feat: add Docker support for offline deployment with qwen3:14b

Major additions: - All-in-One Docker image with Ollama + models bundled - Separate deployment option for existing Ollama installations - Changed default model from qwen3:8b to qwen3:14b - Comprehensive deployment documentation Files added: - Dockerfile: Basic app-only image - Dockerfile.allinone: Complete image with Ollama + models - docker-compose.yml: Easy deployment configuration - docker-entrypoint.sh: Startup script for all-in-one image - requirements.txt: Python dependencies - .dockerignore: Exclude unnecessary files from image Scripts: - export-ollama-models.sh: Export models from local Ollama - build-allinone.sh: Build complete offline-deployable image - build-and-export.sh: Build and export basic image Documentation: - DEPLOYMENT.md: Comprehensive deployment guide - QUICK_START.md: Quick reference for common tasks Configuration: - Updated config.py: DEFAULT_CHAT_MODEL = qwen3:14b - Updated frontend/opro.html: Page title to 系统提示词优化
2025-12-08 10:10:38 +08:00
parent 65cdcf29dc
commit 26f8e0c648
13 changed files with 897 additions and 3 deletions
--- a/.dockerignore
+++ b/.dockerignore
@@ -0,0 +1,26 @@
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+*.so
+*.egg
+*.egg-info
+dist
+build
+.git
+.gitignore
+.vscode
+.idea
+*.md
+!README.md
+local_docs
+examples
+outputs
+.DS_Store
+*.log
+.env
+.venv
+venv
+env
+
--- a/DEPLOYMENT.md
+++ b/DEPLOYMENT.md
@@ -0,0 +1,358 @@
+# Docker 部署指南
+
+本文档说明如何在无外网访问的服务器上部署系统提示词优化工具。
+
+## 部署方案
+
+本项目提供两种部署方案：
+
+### 方案 A: All-in-One 镜像（推荐，适用于无外网服务器）
+
+**优点**：
+- 包含所有依赖：应用代码 + Ollama + LLM 模型
+- 一个镜像文件，部署简单
+- 无需在目标服务器上安装任何额外软件（除了 Docker）
+
+**缺点**：
+- 镜像文件很大（10-20GB）
+- 传输时间较长
+
+### 方案 B: 分离部署（适用于已有 Ollama 的服务器）
+
+**优点**：
+- 镜像文件较小（~500MB）
+- 可以复用现有的 Ollama 服务
+
+**缺点**：
+- 需要在目标服务器上单独安装和配置 Ollama
+- 需要手动下载模型
+
+---
+
+## 方案 A: All-in-One 部署（推荐）
+
+### 前置要求
+
+#### 在开发机器上（有外网访问）
+
+1. **Docker** 已安装
+2. **Ollama** 已安装并运行
+3. **磁盘空间**：至少 30GB 可用空间
+4. 已下载所需的 Ollama 模型：
+   - `qwen3:14b` (主模型，~8GB)
+   - `qwen3-embedding:4b` (嵌入模型，~2GB)
+
+#### 在目标服务器上（无外网访问）
+
+1. **Docker** 已安装
+2. **磁盘空间**：至少 25GB 可用空间
+
+### 部署步骤
+
+#### 步骤 1: 下载所需的 Ollama 模型
+
+在开发机器上，确保已下载所需模型：
+
+```bash
+# 下载主模型（约 8GB）
+ollama pull qwen3:14b
+
+# 下载嵌入模型（约 2GB）
+ollama pull qwen3-embedding:4b
+
+# 验证模型已下载
+ollama list
+```
+
+#### 步骤 2: 导出 Ollama 模型
+
+```bash
+# 运行导出脚本
+./export-ollama-models.sh
+```
+
+这将创建 `ollama-models/` 目录，包含所有模型文件。
+
+#### 步骤 3: 构建 All-in-One Docker 镜像
+
+```bash
+# 运行构建脚本（推荐）
+./build-allinone.sh
+
+# 或手动构建
+docker build -f Dockerfile.allinone -t system-prompt-optimizer:allinone .
+```
+
+**注意**：构建过程可能需要 10-30 分钟，取决于机器性能。
+
+#### 步骤 4: 导出 Docker 镜像
+
+如果使用 `build-allinone.sh`，镜像已自动导出。否则手动导出：
+
+```bash
+# 导出镜像（约 10-20GB）
+docker save -o system-prompt-optimizer-allinone.tar system-prompt-optimizer:allinone
+
+# 验证文件大小
+ls -lh system-prompt-optimizer-allinone.tar
+```
+
+#### 步骤 5: 传输到目标服务器
+
+使用 scp、U盘或其他方式传输镜像文件：
+
+```bash
+# 使用 scp（如果网络可达）
+scp system-prompt-optimizer-allinone.tar user@server:/path/
+
+# 或使用 rsync（支持断点续传）
+rsync -avP --progress system-prompt-optimizer-allinone.tar user@server:/path/
+
+# 或使用 U盘/移动硬盘物理传输
+```
+
+#### 步骤 6: 在目标服务器上加载镜像
+
+```bash
+# 加载镜像（需要几分钟）
+docker load -i system-prompt-optimizer-allinone.tar
+
+# 验证镜像已加载
+docker images | grep system-prompt-optimizer
+```
+
+#### 步骤 7: 启动服务
+
+```bash
+# 启动容器
+docker run -d \
+  --name system-prompt-optimizer \
+  -p 8010:8010 \
+  -p 11434:11434 \
+  -v $(pwd)/outputs:/app/outputs \
+  --restart unless-stopped \
+  system-prompt-optimizer:allinone
+
+# 查看启动日志
+docker logs -f system-prompt-optimizer
+```
+
+**重要**：首次启动需要等待 30-60 秒，Ollama 服务需要初始化。
+
+#### 步骤 8: 验证部署
+
+```bash
+# 等待服务启动（约 30-60 秒）
+sleep 60
+
+# 健康检查
+curl http://localhost:8010/health
+
+# 应该返回：
+# {"status":"ok","version":"0.1.0"}
+
+# 检查 Ollama 服务
+curl http://localhost:11434/api/tags
+
+# 检查可用模型
+curl http://localhost:8010/models
+
+# 访问 Web 界面
+# 浏览器打开: http://<服务器IP>:8010/ui/opro.html
+```
+
+---
+
+## 方案 B: 分离部署
+
+### 前置要求
+
+#### 在目标服务器上
+
+1. **Docker** 已安装
+2. **Ollama** 服务已安装并运行
+3. 已拉取所需的 Ollama 模型：
+   - `qwen3:14b` (主模型)
+   - `qwen3-embedding:4b` (嵌入模型)
+
+### 部署步骤
+
+#### 步骤 1: 构建应用镜像
+
+```bash
+# 在开发机器上构建
+docker build -t system-prompt-optimizer:latest .
+
+# 导出镜像
+docker save -o system-prompt-optimizer.tar system-prompt-optimizer:latest
+```
+
+#### 步骤 2: 传输并加载
+
+```bash
+# 传输到目标服务器
+scp system-prompt-optimizer.tar user@server:/path/
+
+# 在目标服务器上加载
+docker load -i system-prompt-optimizer.tar
+```
+
+#### 步骤 3: 启动服务
+
+```bash
+# 使用 Docker Compose
+docker-compose up -d
+
+# 或使用 Docker 命令
+docker run -d \
+  --name system-prompt-optimizer \
+  -p 8010:8010 \
+  -e OLLAMA_HOST=http://host.docker.internal:11434 \
+  -v $(pwd)/outputs:/app/outputs \
+  --add-host host.docker.internal:host-gateway \
+  --restart unless-stopped \
+  system-prompt-optimizer:latest
+```
+
+## 配置说明
+
+### 环境变量
+
+在 `docker-compose.yml` 或 `docker run` 命令中可以配置以下环境变量：
+
+- `OLLAMA_HOST`: Ollama 服务地址（默认: `http://host.docker.internal:11434`）
+- `PYTHONUNBUFFERED`: Python 输出缓冲（默认: `1`）
+
+### 端口映射
+
+- **8010**: Web 界面和 API 端口
+
+### 数据持久化
+
+- `./outputs`: 用户反馈日志存储目录（映射到容器内 `/app/outputs`）
+
+## 故障排查
+
+### 1. 无法连接 Ollama 服务
+
+**问题**: 容器内无法访问宿主机的 Ollama 服务
+
+**解决方案**:
+```bash
+# 确保使用了 --add-host 参数
+--add-host host.docker.internal:host-gateway
+
+# 或者直接使用宿主机 IP
+-e OLLAMA_HOST=http://192.168.1.100:11434
+```
+
+### 2. 模型不可用（All-in-One 部署）
+
+**问题**: 容器内模型未正确加载
+
+**解决方案**:
+```bash
+# 进入容器检查
+docker exec -it system-prompt-optimizer bash
+
+# 在容器内检查模型
+ollama list
+
+# 如果模型不存在，检查模型目录
+ls -la /root/.ollama/models/
+
+# 退出容器
+exit
+```
+
+如果模型确实丢失，可能需要重新构建镜像。
+
+### 3. 模型不可用（分离部署）
+
+**问题**: Ollama 模型未安装
+
+**解决方案**:
+```bash
+# 在宿主机上拉取模型
+ollama pull qwen3:14b
+ollama pull qwen3-embedding:4b
+
+# 验证模型已安装
+ollama list
+```
+
+### 4. 容器启动失败
+
+**问题**: 端口被占用或权限问题
+
+**解决方案**:
+```bash
+# 检查端口占用
+netstat -tulpn | grep 8010
+netstat -tulpn | grep 11434
+
+# 更换端口（All-in-One 需要两个端口）
+docker run -p 8011:8010 -p 11435:11434 ...
