b31255031e
feat(iit-manager): Add WeChat Official Account integration for patient notifications
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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
dfc472810b
feat(iit-manager): Integrate Dify knowledge base for hybrid retrieval
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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
b47079b387
feat(iit): Phase 1.5 AI对话集成REDCap真实数据完成
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- 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
5f089516cb
feat(iit-manager): Day 3 企业微信集成开发完成
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- 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
bdfca32305
docs(iit): REDCap对接技术方案完成与模块状态更新
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- 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
dac3cecf78
feat(iit): Complete IIT Manager Agent Day 1 - Environment initialization and WeChat integration
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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
4c5bb3d174
feat(iit): Initialize IIT Manager Agent MVP - Day 1 complete
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- 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
decff0bb1f
docs(deploy): Complete full system deployment to Aliyun SAE
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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
691dc2bc98
docs(deploy): Update deployment documentation for Node.js backend
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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
ef967d7d7c
build(backend): Complete Node.js backend deployment preparation
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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
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
4c6eaaecbf
feat(dc): Implement Postgres-Only async architecture and performance optimization
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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
9b81aef9a7
feat(dc): Add multi-metric transformation feature (direction 1+2)
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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
19f9c5ea93
docs(deployment): Fix 8 critical deployment issues and enhance documentation
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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
fa72beea6c
feat(platform): Complete Postgres-Only architecture refactoring (Phase 1-7)
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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
74cf346453
feat(dc/tool-c): Add missing value imputation feature with 6 methods and MICE
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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
75ceeb0653
hotfix(dc/tool-c): Fix compute formula validation and binning NaN serialization
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Critical fixes:
1. Compute column: Add Chinese comma support in formula validation
- Problem: Formula with Chinese comma failed validation
- Fix: Add Chinese comma character to allowed_chars regex
- Example: Support formulas like 'col1（kg）+ col2，col3'
2. Binning operation: Fix NaN serialization error
- Problem: 'Out of range float values are not JSON compliant: nan'
- Fix: Enhanced NaN/inf handling in binning endpoint
- Added np.inf/-np.inf replacement before JSON serialization
- Added manual JSON serialization with NaN->null conversion
3. Enhanced all operation endpoints for consistency
- Updated conditional, dropna endpoints with same NaN/inf handling
- Ensures all operations return JSON-compliant data
Modified files:
- extraction_service/operations/compute.py: Add Chinese comma to regex
- extraction_service/main.py: Enhanced NaN handling in binning/conditional/dropna
Status: Hotfix complete, ready for testing
2025-12-09 08:45:27 +08:00
91cab452d1
fix(dc/tool-c): Fix special character handling and improve UX
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Major fixes:
- Fix pivot transformation with special characters in column names
- Fix compute column validation for Chinese punctuation
