# 确认 Ollama 运行中
curl http://localhost:11434/api/tags

# 选择一个擅长代码的模型
ollama pull llama3.3:70b  # 或 deepseek-coder:33b

#!/bin/bash
# generate-compose.sh

read -r -d '' PROMPT << 'EOF'
你是一个 Docker Compose 专家。请根据以下需求生成 docker-compose.yml：

服务：Python FastAPI 应用 + PostgreSQL 16 + Redis 7
要求：
- FastAPI 使用 uvicorn 运行
- 数据库密码从 .env 读取
- 设置健康检查
- 使用 volumes 持久化数据
- 生产环境级别的配置
- 添加 docker-compose.yml 注释说明

只输出 YAML 代码，不要多余文字。
EOF

curl -s http://localhost:11434/api/generate \
  -d "{\"model\": \"llama3.3:70b\", \"prompt\": \"$PROMPT\", \"stream\": false}" \
  | jq -r '.response' > docker-compose.yml

echo "✅ docker-compose.yml 已生成"

# ai-compose-agent.py (伪代码)
import subprocess
import json

def generate_compose(requirements):
    """用 LLM 生成 Compose 配置"""
    prompt = f"根据需求生成 docker-compose.yml：{requirements}"
    # 调用 Ollama API
    return response

def deploy_and_check(compose_content):
    """部署并检查运行状态"""
    with open('docker-compose.yml', 'w') as f:
        f.write(compose_content)
    result = subprocess.run(['docker', 'compose', 'up', '-d'], 
                          capture_output=True, text=True)
    # 检查各服务健康状态
    status = subprocess.run(['docker', 'compose', 'ps', '--format', 'json'],
                          capture_output=True, text=True)
    return result.returncode, result.stderr, status.stdout

def optimize(compose_content, errors):
    """用 LLM 修复问题"""
    prompt = f"以下 docker-compose.yml 部署出错。请修复：\n\n{compose_content}\n\n错误信息：{errors}"
    # 调用 LLM 生成修复后的版本
    return new_compose

# 循环迭代
max_attempts = 5
for i in range(max_attempts):
    compose = generate_compose(requirements) if i == 0 else optimize(compose, errors)
    code, stderr, status = deploy_and_check(compose)
    if code == 0 and all_healthy(status):
        print("✅ 部署成功！")
        break
    errors = stderr

version: '3.8'

services:
  mongodb:
    image: mongo:7.0
    container_name: todo-mongodb
    restart: unless-stopped
    environment:
      MONGO_INITDB_ROOT_USERNAME: ${MONGO_USER}
      MONGO_INITDB_ROOT_PASSWORD: ${MONGO_PASS}
      MONGO_INITDB_DATABASE: ${MONGO_DB:-todoapp}
    volumes:
      - mongo_data:/data/db
      - mongo_config:/data/configdb
    networks:
      - backend
    healthcheck:
      test: echo 'db.runCommand("ping").ok' | mongosh --quiet
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s
    deploy:
      resources:
        limits:
          cpus: '1.0'
          memory: 512M
        reservations:
          cpus: '0.25'
          memory: 256M

  app:
    build:
      context: .
      dockerfile: Dockerfile
    container_name: todo-app
    restart: unless-stopped
    depends_on:
      mongodb:
        condition: service_healthy
    environment:
      NODE_ENV: production
      MONGODB_URI: mongodb://${MONGO_USER}:${MONGO_PASS}@mongodb:27017/${MONGO_DB:-todoapp}?authSource=admin
      PORT: 3000
    networks:
      - backend
    healthcheck:
      test: ["CMD", "wget", "--spider", "http://localhost:3000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 15s
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 256M

  nginx:
    image: nginx:1.25-alpine
    container_name: todo-nginx
    restart: unless-stopped
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./nginx.conf:/etc/nginx/conf.d/default.conf:ro
      - ./ssl:/etc/nginx/ssl:ro
    depends_on:
      app:
        condition: service_healthy
    networks:
      - frontend
      - backend
    healthcheck:
      test: ["CMD", "nginx", "-t"]
      interval: 60s
      timeout: 5s
      retries: 2
    deploy:
      resources:
        limits:
          cpus: '0.3'
          memory: 128M

networks:
  frontend:
    driver: bridge
  backend:
    driver: bridge
    internal: true

volumes:
  mongo_data:
  mongo_config: