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fix: 修复Docker部署问题
- 修复JVM参数格式错误，移除有问题的日志格式配置 - 创建docker-build目录解决.dockerignore排除问题 - 更新健康检查端点为/api/v1/status - 添加Docker部署脚本(deploy-docker.sh) - 创建简单的docker-compose配置 - 清理.env.docker文件，移除JVM参数 - 添加Dockerfile.simple作为备用方案
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‎.env.docker‎

Lines changed: 6 additions & 31 deletions
Original file line numberDiff line numberDiff line change
@@ -1,34 +1,9 @@
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# Docker部署环境变量配置
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# 复制此文件为.env并在部署前修改
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# ========== 应用配置 ==========
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# 主机端口映射
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HOST_PORT=8080
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# AI服务配置
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AI_API_KEY=your_deepseek_api_key_here
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AI_MODEL=deepseek-chat
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# Docker环境配置
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AI_API_KEY=sk-1899062c03f640f090129c4692ccc26f
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AI_BASE_URL=https://api.deepseek.com
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AI_TIMEOUT=30000
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AI_MODEL=deepseek-chat
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AI_MAX_TOKENS=1000
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AI_ENABLED=true
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# ========== 数据库配置（可选） ==========
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DB_PASSWORD=ai_password
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DB_PORT=5432
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# ========== Redis配置（可选） ==========
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REDIS_PORT=6379
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# ========== 监控配置（可选） ==========
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GRAFANA_PASSWORD=admin
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# ========== 高级配置 ==========
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# JVM参数
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JAVA_OPTS=-Xmx512m -Xms256m -XX:+UseG1GC
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# 时区
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TZ=Asia/Shanghai
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AI_TIMEOUT=30000
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# 日志级别
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LOGGING_LEVEL_ROOT=INFO
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SERVER_PORT=8080
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SPRING_PROFILES_ACTIVE=docker

‎.env.docker.clean‎

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# Docker环境配置
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AI_API_KEY=sk-1899062c03f640f090129c4692ccc26f
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AI_BASE_URL=https://api.deepseek.com
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AI_MODEL=deepseek-chat
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AI_MAX_TOKENS=1000
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AI_TIMEOUT=30000
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SERVER_PORT=8080
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SPRING_PROFILES_ACTIVE=docker

‎Dockerfile‎

Lines changed: 8 additions & 34 deletions
Original file line numberDiff line numberDiff line change
@@ -1,26 +1,4 @@
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# Java AI Starter - Dockerfile
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# 多阶段构建，优化镜像大小
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# 第一阶段：构建阶段
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FROM maven:3.8.4-openjdk-17-slim AS builder
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WORKDIR /app
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# 复制Maven配置文件
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COPY pom.xml .
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COPY .mvn .mvn
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COPY mvnw .
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# 下载依赖（利用Docker缓存）
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RUN mvn dependency:go-offline -B
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# 复制源代码
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COPY src src
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# 构建应用
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RUN mvn clean package -DskipTests
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# 第二阶段：运行阶段
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# 简单的Dockerfile - 使用现有jar文件
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FROM eclipse-temurin:17-jre
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# 设置时区
@@ -32,8 +10,12 @@ RUN groupadd -r appuser && useradd -r -g appuser appuser
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WORKDIR /app
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# 从构建阶段复制jar文件
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COPY --from=builder /app/target/java-ai-starter-*.jar app.jar
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# 复制jar文件
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COPY ./target/java-ai-starter-*.jar app.jar
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# 复制启动脚本
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COPY docker-entrypoint.sh /usr/local/bin/
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RUN chmod +x /usr/local/bin/docker-entrypoint.sh
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# 创建必要的目录
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RUN mkdir -p /app/logs /app/config && \
@@ -44,19 +26,11 @@ USER appuser
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# 健康检查
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HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
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CMD curl -f http://localhost:8080/api/health || exit 1
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CMD curl -f http://localhost:8080/api/v1/status || exit 1
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# 暴露端口
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EXPOSE 8080
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# 设置环境变量
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ENV JAVA_OPTS="-Xmx512m -Xms256m -XX:+UseG1GC -XX:MaxGCPauseMillis=200"
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ENV SPRING_PROFILES_ACTIVE="docker"
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# 入口点脚本
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COPY docker-entrypoint.sh /usr/local/bin/
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RUN chmod +x /usr/local/bin/docker-entrypoint.sh
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# 入口点
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ENTRYPOINT ["docker-entrypoint.sh"]
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‎Dockerfile.simple‎