- Fix recode dialog to fetch unique values from full dataset via new API
- Add column mapping mechanism to handle special characters
Database migration:
- Add column_mapping field to dc_tool_c_sessions table
- Migration file: 20251208_add_column_mapping
UX improvements:
- Darken table grid lines for better visibility
- Reduce column width by 40% with tooltip support
- Insert new columns next to source columns
- Preserve original row order after operations
- Add notice about 50-row preview limit
Modified files:
- Backend: SessionService, SessionController, QuickActionService, routes
- Python: pivot.py, compute.py, recode.py, binning.py, conditional.py
- Frontend: DataGrid, RecodeDialog, index.tsx, ag-grid-custom.css
- Database: schema.prisma, migration SQL
Status: Code complete, database migrated, ready for testing
2025-12-08 23:20:55 +08:00
f729699510
feat(dc): Complete Tool C quick action buttons Phase 1-2 - 7 functions
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Summary:
- Implement 7 quick action functions (filter, recode, binning, conditional, dropna, compute, pivot)
- Refactor to pre-written Python functions architecture (stable and secure)
- Add 7 Python operations modules with full type hints
- Add 7 frontend Dialog components with user-friendly UI
- Fix NaN serialization issues and auto type conversion
- Update all related documentation
Technical Details:
- Python: operations/ module (filter.py, recode.py, binning.py, conditional.py, dropna.py, compute.py, pivot.py)
- Backend: QuickActionService.ts with 7 execute methods
- Frontend: 7 Dialog components with complete validation
- Toolbar: Enable 7 quick action buttons
Status: Phase 1-2 completed, basic testing passed, ready for further testing
2025-12-08 17:38:08 +08:00
f01981bf78
feat(dc/tool-c): 完成AI代码生成服务（Day 3 MVP）
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核心功能：
- 新增AICodeService（550行）：AI代码生成核心服务
- 新增AIController（257行）：4个API端点
- 新增dc_tool_c_ai_history表：存储对话历史
- 实现自我修正机制：最多3次智能重试
- 集成LLMFactory：复用通用能力层
- 10个Few-shot示例：覆盖Level 1-4场景
技术优化：
- 修复NaN序列化问题（Python端转None）
- 修复数据传递问题（从Session获取真实数据）
- 优化System Prompt（明确环境信息）
- 调整Few-shot示例（移除import语句）
测试结果：
- 通过率：9/11（81.8%） 达到MVP标准
- 成功场景：缺失值处理、编码、分箱、BMI、筛选、填补、统计、分类
- 待优化：数值清洗、智能去重（已记录技术债务TD-C-006）
API端点：
- POST /api/v1/dc/tool-c/ai/generate（生成代码）
- POST /api/v1/dc/tool-c/ai/execute（执行代码）
- POST /api/v1/dc/tool-c/ai/process（生成并执行，一步到位）
- GET /api/v1/dc/tool-c/ai/history/:sessionId（对话历史）
文档更新：
- 新增Day 3开发完成总结（770行）
- 新增复杂场景优化技术债务（TD-C-006）
- 更新工具C当前状态文档
- 更新技术债务清单
影响范围：
- backend/src/modules/dc/tool-c/*（新增2个文件，更新1个文件）
- backend/scripts/create-tool-c-ai-history-table.mjs（新增）
- backend/prisma/schema.prisma（新增DcToolCAiHistory模型）
- extraction_service/services/dc_executor.py（NaN序列化修复）
- docs/03-业务模块/DC-数据清洗整理/*（5份文档更新）
Breaking Changes: 无
总代码行数：+950行
Refs: #Tool-C-Day3
2025-12-07 16:21:32 +08:00
2348234013
feat(dc/tool-c): Day 2 - Session管理与数据处理完成
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核心功能:
- 数据库: 创建dc_tool_c_sessions表 (12字段, 3索引)
- 服务层: SessionService (383行), DataProcessService (303行)
- 控制器: SessionController (300行, 6个API端点)
- 路由: 新增6个Session管理路由
- 测试: 7个API测试全部通过 (100%)
技术亮点:
- 零落盘架构: Excel内存解析, OSS存储
- Session管理: 10分钟过期, 心跳延长机制
- 云原生规范: storage/logger/prisma全平台复用
- 完整测试: 上传/预览/完整数据/删除/心跳
文件清单:
- backend/prisma/schema.prisma (新增DcToolCSession模型)
- backend/prisma/migrations/create_tool_c_session.sql
- backend/scripts/create-tool-c-table.mjs
- backend/src/modules/dc/tool-c/services/ (SessionService, DataProcessService)
- backend/src/modules/dc/tool-c/controllers/SessionController.ts
- backend/src/modules/dc/tool-c/routes/index.ts
- backend/test-tool-c-day2.mjs
- docs/03-业务模块/DC-数据清洗整理/00-工具C当前状态与开发指南.md
- docs/03-业务模块/DC-数据清洗整理/06-开发记录/2025-12-06_工具C_Day2开发完成总结.md
代码统计: ~1900行
测试结果: 7/7 通过 (100%)
云原生规范: 完全符合
2025-12-06 22:12:47 +08:00
8be741cd52
docs(dc/tool-c): Complete Tool C MVP planning and TODO list
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Summary:
- Update Tool C MVP Development Plan (V1.3)
* Clarify Python execution as core feature
* Add 15 real medical data cleaning scenarios (basic/medium/advanced)
* Enhance System Prompt with 10 Few-shot examples
* Discover existing Python service (extraction_service)
