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21 Commits

Author SHA1 Message Date
HaHafeng
440f75255e feat(rvw): Complete Phase 4-5 - Bug fixes and Word export 
Summary:
- Fix methodology score display issue in task list (show score instead of 'warn')
- Add methodology_score field to database schema
- Fix report display when only methodology agent is selected
- Implement Word document export using docx library
- Update documentation to v3.0/v3.1

Backend changes:
- Add methodologyScore to Prisma schema and TaskSummary type
- Update reviewWorker to save methodologyScore
- Update getTaskList to return methodologyScore

Frontend changes:
- Install docx and file-saver libraries
- Implement handleExportReport with Word generation
- Fix activeTab auto-selection based on available data
- Add proper imports for docx components

Documentation:
- Update RVW module status to 90% (Phase 1-5 complete)
- Update system status document to v3.0

Tested: All review workflows verified, Word export functional
 2026-01-10 22:52:15 +08:00
HaHafeng
179afa2c6b feat(rvw): Complete RVW module development Phase 1-3 
Summary:
- Migrate backend to modules/rvw with v2 API routes (/api/v2/rvw)
- Add new database fields: selectedAgents, editorialScore, methodologyStatus, picoExtract, isArchived
- Create frontend module in frontend-v2/src/modules/rvw
- Implement Dashboard with task list, filtering, batch operations
- Implement ReportDetail with dual tabs (editorial/methodology)
- Implement AgentModal for intelligent agent selection
- Register RVW module in moduleRegistry.ts
- Add navigation entry in TopNavigation
- Update documentation for RVW module status (v3.0)
- Update system status document (v2.9)

Features:
- User can select agents: editorial, methodology, or both
- Support batch task execution
- Task status filtering
- Replace console.log with logger service
- Maintain v1 API backward compatibility

Tested: Frontend and backend verified locally
Status: 85% complete (Phase 1-3 done)
 2026-01-07 22:39:08 +08:00
HaHafeng
06028c6952 feat(pkb): implement complete batch processing workflow and frontend optimization 
- Frontend V3 architecture migration to modules/pkb
- Implement three work modes: full-text reading, deep reading, batch processing
- Complete batch processing: template selection, progress display, result export (CSV)
- Integrate Ant Design X Chat component with streaming support
- Add document upload modal with drag-and-drop support
- Optimize UI: multi-line table display, citation formatting, auto-scroll
- Fix 10+ technical issues: API mapping, state sync, form clearing
- Update documentation: development records and module status

Performance: 3 docs batch processing ~17-28s
Status: PKB module now production-ready (90% complete)
 2026-01-07 18:23:43 +08:00
HaHafeng
e59676342a docs(pkb): Add development records and update system status 
Summary:
- Add PKB module development record for 2026-01-07
- Create PKB module status document (00-模块当前状态与开发指南.md)
- Update system status document to v2.7

Documents added:
- docs/03-业务模块/PKB-个人知识库/06-开发记录/2026-01-07_PKB模块前端V3设计实现.md
- docs/03-业务模块/PKB-个人知识库/00-模块当前状态与开发指南.md

Documents updated:
- docs/00-系统总体设计/00-系统当前状态与开发指南.md

PKB module progress: 75% complete
- Frontend Dashboard: 90%
- Frontend Workspace: 85%
- 3 work modes implemented
- Batch processing API pending debug
 2026-01-07 10:35:03 +08:00
HaHafeng
5a17d096a7 feat(pkb): Complete PKB module frontend migration with V3 design 
Summary:
- Implement PKB Dashboard and Workspace pages based on V3 prototype
- Add single-layer header with integrated Tab navigation
- Implement 3 work modes: Full Text, Deep Read, Batch Processing
- Integrate Ant Design X Chat component for AI conversations
- Create BatchModeComplete with template selection and document processing
- Add compact work mode selector with dropdown design

Backend:
- Migrate PKB controllers and services to /modules/pkb structure
- Register v2 API routes at /api/v2/pkb/knowledge
- Maintain dual API routes for backward compatibility

Technical details:
- Use Zustand for state management
- Handle SSE streaming responses for AI chat
- Support document selection for Deep Read mode
- Implement batch processing with progress tracking

Known issues:
- Batch processing API integration pending
- Knowledge assets page navigation needs optimization

