431 lines
14 KiB
Bash
Executable File
431 lines
14 KiB
Bash
Executable File
#!/bin/bash
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# =============================================================================
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# AI Agent 启动与管理脚本
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# 用法: ./start.sh [check|backend|frontend|both|docker-up|docker-down]
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# =============================================================================
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set -e
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# 颜色定义
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GREEN='\033[0;32m'
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BLUE='\033[0;34m'
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RED='\033[0;31m'
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YELLOW='\033[1;33m'
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NC='\033[0m' # No Color
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# 项目根目录
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PROJECT_DIR="/home/huang/Study/AIProject/Agent1"
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echo -e "${BLUE}========================================${NC}"
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echo -e "${BLUE} AI Agent - 个人生活助手${NC}"
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echo -e "${BLUE}========================================${NC}"
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echo ""
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# =============================================================================
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# 配置检查函数
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# =============================================================================
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check_config() {
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echo -e "${BLUE}📋 开始环境配置检查...${NC}"
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echo ""
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PASS=0
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FAIL=0
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WARN=0
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# 辅助函数
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check_pass() {
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echo -e "${GREEN}✓${NC} $1"
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((PASS++))
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}
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check_fail() {
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echo -e "${RED}✗${NC} $1"
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((FAIL++))
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}
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check_warn() {
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echo -e "${YELLOW}⚠${NC} $1"
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((WARN++))
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}
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# 1. 检查 .env 文件
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echo "🔍 检查配置文件..."
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if [ -f "$PROJECT_DIR/.env" ]; then
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check_pass ".env 文件存在"
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# 检查文件格式
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if grep -q "^EOF" .env 2>/dev/null; then
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check_fail ".env 文件格式错误：发现多余的 EOF 标记"
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else
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check_pass ".env 文件格式正确"
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fi
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else
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check_fail ".env 文件不存在"
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echo " 提示: 请创建 .env 文件并配置环境变量"
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return 1
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fi
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# 2. 检查必需的环境变量
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echo ""
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echo "🔑 检查环境变量..."
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# 检查 ZHIPUAI_API_KEY
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if grep -q "^ZHIPUAI_API_KEY=" "$PROJECT_DIR/.env" 2>/dev/null; then
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API_KEY=$(grep "^ZHIPUAI_API_KEY=" "$PROJECT_DIR/.env" | head -1 | cut -d'=' -f2- | tr -d '[:space:]')
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if [ ${#API_KEY} -gt 10 ]; then
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check_pass "ZHIPUAI_API_KEY 已配置（长度: ${#API_KEY}）"
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else
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check_fail "ZHIPUAI_API_KEY 配置可能无效（过短）"
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fi
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else
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check_fail "ZHIPUAI_API_KEY 未配置或格式错误"
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fi
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# 检查 VLLM_LOCAL_KEY
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if grep -q "^VLLM_LOCAL_KEY=" "$PROJECT_DIR/.env" 2>/dev/null; then
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check_pass "VLLM_LOCAL_KEY 已配置"
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else
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check_warn "VLLM_LOCAL_KEY 未配置（如不使用本地模型可忽略）"
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fi
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# 检查 DB_URI (远程服务器)
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if grep -q "^DB_URI=.*115.190.121.151" "$PROJECT_DIR/.env" 2>/dev/null; then
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check_pass "DB_URI 已配置（远程服务器）"
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elif grep -q "^DB_URI=" "$PROJECT_DIR/.env" 2>/dev/null; then
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check_warn "DB_URI 已配置（非远程服务器地址）"
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else
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check_fail "DB_URI 未配置"
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fi
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# 检查 QDRANT_URL (远程服务器)
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if grep -q "^QDRANT_URL=.*115.190.121.151" "$PROJECT_DIR/.env" 2>/dev/null; then
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check_pass "QDRANT_URL 已配置（远程服务器）"
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elif grep -q "^QDRANT_URL=" "$PROJECT_DIR/.env" 2>/dev/null; then
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check_warn "QDRANT_URL 已配置（非远程服务器地址）"
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else
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check_fail "QDRANT_URL 未配置"
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fi
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# 3. 检查 Docker 环境
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echo ""
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echo "🐳 检查 Docker 环境..."
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if command -v docker &> /dev/null; then
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check_pass "Docker 已安装"
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if docker info &> /dev/null; then
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check_pass "Docker 守护进程正在运行"
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else
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check_fail "Docker 守护进程未运行"
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echo " 提示: sudo systemctl start docker"
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fi
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else
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check_fail "Docker 未安装"
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fi
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if command -v docker compose version &> /dev/null || command -v docker-compose &> /dev/null; then
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check_pass "Docker Compose 已安装"
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else
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check_fail "Docker Compose 未安装"
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fi
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# 4. 检查端口占用
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echo ""
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echo "🔌 检查端口占用..."
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for port in 8001 8501; do
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if lsof -i :$port &> /dev/null; then
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check_warn "端口 $port 已被占用"
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else
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check_pass "端口 $port 可用"
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fi
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done
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# 5. 检查远程服务连接
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echo ""
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echo "🌐 检查远程服务连接..."
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# 测试 PostgreSQL 连接
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if command -v psql &> /dev/null; then
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# 注意：这里假设密码为 mysecretpassword，如果不同需调整或从 env 读取
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if PGPASSWORD=mysecretpassword psql -h 115.190.121.151 -U postgres -d langgraph_db -c "SELECT 1;" &> /dev/null; then
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check_pass "PostgreSQL 远程连接正常 (115.190.121.151:5432)"
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else
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check_fail "PostgreSQL 远程连接失败"
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echo " 提示: 检查网络连接和防火墙设置"
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fi
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else
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check_warn "psql 客户端未安装，跳过 PostgreSQL 连接测试"
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fi
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# 测试 Qdrant 连接
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if curl -s http://115.190.121.151:6333/collections &> /dev/null; then
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check_pass "Qdrant 远程连接正常 (115.190.121.151:6333)"
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else
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check_fail "Qdrant 远程连接失败"
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echo " 提示: 检查网络连接和防火墙设置"
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fi
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# 总结
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echo ""
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echo "=========================================="
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echo " 检查结果汇总"
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echo "=========================================="
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echo -e "${GREEN}通过: $PASS${NC}"
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echo -e "${RED}失败: $FAIL${NC}"
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echo -e "${YELLOW}警告: $WARN${NC}"
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echo ""
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if [ $FAIL -eq 0 ]; then
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echo -e "${GREEN}✅ 配置检查通过！${NC}"
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return 0
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else
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echo -e "${RED}❌ 发现 $FAIL 个错误，请修复后重试${NC}"
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return 1
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fi
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}
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# =============================================================================
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# Docker 容器检查函数（仅检查 llama.cpp 服务）
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# =============================================================================
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check_llamacpp() {
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echo -e "${BLUE}🔍 检查 llama.cpp LLM 容器...${NC}"
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if ! docker ps --format '{{.Names}}' | grep -q "^gemma4-llamacpp-server$"; then
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echo -e "${YELLOW}⚠️ llama.cpp LLM 容器未运行${NC}"
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return 1
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else
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echo -e "${GREEN}✓ llama.cpp LLM 容器正在运行 (gemma4-llamacpp-server)${NC}"
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return 0
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fi
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}
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check_embedding() {
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echo -e "${BLUE}🔍 检查 llama.cpp Embedding 容器...${NC}"
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if ! docker ps --format '{{.Names}}' | grep -q "^embedding-server$"; then
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echo -e "${YELLOW}⚠️ llama.cpp Embedding 容器未运行${NC}"
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return 1
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else
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echo -e "${GREEN}✓ llama.cpp Embedding 容器正在运行 (embedding-server)${NC}"
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return 0
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fi
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}
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# =============================================================================
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# 启动 Docker 依赖服务（llama.cpp）
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# =============================================================================
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start_llamacpp() {
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echo -e "${BLUE}🚀 启动 llama.cpp LLM 容器...${NC}"
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# 检查模型文件
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if [ ! -f "/home/huang/Study/AIModel/GGUF/Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q6_K_P.gguf" ]; then
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echo -e "${RED}✗ 错误：LLM 模型文件不存在${NC}"
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exit 1
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fi
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if [ ! -f "/home/huang/Study/AIModel/GGUF/mmproj-Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-f16.gguf" ]; then
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echo -e "${RED}✗ 错误：多模态投影文件不存在${NC}"
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exit 1
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fi
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docker run -d \
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--name gemma4-llamacpp-server \
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--restart=unless-stopped \
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--group-add=video \
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--device=/dev/kfd \
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--device=/dev/dri \
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-v /home/huang/Study/AIModel/GGUF:/models \
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-p 8081:8080 \
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ghcr.io/ggml-org/llama.cpp:server-rocm \
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-m /models/Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-Q6_K_P.gguf \
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--mmproj /models/mmproj-Gemma-4-E2B-Uncensored-HauhauCS-Aggressive-f16.gguf \
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--host 0.0.0.0 \
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--port 8080 \
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-ngl 99
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echo -e "${GREEN}✓ llama.cpp LLM 容器已启动 (端口 8081)${NC}"
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echo -e "${YELLOW}⏳ 等待模型加载（可能需要几分钟）...${NC}"
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sleep 15
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}
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start_embedding() {
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echo -e "${BLUE}🚀 启动 llama.cpp Embedding 容器...${NC}"
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# 检查模型文件
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if [ ! -f "/home/huang/Study/AIModel/GGUF/embeddinggemma-300M-Q8_0.gguf" ]; then
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echo -e "${RED}✗ 错误：Embedding 模型文件不存在${NC}"
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exit 1
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fi
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docker run -d \
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--name embedding-server \
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--restart=unless-stopped \
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--group-add=video \
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--device=/dev/kfd \
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--device=/dev/dri \
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-v /home/huang/Study/AIModel/GGUF:/models \
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-p 8082:8080 \
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ghcr.io/ggml-org/llama.cpp:server-rocm \
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-m /models/embeddinggemma-300M-Q8_0.gguf \
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--host 0.0.0.0 \
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--port 8080 \
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-ngl 99 \
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--embeddings \
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-c 512
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echo -e "${GREEN}✓ llama.cpp Embedding 容器已启动 (端口 8082)${NC}"
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sleep 5
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}
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# =============================================================================
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# 启动 Python 服务
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# =============================================================================
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start_backend() {
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echo -e "\n${BLUE}🚀 启动后端服务 (端口 8003)...${NC}"
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cd "$PROJECT_DIR"
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# 加载 .env 文件中的环境变量
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set -a
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source .env 2>/dev/null || true
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set +a
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export PYTHONPATH="$PROJECT_DIR"
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export BACKEND_PORT=8003
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python app/backend.py &
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BACKEND_PID=$!
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echo -e "${GREEN}✓ 后端服务已启动 (PID: $BACKEND_PID)${NC}"
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sleep 2
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}
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start_frontend() {
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echo -e "\n${BLUE}🎨 启动前端界面 (端口 8501)...${NC}"
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cd "$PROJECT_DIR"
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# 加载 .env 文件中的环境变量
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set -a
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source .env 2>/dev/null || true
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set +a
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export PYTHONPATH="$PROJECT_DIR"
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streamlit run frontend/frontend.py &
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FRONTEND_PID=$!
