huangfu
a6c2027caa
- 添加 start_all.sh 一键启动脚本，支持启动llama-server和FastAPI服务 - 修改启动脚本使用venv虚拟环境替代conda环境 - 更新README.md，添加一键启动脚本使用说明 - 更新py_tree_generator.py，添加final_prompt返回字段 - 禁用Qwen3模型的思考功能 - 添加RAG检索结果的终端打印 - 移除ROS2相关代码（ros2_client.py已删除）
407 lines
14 KiB
Bash
Executable File
407 lines
14 KiB
Bash
Executable File
#!/bin/bash
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# ==============================================================================
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# 无人机自然语言控制项目 - 一键启动脚本
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# ==============================================================================
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# 功能：启动所有必需的服务（llama-server推理模型、embedding模型、FastAPI后端）
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# 用法：./start_all.sh [选项]
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# ==============================================================================
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set -e # 遇到错误立即退出
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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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# 默认配置（可通过环境变量覆盖）
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PROJECT_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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LLAMA_SERVER_DIR="${LLAMA_SERVER_DIR:-~/llama.cpp/build/bin}"
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INFERENCE_MODEL="${INFERENCE_MODEL:-~/models/gguf/Qwen/Qwen3-4B/Qwen3-4B-Q5_K_M.gguf}"
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EMBEDDING_MODEL="${EMBEDDING_MODEL:-~/models/gguf/Qwen/Qwen3-Embedding-4B/Qwen3-Embedding-4B-Q4_K_M.gguf}"
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VENV_PATH="${VENV_PATH:-${PROJECT_ROOT}/backend_service/venv}"
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LOG_DIR="${PROJECT_ROOT}/logs"
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PID_FILE="${LOG_DIR}/services.pid"
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# 端口配置
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INFERENCE_PORT=8081
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EMBEDDING_PORT=8090
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API_PORT=8000
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# 创建日志目录
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mkdir -p "${LOG_DIR}"
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# ==============================================================================
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# 辅助函数
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# ==============================================================================
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print_info() {
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echo -e "${BLUE}[INFO]${NC} $1"
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}
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print_success() {
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echo -e "${GREEN}[SUCCESS]${NC} $1"
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}
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print_warning() {
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echo -e "${YELLOW}[WARNING]${NC} $1"
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}
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print_error() {
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echo -e "${RED}[ERROR]${NC} $1"
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}
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# 检查命令是否存在
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check_command() {
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if ! command -v "$1" &> /dev/null; then
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print_error "$1 命令未找到，请先安装"
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return 1
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fi
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return 0
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}
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# 检查端口是否被占用
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check_port() {
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local port=$1
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if lsof -Pi :${port} -sTCP:LISTEN -t >/dev/null 2>&1 ; then
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return 0 # 端口被占用
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else
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return 1 # 端口空闲
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fi
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}
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# 等待服务就绪
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wait_for_service() {
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local url=$1
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local service_name=$2
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local max_attempts=30
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local attempt=0
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print_info "等待 ${service_name} 启动..."
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while [ $attempt -lt $max_attempts ]; do
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if curl -s "${url}" > /dev/null 2>&1; then
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print_success "${service_name} 已就绪"
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return 0
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fi
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attempt=$((attempt + 1))
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sleep 1
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done
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print_error "${service_name} 启动超时"
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return 1
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}
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# 停止所有服务
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stop_services() {
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print_info "正在停止所有服务..."
