AI 全栈开发实战（11）：CI/CD 与自动化测试——从 pytest 到 GitHub Actions

AI 项目的 CI/CD 怎么做？自动化测试和部署流水线搭建

代码写完了，怎么保证每次修改不会破坏已有功能？怎么自动部署到服务器？

本篇回答三个问题：

怎么写 AI 项目的自动化测试？

后端测试（pytest）

# backend/tests/test_api.py
import pytest
from httpx import ASGITransport, AsyncClient
from app.main import app

@pytest.fixture
def client():
transport = ASGITransport(app=app)
return AsyncClient(transport=transport, base_url="http://test")

@pytest.mark.asyncio
async def test_health_check(client):
"""健康检查接口测试。"""
response = await client.get("/api/health")
assert response.status_code == 200
assert response.json()["status"] == "ok"

@pytest.mark.asyncio
async def test_register(client):
"""用户注册接口测试。"""
import random
email = f"test{random.randint(10000,99999)}@example.com"
response = await client.post("/api/auth/register", json={
"email": email,
"password": "test123456",
"nickname": "测试用户",
})
assert response.status_code == 201
data = response.json()
assert "access_token" in data
assert data["user"]["email"] == email

@pytest.mark.asyncio
async def test_login_invalid_password(client):
"""错误密码登录测试。"""
response = await client.post("/api/auth/login", json={
"email": "nonexist@example.com",
"password": "wrong",
})
assert response.status_code == 401

@pytest.mark.asyncio
async def test_create_knowledge_base(client):
"""创建知识库测试（需先登录）。"""
# 先注册
import random
email = f"kb{random.randint(10000,99999)}@example.com"
reg = await client.post("/api/auth/register", json={
"email": email, "password": "test123456", "nickname": "test"
})
token = reg.json()["access_token"]

# 创建知识库
response = await client.post("/api/knowledge-bases",
json={"name": "测试知识库", "description": "测试描述"},
headers={"Authorization": f"Bearer {token}"}
)
assert response.status_code == 201
assert response.json()["name"] == "测试知识库"

测试数据库隔离

测试不应该用生产数据库。用 pytest 的 fixture 隔离：

# backend/tests/conftest.py
import pytest
from app.database import engine, Base
from sqlalchemy.ext.asyncio import create_async_engine

@pytest.fixture(autouse=True)
async def setup_db():
"""每个测试用例使用独立的数据库会话。"""
# 使用 SQLite 内存数据库进行测试
test_engine = create_async_engine("sqlite+aiosqlite:///:memory:")
async with test_engine.begin() as conn:
await conn.run_sync(Base.metadata.create_all)
yield
await test_engine.dispose()

CI/CD 流水线怎么配？

GitHub Actions 配置

# .github/workflows/ci.yml
name: CI

on:
push:
branches: [main, develop]
pull_request:
branches: [main]

jobs:
test:
runs-on: ubuntu–latest

services:
postgres:
image: postgres:16–alpine
env:
POSTGRES_DB: know_test
POSTGRES_USER: know
POSTGRES_PASSWORD: know_test
ports:
– 5432:5432
options: ––health–cmd pg_isready ––health–interval 10s ––health–timeout 5s ––health–retries 5

redis:
image: redis:7–alpine
ports:
– 6379:6379
options: ––health–cmd "redis–cli ping" ––health–interval 10s ––health–timeout 5s ––health–retries 5

steps:
– uses: actions/checkout@v4

– name: Set up Python
uses: actions/setup–python@v5
with:
python-version: "3.11"

– name: Install dependencies
run: |
cd backend
pip install -r requirements.txt
pip install pytest httpx pytest-asyncio

– name: Run tests
env:
DATABASE_URL: postgresql+asyncpg://know:know_test@localhost:5432/know_test
REDIS_URL: redis://localhost:6379/0
run: |
cd backend
pytest tests/ -v –cov=app –cov-report=term-missing

代码质量检查

# 在 ci.yml 中加一个 lint job
lint:
runs-on: ubuntu–latest
steps:
– uses: actions/checkout@v4
– uses: actions/setup–python@v5
with:
python-version: "3.11"
– run: pip install ruff mypy
– run: ruff check backend/
– run: mypy backend/ ––ignore–missing–imports

Docker 镜像构建与推送

build:
needs: [test, lint]
runs-on: ubuntu–latest
if: github.ref == 'refs/heads/main'
steps:
– uses: actions/checkout@v4

– name: Set up Docker Buildx
uses: docker/setup–buildx–action@v3

– name: Log in to Docker Hub
uses: docker/login–action@v3
with:
username: ${{ secrets.DOCKER_USERNAME }}
password: ${{ secrets.DOCKER_PASSWORD }}

– name: Build and push backend
uses: docker/build–push–action@v5
with:
context: ./backend
push: true
tags: yourdockerhub/know–backend:latest
cache-from: type=gha
cache-to: type=gha,mode=max

– name: Build and push frontend
uses: docker/build–push–action@v5
with:
context: ./frontend
push: true
tags: yourdockerhub/know–frontend:latest
cache-from: type=gha
cache-to: type=gha,mode=max

部署自动化怎么做？

自动部署到服务器

deploy:
needs: [build]
runs-on: ubuntu–latest
if: github.ref == 'refs/heads/main'
steps:
– name: Deploy via SSH
uses: appleboy/ssh–action@v1.0.3
with:
host: ${{ secrets.DEPLOY_HOST }}
username: ${{ secrets.DEPLOY_USER }}
key: ${{ secrets.DEPLOY_KEY }}
script: |
cd /opt/know
docker compose pull
docker compose up -d –force-recreate
docker image prune -f

完整 CI/CD 流水线

提交代码 → 自动测试 → 代码检查 → 构建镜像 → 推送到仓库 → 自动部署
git push pytest ruff docker docker push ssh deploy

本地测试常用命令

# 运行所有测试
cd backend && pytest tests/ -v

# 运行单个测试文件
pytest tests/test_api.py -v

# 运行单个测试函数
pytest tests/test_api.py::test_register -v

# 带覆盖率报告
pytest tests/ –cov=app –cov-report=html

# 代码格式检查
ruff check app/

# 类型检查
mypy app/

总结

本文是 《AI 全栈开发实战——做一个真正的产品》 系列的第 11 篇。 本文由 Zyentor（智元界） 原创发布

本文发布于 Zyentor（智元界） —— AI 开发者社区 原文链接：https://www.zyentor.com/news/3881