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# 简单的Dockerfile - 使用现有jar文件
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FROM eclipse-temurin:17-jre
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# 设置时区
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ENV TZ=Asia/Shanghai
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RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
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# 创建非root用户
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RUN groupadd -r appuser && useradd -r -g appuser appuser
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WORKDIR /app
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# 复制jar文件
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COPY target/java-ai-starter-*.jar app.jar
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# 复制启动脚本
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COPY docker-entrypoint.sh /usr/local/bin/
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RUN chmod +x /usr/local/bin/docker-entrypoint.sh
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# 创建必要的目录
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RUN mkdir -p /app/logs /app/config && \
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chown -R appuser:appuser /app
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# 切换到非root用户
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USER appuser
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# 健康检查
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HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
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CMD curl -f http://localhost:8080/api/v1/status || exit 1
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# 暴露端口
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EXPOSE 8080
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# 入口点
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ENTRYPOINT ["docker-entrypoint.sh"]
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# 默认命令
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CMD ["java", "-jar", "app.jar"]

‎deploy-docker.sh‎

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#!/bin/bash
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# Docker部署脚本
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set -e
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echo "🚀 Java AI Starter Docker部署"
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echo "=============================="
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# 颜色定义
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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BLUE='\033[0;34m'
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NC='\033[0m' # No Color
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# 检查Docker
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check_docker() {
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if ! command -v docker &> /dev/null; then
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echo -e "${RED}❌ Docker未安装${NC}"
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exit 1
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fi
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echo -e "${GREEN}✅ Docker已安装${NC}"
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}
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# 检查环境文件
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check_env() {
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if [ ! -f ".env.docker" ]; then
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echo -e "${YELLOW}⚠️ 未找到.env.docker文件${NC}"
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echo "创建默认环境文件..."
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cat > .env.docker << EOF
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# Docker环境配置
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AI_API_KEY=sk-1899062c03f640f090129c4692ccc26f
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AI_BASE_URL=https://api.deepseek.com
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AI_MODEL=deepseek-chat
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AI_MAX_TOKENS=1000
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AI_TIMEOUT=30000
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SERVER_PORT=8080
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SPRING_PROFILES_ACTIVE=docker
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# 数据库配置（可选）
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# DB_HOST=postgres
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# DB_PORT=5432
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# DB_PASSWORD=ai_password
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# Redis配置（可选）
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# REDIS_HOST=redis
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# REDIS_PORT=6379
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EOF
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echo -e "${GREEN}✅ 已创建.env.docker文件${NC}"
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fi
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# 加载环境变量（排除包含特殊字符的行）
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if [ -f ".env.docker" ]; then
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echo "加载环境变量..."
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# 只加载简单的KEY=VALUE行，排除包含JVM参数的行
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while IFS= read -r line; do
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# 跳过注释和空行
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[[ "$line" =~ ^#.*$ ]] && continue
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[[ -z "$line" ]] && continue
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# 跳过包含JVM参数的行
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[[ "$line" =~ .*=.*-Xmx.* ]] && continue
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[[ "$line" =~ .*=.*-Xms.* ]] && continue
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[[ "$line" =~ .*=.*-XX:.* ]] && continue
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# 导出有效的环境变量
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export "$line" 2>/dev/null || echo "跳过: $line"
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done < .env.docker
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fi
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}
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# 构建Docker镜像
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build_image() {
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echo -e "${BLUE}🔨 构建Docker镜像...${NC}"
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docker build -t java-ai-starter:latest .
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if [ $? -eq 0 ]; then
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echo -e "${GREEN}✅ Docker镜像构建成功${NC}"
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else
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echo -e "${RED}❌ Docker镜像构建失败${NC}"