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if [ -f "${PID_FILE}" ]; then
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while read pid; do
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if ps -p $pid > /dev/null 2>&1; then
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print_info "停止进程 PID: $pid"
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kill $pid 2>/dev/null || true
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fi
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done < "${PID_FILE}"
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rm -f "${PID_FILE}"
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fi
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# 尝试通过端口停止服务
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for port in ${INFERENCE_PORT} ${EMBEDDING_PORT} ${API_PORT}; do
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if check_port ${port}; then
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local pid=$(lsof -ti:${port})
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if [ ! -z "$pid" ]; then
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print_info "停止占用端口 ${port} 的进程 (PID: $pid)"
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kill $pid 2>/dev/null || true
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fi
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fi
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done
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print_success "所有服务已停止"
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}
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# 清理函数（脚本退出时调用）
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cleanup() {
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if [ "$?" -ne 0 ]; then
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print_error "启动过程中发生错误，正在清理..."
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fi
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# 注意：这里不自动停止服务，让用户手动控制
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}
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trap cleanup EXIT
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# ==============================================================================
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# 主函数
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# ==============================================================================
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start_services() {
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print_info "=========================================="
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print_info " 无人机自然语言控制项目 - 服务启动"
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print_info "=========================================="
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echo ""
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# 检查必要的命令
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print_info "检查必要的命令..."
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check_command "python3" || exit 1
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check_command "curl" || exit 1
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check_command "lsof" || print_warning "lsof 未安装，将无法检查端口占用"
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echo ""
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# 检查端口占用
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print_info "检查端口占用..."
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if check_port ${INFERENCE_PORT}; then
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print_warning "端口 ${INFERENCE_PORT} 已被占用，推理模型可能已在运行"
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fi
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if check_port ${EMBEDDING_PORT}; then
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print_warning "端口 ${EMBEDDING_PORT} 已被占用，Embedding模型可能已在运行"
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fi
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if check_port ${API_PORT}; then
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print_error "端口 ${API_PORT} 已被占用，请先停止占用该端口的服务"
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exit 1
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fi
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echo ""
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# 检查llama-server（展开路径中的 ~）
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local llama_server_dir_expanded=$(eval echo "${LLAMA_SERVER_DIR}")
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local llama_server="${llama_server_dir_expanded}/llama-server"
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if [ ! -f "${llama_server}" ]; then
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print_error "llama-server 未找到: ${llama_server}"
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print_info "请设置 LLAMA_SERVER_DIR 环境变量指向正确的路径"
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print_info "当前路径: ${LLAMA_SERVER_DIR}"
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print_info "展开后路径: ${llama_server_dir_expanded}"
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exit 1
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fi
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print_success "找到 llama-server: ${llama_server}"
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echo ""
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# 检查模型文件
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local inference_model_expanded=$(eval echo "${INFERENCE_MODEL}")
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local embedding_model_expanded=$(eval echo "${EMBEDDING_MODEL}")
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if [ ! -f "${inference_model_expanded}" ]; then
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print_error "推理模型文件未找到: ${inference_model_expanded}"
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print_info "请设置 INFERENCE_MODEL 环境变量指向正确的模型路径"
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exit 1
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fi
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print_success "找到推理模型: ${inference_model_expanded}"
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if [ ! -f "${embedding_model_expanded}" ]; then
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print_error "Embedding模型文件未找到: ${embedding_model_expanded}"
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print_info "请设置 EMBEDDING_MODEL 环境变量指向正确的模型路径"
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exit 1
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fi
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print_success "找到Embedding模型: ${embedding_model_expanded}"
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echo ""
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# 检查ROS2环境
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local ros2_setup="${PROJECT_ROOT}/install/setup.bash"
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if [ ! -f "${ros2_setup}" ]; then
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print_warning "ROS2 setup文件未找到: ${ros2_setup}"
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print_warning "如果项目已与ROS2解耦，可以忽略此警告"
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else
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print_success "找到ROS2 setup文件: ${ros2_setup}"
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fi
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echo ""
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# 检查venv虚拟环境
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local venv_path_expanded=$(eval echo "${VENV_PATH}")
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print_info "检查venv虚拟环境: ${venv_path_expanded}"
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if [ ! -d "${venv_path_expanded}" ]; then
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print_error "venv虚拟环境目录不存在: ${venv_path_expanded}"
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print_info "请先创建venv环境: python3 -m venv ${venv_path_expanded}"
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print_info "然后安装依赖: ${venv_path_expanded}/bin/pip install -r backend_service/requirements.txt"
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exit 1
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fi
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if [ ! -f "${venv_path_expanded}/bin/activate" ]; then
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print_error "venv激活脚本不存在: ${venv_path_expanded}/bin/activate"
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print_error "这看起来不是一个有效的venv环境"
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exit 1
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fi
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print_success "venv虚拟环境存在: ${venv_path_expanded}"
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echo ""
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# 初始化PID文件
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> "${PID_FILE}"
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# ==========================================================================
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# 启动推理模型服务
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# ==========================================================================
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print_info "启动推理模型服务 (端口 ${INFERENCE_PORT})..."