+
+# 查看容器日志
+docker logs system-prompt-optimizer
+```
+
+### 5. 性能问题
+
+**问题**: 生成速度慢
+
+**解决方案**:
+- 确保 Ollama 使用 GPU 加速
+- 使用更小的模型（如 `qwen3:4b`）
+- 调整 `config.py` 中的 `GENERATION_POOL_SIZE`
+
+## 更新部署
+
+```bash
+# 1. 在开发机器上重新构建镜像
+docker build -t system-prompt-optimizer:latest .
+
+# 2. 导出新镜像
+docker save -o system-prompt-optimizer-new.tar system-prompt-optimizer:latest
+
+# 3. 传输到服务器并加载
+docker load -i system-prompt-optimizer-new.tar
+
+# 4. 重启服务
+docker-compose down
+docker-compose up -d
+
+# 或使用 docker 命令
+docker stop system-prompt-optimizer
+docker rm system-prompt-optimizer
+docker run -d ...  # 使用相同的启动命令
+```
+
+## 安全建议
+
+1. **网络隔离**: 如果不需要外部访问，只绑定到 localhost
+   ```bash
+   -p 127.0.0.1:8010:8010
+   ```
+
+2. **防火墙**: 配置防火墙规则限制访问
+   ```bash
+   # 只允许特定 IP 访问
+   iptables -A INPUT -p tcp --dport 8010 -s 192.168.1.0/24 -j ACCEPT
+   iptables -A INPUT -p tcp --dport 8010 -j DROP
+   ```
+
+3. **日志管理**: 定期清理日志文件
+   ```bash
+   # 限制 Docker 日志大小
+   docker run --log-opt max-size=10m --log-opt max-file=3 ...
+   ```
+
+## 联系支持
+
+如有问题，请查看：
+- 应用日志: `docker logs system-prompt-optimizer`
+- Ollama 日志: `journalctl -u ollama -f`
+- API 文档: http://localhost:8010/docs
+
--- a/38
+++ b/38
@@ -0,0 +1,38 @@
+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements file
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY _qwen_xinference_demo/ ./_qwen_xinference_demo/
+COPY frontend/ ./frontend/
+COPY config.py .
+
+# Create outputs directory
+RUN mkdir -p outputs
+
+# Expose port
+EXPOSE 8010
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+ENV OLLAMA_HOST=http://host.docker.internal:11434
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+    CMD curl -f http://localhost:8010/health || exit 1
+
+# Run the application
+CMD ["uvicorn", "_qwen_xinference_demo.api:app", "--host", "0.0.0.0", "--port", "8010"]
+
--- a/Dockerfile.allinone
+++ b/Dockerfile.allinone
@@ -0,0 +1,50 @@
+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies including curl for Ollama
+RUN apt-get update && apt-get install -y \
+    curl \
+    ca-certificates \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Ollama
+RUN curl -fsSL https://ollama.com/install.sh | sh
+
+# Copy requirements file
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY _qwen_xinference_demo/ ./_qwen_xinference_demo/
+COPY frontend/ ./frontend/
+COPY config.py .