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echo -e "${GREEN}✓ 前端服务已启动 (PID: $FRONTEND_PID)${NC}"
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echo -e "${GREEN}✓ 访问地址:${NC}"
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echo -e " 本地开发: http://localhost:8501"
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}
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# =============================================================================
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# Docker Compose 管理
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# =============================================================================
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docker_up() {
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echo -e "${BLUE}🐳 使用 Docker Compose 启动所有服务...${NC}"
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cd "$PROJECT_DIR/docker"
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# 检查 .env 文件
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if [ ! -f "../.env" ]; then
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echo -e "${RED}✗ 错误：.env 文件不存在${NC}"
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echo " 请先复制配置文件:"
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echo " cp .env.docker .env # 服务器部署"
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exit 1
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fi
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docker compose up -d --build
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echo -e "\n${GREEN}✓ Docker Compose 服务已启动${NC}"
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echo -e "${BLUE}📊 查看服务状态:${NC} docker compose ps"
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echo -e "${BLUE}📝 查看日志:${NC} docker compose logs -f"
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echo -e "${BLUE}🌐 访问应用:${NC} http://localhost:8501"
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}
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docker_down() {
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echo -e "${BLUE}🛑 停止 Docker Compose 服务...${NC}"
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cd "$PROJECT_DIR/docker"
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docker compose down
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echo -e "${GREEN}✓ 服务已停止${NC}"
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}
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# =============================================================================
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# 清理函数
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# =============================================================================
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cleanup() {
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echo -e "\n${RED}🛑 正在停止 Python 服务...${NC}"
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if [ ! -z "$BACKEND_PID" ]; then
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kill $BACKEND_PID 2>/dev/null || true
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echo -e "${GREEN}✓ 后端服务已停止${NC}"
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fi
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if [ ! -z "$FRONTEND_PID" ]; then
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kill $FRONTEND_PID 2>/dev/null || true
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echo -e "${GREEN}✓ 前端服务已停止${NC}"
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fi
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echo -e "${YELLOW}💡 提示：Docker 容器需要手动停止${NC}"
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echo -e " 停止 llama.cpp LLM: docker stop gemma4-llamacpp-server"
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echo -e " 停止 llama.cpp Embedding: docker stop embedding-server"
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echo -e " 或使用: $0 docker-down"
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exit 0
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}
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# 捕获 Ctrl+C
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trap cleanup SIGINT SIGTERM
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# =============================================================================
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# 主逻辑
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# =============================================================================
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case "${1:-help}" in
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check)
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check_config
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;;
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backend)
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check_config || exit 1
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check_llamacpp || start_llamacpp
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check_embedding || start_embedding
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start_backend
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echo -e "\n${GREEN}后端服务正在运行，按 Ctrl+C 停止${NC}"
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wait $BACKEND_PID
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;;
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frontend)
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check_config || exit 1
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start_frontend
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echo -e "\n${GREEN}前端服务正在运行，按 Ctrl+C 停止${NC}"
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wait $FRONTEND_PID
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;;
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both)
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check_config || exit 1
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check_llamacpp || start_llamacpp
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check_embedding || start_embedding
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start_backend
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start_frontend
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echo -e "\n${GREEN}所有服务正在运行，按 Ctrl+C 停止 Python 服务${NC}"
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echo -e "${YELLOW}注意：Docker 容器会在后台继续运行${NC}"
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wait
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;;
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docker-up)
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check_config || exit 1
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docker_up
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;;
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docker-down)
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docker_down
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;;
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help|*)
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echo -e "${BLUE}用法:${NC} $0 [command]"
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echo ""
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echo -e "${BLUE}命令:${NC}"
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echo " check 检查环境配置"
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echo " backend 仅启动后端服务"
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echo " frontend 仅启动前端服务"
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echo " both 启动前后端服务（默认）"
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echo " docker-up 使用 Docker Compose 启动所有服务"
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echo " docker-down 停止 Docker Compose 服务"
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echo " help 显示此帮助信息"
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echo ""
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echo -e "${BLUE}示例:${NC}"
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echo " $0 check # 检查配置"
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echo " $0 both # 启动本地开发环境"
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echo " $0 docker-up # 启动 Docker 部署环境"
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;;
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esac
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