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cd "${llama_server_dir_expanded}"
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nohup ./llama-server \
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-m "${inference_model_expanded}" \
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--port ${INFERENCE_PORT} \
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--gpu-layers 36 \
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--host 0.0.0.0 \
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-c 8192 \
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> "${LOG_DIR}/inference_model.log" 2>&1 &
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local inference_pid=$!
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echo $inference_pid >> "${PID_FILE}"
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print_success "推理模型服务已启动 (PID: $inference_pid)"
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print_info "日志文件: ${LOG_DIR}/inference_model.log"
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echo ""
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# ==========================================================================
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# 启动Embedding模型服务
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# ==========================================================================
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print_info "启动Embedding模型服务 (端口 ${EMBEDDING_PORT})..."
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nohup ./llama-server \
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-m "${embedding_model_expanded}" \
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--gpu-layers 36 \
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--port ${EMBEDDING_PORT} \
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--embeddings \
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--pooling last \
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--host 0.0.0.0 \
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> "${LOG_DIR}/embedding_model.log" 2>&1 &
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local embedding_pid=$!
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echo $embedding_pid >> "${PID_FILE}"
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print_success "Embedding模型服务已启动 (PID: $embedding_pid)"
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print_info "日志文件: ${LOG_DIR}/embedding_model.log"
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echo ""
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# ==========================================================================
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# 等待模型服务就绪
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# ==========================================================================
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print_info "等待模型服务就绪..."
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sleep 3 # 给服务一些启动时间
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# 等待推理模型服务
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if ! wait_for_service "http://localhost:${INFERENCE_PORT}/health" "推理模型服务"; then
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# 如果health端点不存在，尝试检查根路径
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if ! wait_for_service "http://localhost:${INFERENCE_PORT}/v1/models" "推理模型服务"; then
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print_warning "推理模型服务可能未完全就绪，但将继续启动"
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fi
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fi
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# 等待Embedding模型服务
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if ! wait_for_service "http://localhost:${EMBEDDING_PORT}/health" "Embedding模型服务"; then
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if ! wait_for_service "http://localhost:${EMBEDDING_PORT}/v1/models" "Embedding模型服务"; then
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print_warning "Embedding模型服务可能未完全就绪，但将继续启动"
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fi
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fi
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echo ""
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# ==========================================================================
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# 启动FastAPI后端服务
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# ==========================================================================
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print_info "启动FastAPI后端服务 (端口 ${API_PORT})..."
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cd "${PROJECT_ROOT}"
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# 激活venv虚拟环境并启动FastAPI服务
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# 使用bash -c来在新的shell中激活venv环境
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bash -c "
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# 激活ROS2环境（如果存在）
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if [ -f '${ros2_setup}' ]; then
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source '${ros2_setup}'
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fi
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# 激活venv虚拟环境
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source '${venv_path_expanded}/bin/activate' && \
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cd '${PROJECT_ROOT}/backend_service' && \
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uvicorn src.main:app --host 0.0.0.0 --port ${API_PORT}
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" > "${LOG_DIR}/fastapi.log" 2>&1 &
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local api_pid=$!