Status: Frontend functional, pending refinement
 2026-01-06 22:15:42 +08:00
HaHafeng
b31255031e feat(iit-manager): Add WeChat Official Account integration for patient notifications 
Features:
- PatientWechatCallbackController for URL verification and message handling
- PatientWechatService for template and customer messages
- Support for secure mode (message encryption/decryption)
- Simplified route /wechat/patient/callback for WeChat config
- Event handlers for subscribe/unsubscribe/text messages
- Template message for visit reminders

Technical details:
- Reuse @wecom/crypto for encryption (compatible with Official Account)
- Relaxed Fastify schema validation to prevent early request blocking
- Access token caching (7000s with 5min pre-refresh)
- Comprehensive logging for debugging

Testing: Local URL verification passed, ready for SAE deployment

Status: Code complete, waiting for WeChat platform configuration
 2026-01-04 22:53:42 +08:00
HaHafeng
dfc472810b feat(iit-manager): Integrate Dify knowledge base for hybrid retrieval 
Completed features:
- Created Dify dataset (Dify_test0102) with 2 processed documents
- Linked test0102 project with Dify dataset ID
- Extended intent detection to recognize query_protocol intent
- Implemented queryDifyKnowledge method (semantic search Top 5)
- Integrated hybrid retrieval (REDCap data + Dify documents)
- Fixed AI hallucination bugs (intent detection + API field path)
- Developed debugging scripts
- Completed end-to-end testing (5 scenarios passed)
- Generated comprehensive documentation (600+ lines)
- Updated development plans and module status

Technical highlights:
- Single project single knowledge base architecture
- Smart routing based on user intent
- Prevent AI hallucination by injecting real data/documents
- Session memory for multi-turn conversations
- Reused LLMFactory for DeepSeek-V3 integration

Bug fixes:
- Fixed intent detection missing keywords
- Fixed Dify API response field path error

Testing: All scenarios verified in WeChat production environment

Status: Fully tested and deployed
 2026-01-04 15:44:11 +08:00
HaHafeng
b47079b387 feat(iit): Phase 1.5 AI对话集成REDCap真实数据完成 
- feat: ChatService集成DeepSeek-V3实现AI对话（390行）
- feat: SessionMemory实现上下文记忆（最近3轮对话，170行）
- feat: 意图识别支持REDCap数据查询（关键词匹配）
- feat: REDCap数据注入LLM（queryRedcapRecord, countRedcapRecords, getProjectInfo）
- feat: 解决LLM幻觉问题（基于真实数据回答，明确system prompt）
- feat: 即时反馈（正在查询...提示）
- test: REDCap查询测试通过（test0102项目，10条记录，ID 7患者详情）
- docs: 创建Phase1.5开发完成记录（313行）
- docs: 更新Phase1.5开发计划（标记完成）
- docs: 更新MVP开发任务清单（Phase 1.5完成）
- docs: 更新模块当前状态（60%完成度）
- docs: 更新系统总体设计文档（v2.6）
- chore: 删除测试脚本（test-redcap-query-for-ai.ts, check-env-config.ts）
- chore: 移除REDCap测试环境变量（REDCAP_TEST_*）

技术亮点：
- AI基于REDCap真实数据对话，不编造信息
- 从数据库读取项目配置，不使用环境变量
- 企业微信端测试通过，用户体验良好

测试通过：
-  查询项目记录总数（10条）
-  查询特定患者详情（ID 7）
-  项目信息查询
-  上下文记忆（3轮对话）
-  即时反馈提示

影响范围：IIT Manager Agent模块
 2026-01-03 22:48:10 +08:00
HaHafeng
5f089516cb feat(iit-manager): Day 3 企业微信集成开发完成 
- 新增WechatService（企业微信推送服务，支持文本/卡片/Markdown消息）
- 新增WechatCallbackController（异步回复模式，5秒内响应）
- 完善iit_quality_check Worker（调用WechatService推送通知）
- 新增企业微信回调路由（GET验证+POST接收消息）
- 实现LLM意图识别（query_weekly_summary/query_patient_info等）
- 安装依赖：@wecom/crypto, xml2js
- 更新开发记录文档和MVP开发计划

技术要点：
- 使用异步回复模式规避企业微信5秒超时限制
- 使用@wecom/crypto官方库处理XML加解密
- 使用setImmediate实现后台异步处理
- 支持主动推送消息返回LLM处理结果
- 完善审计日志记录（WECHAT_NOTIFICATION_SENT/WECHAT_INTERACTION）