+
+# Create necessary directories
+RUN mkdir -p outputs /root/.ollama
+
+# Copy pre-downloaded Ollama models
+# This includes qwen3:14b and qwen3-embedding:4b
+COPY ollama-models/ /root/.ollama/
+
+# Expose ports
+EXPOSE 8010 11434
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+ENV OLLAMA_HOST=http://localhost:11434
+
+# Copy startup script
+COPY docker-entrypoint.sh /docker-entrypoint.sh
+RUN chmod +x /docker-entrypoint.sh
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \
+    CMD curl -f http://localhost:8010/health && curl -f http://localhost:11434/api/tags || exit 1
+
+# Run the startup script
+ENTRYPOINT ["/docker-entrypoint.sh"]
+
--- a/QUICK_START.md
+++ b/QUICK_START.md
@@ -0,0 +1,117 @@
+# 快速开始指南
+
+## 离线部署（All-in-One 方案）
+
+### 在开发机器上（有外网）
+
+```bash
+# 1. 下载模型
+ollama pull qwen3:14b
+ollama pull qwen3-embedding:4b
+
+# 2. 导出模型
+./export-ollama-models.sh
+
+# 3. 构建并导出 Docker 镜像
+./build-allinone.sh
+
+# 4. 传输到目标服务器
+# 文件: system-prompt-optimizer-allinone.tar (约 10-20GB)
+scp system-prompt-optimizer-allinone.tar user@server:/path/
+```
+
+### 在目标服务器上（无外网）
+
+```bash
+# 1. 加载镜像
+docker load -i system-prompt-optimizer-allinone.tar
+
+# 2. 启动服务
+docker run -d \
+  --name system-prompt-optimizer \
+  -p 8010:8010 \
+  -p 11434:11434 \
+  -v $(pwd)/outputs:/app/outputs \
+  --restart unless-stopped \
+  system-prompt-optimizer:allinone
+
+# 3. 等待启动（约 60 秒）
+sleep 60
+
+# 4. 验证
+curl http://localhost:8010/health
+curl http://localhost:11434/api/tags
+
+# 5. 访问界面
+# http://<服务器IP>:8010/ui/opro.html
+```
+
+## 常用命令
+
+```bash
+# 查看日志
+docker logs -f system-prompt-optimizer
+
+# 重启服务
+docker restart system-prompt-optimizer
+
+# 停止服务
+docker stop system-prompt-optimizer
+
+# 删除容器
+docker rm -f system-prompt-optimizer
+
+# 进入容器
+docker exec -it system-prompt-optimizer bash
+
+# 检查模型
+docker exec -it system-prompt-optimizer ollama list
+```
+
+## 端口说明
+
+- **8010**: Web 界面和 API
+- **11434**: Ollama 服务（仅 All-in-One 方案需要暴露）
+
+## 文件说明
+
+- `system-prompt-optimizer-allinone.tar`: 完整镜像（10-20GB）
+- `outputs/`: 用户反馈日志目录
+
+## 故障排查
+
+### 服务无法启动
+
+```bash
+# 查看日志
+docker logs system-prompt-optimizer
+
+# 检查端口占用
+netstat -tulpn | grep 8010
+netstat -tulpn | grep 11434
+```
+
+### 模型不可用
+
+```bash
+# 进入容器检查
+docker exec -it system-prompt-optimizer ollama list
+
+# 应该看到:
+# qwen3:14b
+# qwen3-embedding:4b
+```
+
+### 性能慢
+
+- 确保服务器有足够的 RAM（建议 16GB+）
+- 如果有 GPU，使用支持 GPU 的 Docker 运行时
+- 调整 `config.py` 中的 `GENERATION_POOL_SIZE`
+
+## 更多信息
+
+详细文档请参考：
+- `DEPLOYMENT.md`: 完整部署指南
+- `README.md`: 项目说明
+- http://localhost:8010/docs: API 文档
+
--- a/build-allinone.sh
+++ b/build-allinone.sh
@@ -0,0 +1,98 @@
+#!/bin/bash
+
+# Build all-in-one Docker image with Ollama and models
+# This creates a complete offline-deployable image
+
+set -e
+
+IMAGE_NAME="system-prompt-optimizer"
+IMAGE_TAG="allinone"
+EXPORT_FILE="${IMAGE_NAME}-${IMAGE_TAG}.tar"
+
+echo "=========================================="
+echo "Building All-in-One Docker Image"
+echo "=========================================="
+echo ""
+echo "This will create a Docker image containing:"
+echo "  - Python application"
+echo "  - Ollama service"
+echo "  - qwen3:14b model"
+echo "  - qwen3-embedding:4b model"
+echo ""
+echo "WARNING: The final image will be 10-20GB in size!"