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echo $api_pid >> "${PID_FILE}"
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print_success "FastAPI服务已启动 (PID: $api_pid)"
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print_info "日志文件: ${LOG_DIR}/fastapi.log"
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echo ""
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# 等待FastAPI服务就绪
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sleep 3
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if wait_for_service "http://localhost:${API_PORT}/docs" "FastAPI服务"; then
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print_success "所有服务已成功启动！"
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else
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print_warning "FastAPI服务可能未完全就绪，请检查日志: ${LOG_DIR}/fastapi.log"
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fi
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echo ""
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# 显示服务访问信息
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print_info "=========================================="
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print_info " 服务启动完成！"
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print_info "=========================================="
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print_info "推理模型API: http://localhost:${INFERENCE_PORT}/v1"
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print_info "Embedding模型API: http://localhost:${EMBEDDING_PORT}/v1"
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print_info "FastAPI后端: http://localhost:${API_PORT}"
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print_info "API文档: http://localhost:${API_PORT}/docs"
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print_info ""
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print_info "日志文件位置:"
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print_info " - 推理模型: ${LOG_DIR}/inference_model.log"
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print_info " - Embedding模型: ${LOG_DIR}/embedding_model.log"
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print_info " - FastAPI服务: ${LOG_DIR}/fastapi.log"
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print_info ""
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print_info "按 Ctrl+C 停止所有服务"
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print_info "=========================================="
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echo ""
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# 设置信号处理，确保Ctrl+C时能清理
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trap 'print_info "\n正在停止服务..."; stop_services; exit 0' INT TERM
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# 等待所有后台进程（保持脚本运行）
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print_info "所有服务正在运行中，查看日志请使用:"
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print_info " tail -f ${LOG_DIR}/*.log"
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echo ""
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# 等待所有后台进程
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wait
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}
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# ==============================================================================
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# 脚本入口
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# ==============================================================================
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case "${1:-start}" in
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start)
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start_services
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;;
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stop)
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stop_services
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;;
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restart)
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stop_services
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sleep 2
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start_services
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;;
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status)
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print_info "检查服务状态..."
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if [ -f "${PID_FILE}" ]; then
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print_info "已记录的服务进程:"
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while read pid; do
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if ps -p $pid > /dev/null 2>&1; then
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print_success "PID $pid: 运行中"
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else
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print_warning "PID $pid: 已停止"
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fi
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done < "${PID_FILE}"
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else
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print_info "未找到PID文件，服务可能未启动"
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fi
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echo ""
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print_info "端口占用情况:"
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for port in ${INFERENCE_PORT} ${EMBEDDING_PORT} ${API_PORT}; do
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if check_port ${port}; then
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local pid=$(lsof -ti:${port})
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print_success "端口 ${port}: 被占用 (PID: $pid)"
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else
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print_warning "端口 ${port}: 空闲"
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fi
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done
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;;
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*)
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echo "用法: $0 {start|stop|restart|status}"
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echo ""
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echo "命令说明:"
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echo " start - 启动所有服务（默认）"
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echo " stop - 停止所有服务"
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echo " restart - 重启所有服务"
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echo " status - 查看服务状态"
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echo ""
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echo "环境变量配置:"
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echo " LLAMA_SERVER_DIR - llama-server所在目录 (默认: ~/llama.cpp/build/bin)"
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echo " INFERENCE_MODEL - 推理模型路径 (默认: ~/models/gguf/Qwen/Qwen3-4B/Qwen3-4B-Q5_K_M.gguf)"
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echo " EMBEDDING_MODEL - Embedding模型路径 (默认: ~/models/gguf/Qwen/Qwen3-Embedding-4B/Qwen3-Embedding-4B-Q4_K_M.gguf)"
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echo " VENV_PATH - venv虚拟环境路径 (默认: \${PROJECT_ROOT}/backend_service/venv)"
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exit 1
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;;
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esac
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