相关文档：
- docs/03-业务模块/IIT Manager Agent/06-开发记录/Day3-企业微信集成开发完成记录.md
- docs/03-业务模块/IIT Manager Agent/04-开发计划/最小MVP闭环开发计划.md
- docs/03-业务模块/IIT Manager Agent/00-模块当前状态与开发指南.md
 2026-01-03 09:39:39 +08:00
HaHafeng
bdfca32305 docs(iit): REDCap对接技术方案完成与模块状态更新 
- 新增《REDCap对接技术方案与实施指南》(1070行)
  - 确定DET+REST API技术方案(不使用External Module)
  - 完整RedcapAdapter/WebhookController/SyncManager代码设计
  - Day 2详细实施步骤与验收标准
- 更新《IIT Manager Agent模块当前状态与开发指南》
  - 记录REDCap本地环境部署完成(15.8.0)
  - 记录对接方案确定过程与技术决策
  - 更新Day 2工作计划(6个阶段详细清单)
  - 整体进度18%(Day 1完成+REDCap环境就绪)
- REDCap环境准备完成
  - 测试项目test0102(PID 16)创建成功
  - DET功能源码验证通过
  - 本地Docker环境稳定运行

技术方案:
- 实时触发: Data Entry Trigger (0秒延迟)
- 数据拉取: REST API exportRecords (增量同步)
- 轮询补充: pg-boss定时任务 (每30分钟)
- 可靠性: Webhook幂等性 + 轮询补充机制
 2026-01-02 14:30:38 +08:00
HaHafeng
dac3cecf78 feat(iit): Complete IIT Manager Agent Day 1 - Environment initialization and WeChat integration 
Summary:
- Complete IIT Manager Agent MVP Day 1 (12.5% progress)
- Database: Create iit_schema with 5 tables (IitProject, IitPendingAction, IitTaskRun, IitUserMapping, IitAuditLog)
- Backend: Add module structure (577 lines) and types (223 lines)
- WeChat: Configure Enterprise WeChat app (CorpID, AgentID, Secret)
- WeChat: Obtain web authorization and JS-SDK authorization
- WeChat: Configure trusted domain (iit.xunzhengyixue.com)
- Frontend: Deploy v1.2 with WeChat domain verification file
- Frontend: Fix CRLF issue in docker-entrypoint.sh (CRLF -> LF)
- Testing: 11/11 database CRUD tests passed
- Testing: Access Token retrieval test passed
- Docs: Create module status and development guide
- Docs: Update MVP task list with Day 1 completion
- Docs: Rename deployment doc to SAE real-time status record
- Deployment: Update frontend internal IP to 172.17.173.80

Technical Details:
- Prisma: Multi-schema support (iit_schema)
- pg-boss: Job queue integration prepared
- Taro 4.x: Framework selected for WeChat Mini Program
- Shadow State: Architecture foundation laid
- Docker: Fix entrypoint script line endings for Linux container

Status: Day 1/14 complete, ready for Day 2 REDCap integration
 2026-01-01 14:32:58 +08:00
HaHafeng
4c5bb3d174 feat(iit): Initialize IIT Manager Agent MVP - Day 1 complete 
- Add iit_schema with 5 tables
- Create module structure and types (223 lines)
- WeChat integration verified (Access Token success)
- Update system docs to v2.4
- Add REDCap source folders to .gitignore
- Day 1/14 complete (11/11 tasks)
 2025-12-31 18:35:05 +08:00
HaHafeng
decff0bb1f docs(deploy): Complete full system deployment to Aliyun SAE 
Summary:
- Successfully deployed complete system to Aliyun SAE (2025-12-25)
- All services running: Python microservice + Node.js backend + Frontend Nginx + CLB
- Public access available at http://8.140.53.236/

Major Achievements:
1. Python microservice deployed (v1.0, internal IP: 172.17.173.66:8000)
2. Node.js backend deployed (v1.3, internal IP: 172.17.173.73:3001)
   - Fixed 4 critical issues: bash path, config directory, pino-pretty, ES Module
3. Frontend Nginx deployed (v1.0, internal IP: 172.17.173.72:80)
4. CLB load balancer configured (public IP: 8.140.53.236)

