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