* Update to extend existing service instead of rebuilding
- Create Tool C MVP Development TODO List
* 3-week plan with 30 tasks (Day 1-15)
* 4 core milestones with clear acceptance criteria
* Daily checklist and risk management
* Detailed task breakdown for each day
Key Changes:
- Python service: Extend existing extraction_service instead of new setup
- Test scenarios: 15 scenarios (5 basic + 5 medium + 5 advanced)
- Success criteria: Basic >90%, Medium >80%, Advanced >60%, Total >80%
- Development time: Reduced from 3 weeks to 2 weeks (reuse infrastructure)
Status: Planning complete, ready to start Day 1 development
2025-12-06 11:00:44 +08:00
8a17369138
feat(dc): Complete Tool B MVP with full API integration and bug fixes
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Phase 5: Export Feature
- Add Excel export API endpoint (GET /tasks/:id/export)
- Fix Content-Disposition header encoding for Chinese filenames
- Fix export field order to match template definition
- Export finalResult or resultA as fallback
API Integration Fixes (Phase 1-5):
- Fix API response parsing (return result.data consistently)
- Fix field name mismatch (fileKey -> sourceFileKey)
- Fix Excel parsing bug (range:99 -> slice(0,100))
- Add file upload with Excel parsing (columns, totalRows)
- Add detailed error logging for debugging
LLM Integration Fixes:
- Fix LLM call method: LLMFactory.createLLM -> getAdapter
- Fix adapter interface: generateText -> chat([messages])
- Fix response fields: text -> content, tokensUsed -> usage.totalTokens
- Fix model names: qwen-max -> qwen3-72b
React Infinite Loop Fixes:
- Step2: Remove updateState from useEffect deps
- Step3: Add useRef to prevent Strict Mode double execution
- Step3: Clear interval on API failure (max 3 retries)
- Step4: Add useRef to prevent infinite data loading
- Add cleanup functions to all useEffect hooks
Frontend Enhancements:
- Add comprehensive error handling with user-friendly messages
- Remove debug console.logs (production ready)
- Fix TypeScript type definitions (TaskProgress, ExtractionItem)
- Improve Step4Verify data transformation logic
Backend Enhancements:
- Add detailed logging at each step for debugging
- Add parameter validation in controllers
- Improve error messages with stack traces (dev mode)
- Add export field ordering by template definition
Documentation Updates:
- Update module status: Tool B MVP completed
- Create MVP completion summary (06-开发记录)
- Create technical debt document (07-技术债务)
- Update API documentation with test status
- Update database documentation with verified status
- Update system overview with DC module status
- Document 4 known issues (Excel preprocessing, progress display, etc.)
Testing Results:
- File upload: 9 rows parsed successfully
- Health check: Column validation working
- Dual model extraction: DeepSeek-V3 + Qwen-Max both working
- Processing time: ~49s for 9 records (~5s per record)
- Token usage: ~10k tokens total (~1.1k per record)
- Conflict detection: 1 clean, 8 conflicts (88.9% conflict rate)
- Excel export: Working with proper encoding
Files Changed:
Backend (~500 lines):
- ExtractionController.ts: Add upload endpoint, improve logging
- DualModelExtractionService.ts: Fix LLM call methods, add detailed logs
- HealthCheckService.ts: Fix Excel range parsing
- routes/index.ts: Add upload route
Frontend (~200 lines):
- toolB.ts: Fix API response parsing, add error handling
- Step1Upload.tsx: Integrate upload and health check APIs
- Step2Schema.tsx: Fix infinite loop, load templates from API
- Step3Processing.tsx: Fix infinite loop, integrate progress polling
- Step4Verify.tsx: Fix infinite loop, transform backend data correctly
- Step5Result.tsx: Integrate export API
- index.tsx: Add file metadata to state
Scripts:
- check-task-progress.mjs: Database inspection utility
Docs (~8 files):
- 00-模块当前状态与开发指南.md: Update to v2.0
- API设计文档.md: Mark all endpoints as tested
- 数据库设计文档.md: Update verification status
- DC模块Tool-B开发计划.md: Add MVP completion notice
- DC模块Tool-B开发任务清单.md: Update progress to 100%
- Tool-B-MVP完成总结.md: New completion summary
- Tool-B技术债务清单.md: New technical debt document
- 00-系统当前状态与开发指南.md: Update DC module status
Status: Tool B MVP complete and production ready