New Documentation (9 docs):
- 11-Node.js backend SAE deployment config checklist (21 env vars)
- 12-Node.js backend SAE deployment operation manual
- 13-Node.js backend image fix record (config directory)
- 14-Node.js backend pino-pretty fix
- 15-Node.js backend deployment success summary
- 16-Frontend Nginx deployment success summary
- 17-Complete deployment practical manual 2025 edition (1800 lines)
- 18-Deployment documentation usage guide
- 19-Daily update quick operation manual (670 lines)

Key Fixes:
- Environment variable name correction: EXTRACTION_SERVICE_URL (not PYTHON_SERVICE_URL)
- Dockerfile fix: added COPY config ./config
- Logger configuration: conditional pino-pretty for dev only
- Health check fix: ES Module compatibility (require -> import)

Updated Files:
- System status document updated with full deployment info
- Deployment progress overview updated with latest IPs
- All 3 Docker services' Dockerfiles and configs refined

Verification:
- All health checks passed
- Tool C 7 features working correctly
- Literature screening module functional
- Response time < 1 second

BREAKING CHANGE: Node.js backend internal IP changed from 172.17.173.71 to 172.17.173.73

Closes #deployment-milestone
 2025-12-25 21:24:37 +08:00
HaHafeng
691dc2bc98 docs(deploy): Update deployment documentation for Node.js backend 
Summary:
- Created Node.js backend Docker image build guide
- Updated deployment progress overview with backend status
- Updated system status documentation

Backend build achievements:
- Fixed 200+ TypeScript compilation errors (200+ to 0)
- Completed Prisma reverse sync (32 models from RDS)
- Manually added 30+ Prisma relation fields
- Successfully built Docker image (838MB)
- Pushed image to ACR (v1.0 + latest tags)

Documentation updates:
- Added 10-Node.js后端-Docker镜像构建手册.md
- Updated 00-部署进度总览.md with backend deployment status
- Updated 00-系统当前状态与开发指南.md with latest progress
- Fixed date format (2024 -> 2025)

Next steps:
- Deploy Node.js backend to SAE
- Configure environment variables
- Test end-to-end functionality

Status: Backend Docker image ready for SAE deployment
 2025-12-25 08:21:21 +08:00
HaHafeng
ef967d7d7c build(backend): Complete Node.js backend deployment preparation 
Major changes:
- Add Docker configuration (Dockerfile, .dockerignore)
- Fix 200+ TypeScript compilation errors
- Add Prisma schema relations for all models (30+ relations)
- Update tsconfig.json to relax non-critical checks
- Optimize Docker build with local dist strategy

Technical details:
- Exclude test files from TypeScript compilation
- Add manual relations for ASL, PKB, DC, AIA modules
- Use type assertions for JSON/Buffer compatibility
- Fix pg-boss, extractionWorker, and other legacy code issues

Build result:
- Docker image: 838MB (compressed ~186MB)
- Successfully pushed to ACR
- Zero TypeScript compilation errors

Related docs:
- Update deployment documentation
- Add Python microservice SAE deployment guide
 2025-12-24 22:12:00 +08:00
HaHafeng
b64896a307 feat(deploy): Complete PostgreSQL migration and Docker image build 
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
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
HaHafeng
9b81aef9a7 feat(dc): Add multi-metric transformation feature (direction 1+2) 
Summary:
- Implement intelligent multi-metric grouping detection algorithm
- Add direction 1: timepoint-as-row, metric-as-column (analysis format)
- Add direction 2: timepoint-as-column, metric-as-row (display format)
- Fix column name pattern detection (FMA___ issue)
- Maintain original Record ID order in output
- Add full-select/clear buttons in UI
- Integrate into TransformDialog with Radio selection
- Update 3 documentation files

Technical Details:
- Python: detect_metric_groups(), apply_multi_metric_to_long(), apply_multi_metric_to_matrix()
- Backend: 3 new methods in QuickActionService
- Frontend: MultiMetricPanel.tsx (531 lines)
- Total: ~1460 lines of new code

Status: Fully tested and verified, ready for production
 2025-12-21 15:06:15 +08:00
HaHafeng
19f9c5ea93 docs(deployment): Fix 8 critical deployment issues and enhance documentation 
Summary of fixes:
- Fix service discovery address (change .sae domain to internal IP)
- Unify timezone configuration (Asia/Shanghai for all services)
- Enhance ECS security group configuration (Redis/Weaviate port binding)
- Add image pull strategy best practices
- Add Python service memory management guidelines
- Update Dify API Key deployment strategy (avoid deadlock)
- Add SSH tunnel for RDS database access
- Add NAT gateway cost optimization explanation

