HaHafeng
b64896a307
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
1.8 KiB
1.8 KiB
🚀 快速开始 - 1分钟运行测试
Windows用户
方法1：双击运行（最简单）
- 双击
run_tests.bat - 等待测试完成
方法2：命令行
cd AIclinicalresearch\tests
run_tests.bat
Linux/Mac用户
cd AIclinicalresearch/tests
chmod +x run_tests.sh
./run_tests.sh
⚠️ 前提条件
必须先启动Python服务！
# 打开新终端
cd AIclinicalresearch/extraction_service
python main.py
看到这行表示启动成功：
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8001
📊 预期结果
✅ 全部通过：
总测试数: 18
✅ 通过: 18
❌ 失败: 0
通过率: 100.0%
🎉 所有测试通过！
⚠️ 部分失败：
- 查看红色错误信息
- 检查失败的具体测试
- 查看Python服务日志
🎯 测试内容
- ✅ 6种简单填补方法（均值、中位数、众数、固定值、前向填充、后向填充）
- ✅ MICE多重插补（单列、多列）
- ✅ 边界情况（100%缺失、0%缺失、特殊字符）
- ✅ 各种数据类型（数值、分类、混合）
- ✅ 性能测试（1000行数据）
💡 提示
- 第一次运行会自动安装依赖（pandas, numpy, requests）
- 测试时间约 45-60 秒
- 测试数据自动生成，无需手动准备
- 颜色输出：绿色=通过，红色=失败，黄色=警告
🆘 遇到问题？
问题1：无法连接到服务
解决：确保Python服务在运行（python main.py）
问题2：依赖安装失败
解决：手动安装 pip install pandas numpy requests
问题3：测试失败
解决：查看错误信息，检查代码逻辑
准备好了吗？启动服务，运行测试！ 🚀