2025-12-03 15:07:39 +08:00
5f1e7af92c
feat(dc): Complete Tool B frontend development with UI optimization
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- Implement Tool B 5-step workflow (upload, schema, processing, verify, result)
- Add back navigation button to Portal
- Optimize Step 2 field list styling to match prototype
- Fix step 3 label: 'dual-blind' to 'dual-model'
- Create API service layer with 7 endpoints
- Integrate Tool B route into DC module
- Add comprehensive TypeScript types
Components (~1100 lines):
- index.tsx: Main Tool B entry with state management
- Step1Upload.tsx: File upload and health check
- Step2Schema.tsx: Smart template configuration
- Step3Processing.tsx: Dual-model extraction progress
- Step4Verify.tsx: Conflict verification workbench
- Step5Result.tsx: Result display
- StepIndicator.tsx: Step progress component
- api/toolB.ts: API service layer
Status: Frontend complete, ready for API integration
2025-12-03 09:36:35 +08:00
d4d33528c7
feat(dc): Complete Phase 1 - Portal workbench page development
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Summary:
- Implement DC module Portal page with 3 tool cards
- Create ToolCard component with decorative background and hover animations
- Implement TaskList component with table layout and progress bars
- Implement AssetLibrary component with tab switching and file cards
- Complete database verification (4 tables confirmed)
- Complete backend API verification (6 endpoints ready)
- Optimize UI to match prototype design (V2.html)
Frontend Components (~715 lines):
- components/ToolCard.tsx - Tool cards with animations
- components/TaskList.tsx - Recent tasks table view
- components/AssetLibrary.tsx - Data asset library with tabs
- hooks/useRecentTasks.ts - Task state management
- hooks/useAssets.ts - Asset state management
- pages/Portal.tsx - Main portal page
- types/portal.ts - TypeScript type definitions
Backend Verification:
- Backend API: 1495 lines code verified
- Database: dc_schema with 4 tables verified
- API endpoints: 6 endpoints tested (templates API works)
Documentation:
- Database verification report
- Backend API test report
- Phase 1 completion summary
- UI optimization report
- Development task checklist
- Development plan for Tool B
Status: Phase 1 completed (100%), ready for browser testing
Next: Phase 2 - Tool B Step 1 and 2 development
2025-12-02 21:53:24 +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
2e8699c217
feat(asl): Week 2 Day 2 - Excel import with template download and intelligent dedup
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Features:
- feat: Excel template generation and download (with examples)
- feat: Excel file parsing in memory (cloud-native, no disk write)
- feat: Field validation (title + abstract required)
- feat: Smart deduplication (DOI priority + Title fallback)
- feat: Literature preview table with statistics
- feat: Complete submission flow (create project + import literatures)
Components:
- feat: Create excelUtils.ts with full Excel processing toolkit
- feat: Enhance TitleScreeningSettings page with upload/preview/submit
- feat: Update API interface signatures and export unified aslApi object
Dependencies:
- chore: Add xlsx library for Excel file processing
Ref: Week 2 Frontend Development - Day 2
Scope: ASL Module MVP - Title Abstract Screening
Cloud-Native: Memory parsing, no file persistence
2025-11-19 10:24:47 +08:00
3634933ece
refactor(asl): ASL frontend architecture refactoring with left navigation
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- feat: Create ASLLayout component with 7-module left navigation
- feat: Implement Title Screening Settings page with optimized PICOS layout
- feat: Add placeholder pages for Workbench and Results
- fix: Fix nested routing structure for React Router v6
- fix: Resolve Spin component warning in MainLayout
- fix: Add QueryClientProvider to App.tsx
- style: Optimize PICOS form layout (P+I left, C+O+S right)
- style: Align Inclusion/Exclusion criteria side-by-side
- docs: Add architecture refactoring and routing fix reports
Ref: Week 2 Frontend Development
Scope: ASL module MVP - Title Abstract Screening
2025-11-18 21:51:51 +08:00
