: 3 users, 2 projects, 1204 literatures verified
- Backup: rds_init_20251224_154529.sql (90MB)
Docker Images:
- Frontend: crpi-cd5ij4pjt65mweeo.cn-beijing.personal.cr.aliyuncs.com/ai-clinical/ai-clinical_frontend-nginx:v1.0
- Python: crpi-cd5ij4pjt65mweeo.cn-beijing.personal.cr.aliyuncs.com/ai-clinical/python-extraction:v1.0
Key Achievements:
- Resolved Docker Hub network issues (using generic tags)
- Fixed 30 TypeScript compilation errors
- Removed Nougat OCR to reduce image size by 1.5GB
- Used Aliyun Debian mirror to resolve apt-get network issues
- Implemented multi-stage builds for optimization
Next Steps:
- Deploy Python microservice to SAE
- Build Node.js backend Docker image
- Deploy Node.js backend to SAE
- Deploy frontend Nginx to SAE
- End-to-end verification testing
Status: Docker images ready, SAE deployment pending
2025-12-24 18:21:55 +08:00
88cc049fb3
feat(asl): Complete Day 5 - Fulltext Screening Backend API Development
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- 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
2025-11-23 10:52:07 +08:00
beb7f7f559
feat(asl): Implement full-text screening core LLM service and validation system (Day 1-3)
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Core Components:
- PDFStorageService with Dify/OSS adapters
- LLM12FieldsService with Nougat-first + dual-model + 3-layer JSON parsing
- PromptBuilder for dynamic prompt assembly
- MedicalLogicValidator with 5 rules + fault tolerance
- EvidenceChainValidator for citation integrity
- ConflictDetectionService for dual-model comparison
Prompt Engineering:
- System Prompt (6601 chars, Section-Aware strategy)
- User Prompt template (PICOS context injection)
- JSON Schema (12 fields constraints)
- Cochrane standards (not loaded in MVP)
Key Innovations:
- 3-layer JSON parsing (JSON.parse + json-repair + code block extraction)
- Promise.allSettled for dual-model fault tolerance
- safeGetFieldValue for robust field extraction
- Mixed CN/EN token calculation
Integration Tests:
- integration-test.ts (full test)
- quick-test.ts (quick test)
- cached-result-test.ts (fault tolerance test)
Documentation Updates:
- Development record (Day 2-3 summary)
- Quality assurance strategy (full-text screening)
- Development plan (progress update)
- Module status (v1.1 update)
- Technical debt (10 new items)
Test Results:
- JSON parsing success rate: 100%
- Medical logic validation: 5/5 passed
- Dual-model parallel processing: OK
- Cost per PDF: CNY 0.10
Files: 238 changed, 14383 insertions(+), 32 deletions(-)
Docs: docs/03-涓氬姟妯″潡/ASL-AI鏅鸿兘鏂囩尞/05-寮€鍙戣褰?2025-11-22_Day2-Day3_LLM鏈嶅姟涓庨獙璇佺郴缁熷紑鍙?md
2025-11-22 22:21:12 +08:00
8eef9e0544
feat(asl): Complete Week 4 - Results display and Excel export with hybrid solution
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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
2025-11-21 20:12:38 +08:00