+echo ""
+
+# Check if ollama-models directory exists
+if [ ! -d "ollama-models" ]; then
+    echo "ERROR: ollama-models directory not found!"
+    echo ""
+    echo "Please run ./export-ollama-models.sh first to export the models."
+    exit 1
+fi
+
+echo "✓ Found ollama-models directory"
+echo ""
+
+# Check disk space
+AVAILABLE_SPACE=$(df -h . | awk 'NR==2 {print $4}')
+echo "Available disk space: $AVAILABLE_SPACE"
+echo "Required: ~20GB for build process"
+echo ""
+
+read -p "Continue with build? (y/n) " -n 1 -r
+echo
+if [[ ! $REPLY =~ ^[Yy]$ ]]; then
+    echo "Build cancelled."
+    exit 1
+fi
+
+echo ""
+echo "=========================================="
+echo "Building Docker image..."
+echo "=========================================="
+docker build -f Dockerfile.allinone -t ${IMAGE_NAME}:${IMAGE_TAG} .
+
+echo ""
+echo "=========================================="
+echo "Build complete!"
+echo "=========================================="
+docker images | grep ${IMAGE_NAME}
+
+echo ""
+echo "=========================================="
+echo "Exporting image to ${EXPORT_FILE}..."
+echo "=========================================="
+echo "This will take several minutes..."
+docker save -o ${EXPORT_FILE} ${IMAGE_NAME}:${IMAGE_TAG}
+
+echo ""
+echo "=========================================="
+echo "Export complete!"
+echo "=========================================="
+ls -lh ${EXPORT_FILE}
+
+echo ""
+echo "=========================================="
+echo "Deployment Instructions"
+echo "=========================================="
+echo ""
+echo "1. Transfer ${EXPORT_FILE} to target server:"
+echo "   scp ${EXPORT_FILE} user@server:/path/"
+echo ""
+echo "2. On target server, load the image:"
+echo "   docker load -i ${EXPORT_FILE}"
+echo ""
+echo "3. Run the container:"
+echo "   docker run -d \\"
+echo "     --name system-prompt-optimizer \\"
+echo "     -p 8010:8010 \\"
+echo "     -p 11434:11434 \\"
+echo "     -v \$(pwd)/outputs:/app/outputs \\"
+echo "     --restart unless-stopped \\"
+echo "     ${IMAGE_NAME}:${IMAGE_TAG}"
+echo ""
+echo "4. Access the application:"
+echo "   http://<server-ip>:8010/ui/opro.html"
+echo ""
+echo "See DEPLOYMENT.md for more details."
+
--- a/build-and-export.sh
+++ b/build-and-export.sh
@@ -0,0 +1,37 @@
+#!/bin/bash
+
+# Build and export Docker image for offline deployment
+# Usage: ./build-and-export.sh
+
+set -e
+
+IMAGE_NAME="system-prompt-optimizer"
+IMAGE_TAG="latest"
+EXPORT_FILE="${IMAGE_NAME}.tar"
+
+echo "=========================================="
+echo "Building Docker image..."
+echo "=========================================="
+docker build -t ${IMAGE_NAME}:${IMAGE_TAG} .
+
+echo ""
+echo "=========================================="
+echo "Exporting Docker image to ${EXPORT_FILE}..."
+echo "=========================================="
+docker save -o ${EXPORT_FILE} ${IMAGE_NAME}:${IMAGE_TAG}
+
+echo ""
+echo "=========================================="
+echo "Export complete!"
+echo "=========================================="
+ls -lh ${EXPORT_FILE}
+
+echo ""
+echo "Next steps:"
+echo "1. Transfer ${EXPORT_FILE} to target server"
+echo "2. Transfer docker-compose.yml to target server (optional)"
+echo "3. On target server, run: docker load -i ${EXPORT_FILE}"
+echo "4. On target server, run: docker-compose up -d"
+echo ""
+echo "See DEPLOYMENT.md for detailed instructions."
+
--- a/config.py
+++ b/config.py
@@ -7,7 +7,7 @@ APP_CONTACT = {"name": "OPRO Team", "url": "http://127.0.0.1:8010/ui/"}
 OLLAMA_HOST = "http://127.0.0.1:11434"
 OLLAMA_GENERATE_URL = f"{OLLAMA_HOST}/api/generate"
 OLLAMA_TAGS_URL = f"{OLLAMA_HOST}/api/tags"
-DEFAULT_CHAT_MODEL = "qwen3:8b"
+DEFAULT_CHAT_MODEL = "qwen3:14b"
 DEFAULT_EMBED_MODEL = "qwen3-embedding:4b"

 # Xinference
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -0,0 +1,23 @@
+version: '3.8'
+
+services:
+  app:
+    build: .