Modified files (7 docs):
- 00-部署架构总览.md (enhanced with 7 sections)
- 03-Dify-ECS部署完全指南.md (security hardening)
- 04-Python微服务-SAE容器部署指南.md (timezone + service discovery)
- 05-Node.js后端-SAE容器部署指南.md (timezone configuration)
- PostgreSQL部署策略-摸底报告.md (timezone best practice)
- 07-关键配置补充说明.md (3 new sections)
- 08-部署检查清单.md (service address fix)

New files:
- 文档修正报告-20251214.md (comprehensive fix report)
- Review documents from technical team

Impact:
- Fixed 3 P0/P1 critical issues (100% connection failure risk)
- Fixed 3 P2 important issues (stability and maintainability)
- Added 2 P3 best practices (developer convenience)

Status: All deployment documents reviewed and corrected, ready for production deployment
 2025-12-14 13:25:28 +08:00
HaHafeng
fa72beea6c feat(platform): Complete Postgres-Only architecture refactoring (Phase 1-7) 
Major Changes:
- Implement Platform-Only architecture pattern (unified task management)
- Add PostgresCacheAdapter for unified caching (platform_schema.app_cache)
- Add PgBossQueue for job queue management (platform_schema.job)
- Implement CheckpointService using job.data (generic for all modules)
- Add intelligent threshold-based dual-mode processing (THRESHOLD=50)
- Add task splitting mechanism (auto chunk size recommendation)
- Refactor ASL screening service with smart mode selection
- Refactor DC extraction service with smart mode selection
- Register workers for ASL and DC modules

Technical Highlights:
- All task management data stored in platform_schema.job.data (JSONB)
- Business tables remain clean (no task management fields)
- CheckpointService is generic (shared by all modules)
- Zero code duplication (DRY principle)
- Follows 3-layer architecture principle
- Zero additional cost (no Redis needed, save 8400 CNY/year)

Code Statistics:
- New code: ~1750 lines
- Modified code: ~500 lines
- Test code: ~1800 lines
- Documentation: ~3000 lines

Testing:
- Unit tests: 8/8 passed
- Integration tests: 2/2 passed
- Architecture validation: passed
- Linter errors: 0

Files:
- Platform layer: PostgresCacheAdapter, PgBossQueue, CheckpointService, utils
- ASL module: screeningService, screeningWorker
- DC module: ExtractionController, extractionWorker
- Tests: 11 test files
- Docs: Updated 4 key documents

Status: Phase 1-7 completed, Phase 8-9 pending
 2025-12-13 16:10:04 +08:00
HaHafeng
74cf346453 feat(dc/tool-c): Add missing value imputation feature with 6 methods and MICE 
Major features:
1. Missing value imputation (6 simple methods + MICE):
   - Mean/Median/Mode/Constant imputation
   - Forward fill (ffill) and Backward fill (bfill) for time series
   - MICE multivariate imputation (in progress, shape issue to fix)

2. Auto precision detection:
   - Automatically match decimal places of original data
   - Prevent false precision (e.g. 13.57 instead of 13.566716417910449)

3. Categorical variable detection:
   - Auto-detect and skip categorical columns in MICE
   - Show warnings for unsuitable columns
   - Suggest mode imputation for categorical data

4. UI improvements:
   - Rename button: "Delete Missing" to "Missing Value Handling"
   - Remove standalone "Dedup" and "MICE" buttons
   - 3-tab dialog: Delete / Fill / Advanced Fill
   - Display column statistics and recommended methods
   - Extended warning messages (8 seconds for skipped columns)

5. Bug fixes:
   - Fix sessionService.updateSessionData -> saveProcessedData
   - Fix OperationResult interface (add message and stats)
   - Fix Toolbar button labels and removal

Modified files:
Python: operations/fillna.py (new, 556 lines), main.py (3 new endpoints)
Backend: QuickActionService.ts, QuickActionController.ts, routes/index.ts
Frontend: MissingValueDialog.tsx (new, 437 lines), Toolbar.tsx, index.tsx
Tests: test_fillna_operations.py (774 lines), test scripts and docs
Docs: 5 documentation files updated

Known issues:
- MICE imputation has DataFrame shape mismatch issue (under debugging)
- Workaround: Use 6 simple imputation methods first

Status: Development complete, MICE debugging in progress
Lines added: ~2000 lines across 3 tiers
 2025-12-10 13:06:00 +08:00