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exit 1
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fi
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}
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# 运行Docker容器
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run_container() {
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echo -e "${BLUE}🐳 启动Docker容器...${NC}"
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# 停止并删除现有容器
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if docker ps -a | grep -q "java-ai-starter"; then
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echo "停止现有容器..."
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docker stop java-ai-starter 2>/dev/null || true
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docker rm java-ai-starter 2>/dev/null || true
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fi
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# 运行新容器
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docker run -d \
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--name java-ai-starter \
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--restart unless-stopped \
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-p ${SERVER_PORT:-8080}:8080 \
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-e AI_API_KEY="${AI_API_KEY}" \
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-e AI_BASE_URL="${AI_BASE_URL:-https://api.deepseek.com}" \
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-e AI_MODEL="${AI_MODEL:-deepseek-chat}" \
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-e AI_MAX_TOKENS="${AI_MAX_TOKENS:-1000}" \
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-e AI_TIMEOUT="${AI_TIMEOUT:-30000}" \
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-e SPRING_PROFILES_ACTIVE="${SPRING_PROFILES_ACTIVE:-docker}" \
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-e SERVER_PORT=8080 \
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-v $(pwd)/logs:/app/logs \
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-v $(pwd)/config:/app/config \
110+
java-ai-starter:latest
111+
112+
if [ $? -eq 0 ]; then
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echo -e "${GREEN}✅ Docker容器启动成功${NC}"
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else
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echo -e "${RED}❌ Docker容器启动失败${NC}"
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exit 1
117+
fi
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}
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# 检查容器状态
121+
check_status() {
122+
echo -e "${BLUE}🔍 检查容器状态...${NC}"
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sleep 3
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# 检查容器是否运行
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if docker ps | grep -q "java-ai-starter"; then
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echo -e "${GREEN}✅ 容器正在运行${NC}"
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else
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echo -e "${RED}❌ 容器未运行${NC}"
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docker logs java-ai-starter --tail 20
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exit 1
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fi
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# 检查健康状态
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echo "等待应用启动..."
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sleep 5
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# 测试API
140+
local max_attempts=10
141+
local attempt=1
142+
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while [ $attempt -le $max_attempts ]; do
144+
echo "尝试 $attempt/$max_attempts..."
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if curl -s http://localhost:${SERVER_PORT:-8080}/api/v1/ping > /dev/null 2>&1; then
146+
echo -e "${GREEN}✅ API响应正常${NC}"
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break
148+
fi
149+
150+
if [ $attempt -eq $max_attempts ]; then
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echo -e "${RED}❌ API未响应${NC}"
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docker logs java-ai-starter --tail 30
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exit 1
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fi
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sleep 5
157+
((attempt++))
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done
159+
}
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# 显示部署信息
162+
show_info() {
163+
local port=${SERVER_PORT:-8080}
164+
165+
echo ""
166+
echo -e "${GREEN}🎉 部署完成！${NC}"
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echo "========================"
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echo "📊 部署信息:"
169+
echo " - 容器名称: java-ai-starter"
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echo " - 主机端口: $port"
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echo " - 容器端口: 8080"
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echo " - 镜像标签: java-ai-starter:latest"
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echo ""
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echo "🌐 访问地址:"
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echo " - http://localhost:$port"
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echo ""
177+
echo "📋 测试命令:"
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echo " curl http://localhost:$port/api/v1/ping"
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echo " curl -X POST http://localhost:$port/api/v1/chat/text \\"
180+
echo " -H \"Content-Type: application/json\" \\"
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echo " -d '{\"message\":\"你好\"}'"
182+
echo ""
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echo "🔧 管理命令:"
184+
echo " # 查看日志"
185+
echo " docker logs -f java-ai-starter"
186+
echo ""
187+
echo " # 进入容器"
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echo " docker exec -it java-ai-starter sh"
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echo ""
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echo " # 停止容器"
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echo " docker stop java-ai-starter"
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echo ""
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echo " # 删除容器"
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echo " docker rm java-ai-starter"
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echo ""
196+
echo " # 删除镜像"
197+
echo " docker rmi java-ai-starter:latest"
198+
}
199+
200+
# 主函数
201+
main() {
202+
check_docker
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check_env
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build_image
205+
run_container
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check_status
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show_info
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}
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# 执行主函数
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main "$@"

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