+    container_name: system-prompt-optimizer
+    ports:
+      - "8010:8010"
+    environment:
+      - OLLAMA_HOST=http://host.docker.internal:11434
+      - PYTHONUNBUFFERED=1
+    volumes:
+      - ./outputs:/app/outputs
+    restart: unless-stopped
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8010/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 5s
+
--- a/docker-entrypoint.sh
+++ b/docker-entrypoint.sh
@@ -0,0 +1,35 @@
+#!/bin/bash
+
+set -e
+
+echo "Starting Ollama service..."
+ollama serve &
+
+# Wait for Ollama to be ready
+echo "Waiting for Ollama to start..."
+for i in {1..30}; do
+    if curl -s http://localhost:11434/api/tags > /dev/null 2>&1; then
+        echo "Ollama is ready!"
+        break
+    fi
+    echo "Waiting for Ollama... ($i/30)"
+    sleep 2
+done
+
+# Check if models exist, if not, show warning
+echo "Checking for models..."
+ollama list
+
+if ! ollama list | grep -q "qwen3:14b"; then
+    echo "WARNING: qwen3:14b model not found!"
+    echo "The application requires qwen3:14b to function properly."
+fi
+
+if ! ollama list | grep -q "qwen3-embedding"; then
+    echo "WARNING: qwen3-embedding model not found!"
+    echo "The application requires qwen3-embedding:4b for embeddings."
+fi
+
+echo "Starting FastAPI application..."
+exec uvicorn _qwen_xinference_demo.api:app --host 0.0.0.0 --port 8010
+
--- a/export-ollama-models.sh
+++ b/export-ollama-models.sh
@@ -0,0 +1,105 @@
+#!/bin/bash
+
+# Export Ollama models for offline deployment
+# This script copies Ollama models from your local machine
+# so they can be bundled into the Docker image
+#
+# Required models:
+# - qwen3:14b (main chat model)
+# - qwen3-embedding:4b (embedding model)
+
+set -e
+
+MODELS_DIR="ollama-models"
+OLLAMA_MODELS_PATH="$HOME/.ollama"
+
+echo "=========================================="
+echo "Exporting Ollama models for offline deployment"
+echo "=========================================="
+
+# Check if Ollama is installed
+if ! command -v ollama &> /dev/null; then
+    echo "ERROR: Ollama is not installed or not in PATH"
+    exit 1
+fi
+
+# Check if required models are available
+echo ""
+echo "Checking for required models..."
+MISSING_MODELS=0
+
+if ! ollama list | grep -q "qwen3:14b"; then
+    echo "ERROR: qwen3:14b model not found!"
+    echo "Please run: ollama pull qwen3:14b"
+    MISSING_MODELS=1
+fi
+
+if ! ollama list | grep -q "qwen3-embedding:4b"; then
+    echo "ERROR: qwen3-embedding:4b model not found!"
+    echo "Please run: ollama pull qwen3-embedding:4b"
+    MISSING_MODELS=1
+fi
+
+if [ $MISSING_MODELS -eq 1 ]; then
+    echo ""
+    echo "Please download the required models first:"
+    echo "  ollama pull qwen3:14b"
+    echo "  ollama pull qwen3-embedding:4b"
+    exit 1
+fi
+
+echo "✓ All required models found"
+
+# Check if Ollama directory exists
+if [ ! -d "$OLLAMA_MODELS_PATH" ]; then
+    echo "ERROR: Ollama directory not found at $OLLAMA_MODELS_PATH"
+    exit 1
+fi
+
+# Create export directory
+echo ""
+echo "Creating export directory: $MODELS_DIR"
+rm -rf "$MODELS_DIR"
+mkdir -p "$MODELS_DIR"
+
+echo ""
+echo "Copying Ollama data from $OLLAMA_MODELS_PATH to $MODELS_DIR..."
+echo "This may take several minutes (models are large)..."
+
+# Copy the entire .ollama directory structure
+cp -r "$OLLAMA_MODELS_PATH"/* "$MODELS_DIR/"
+
+echo ""
+echo "=========================================="
+echo "Models exported successfully!"
+echo "=========================================="
+du -sh "$MODELS_DIR"
+
+echo ""
+echo "Directory structure:"
+ls -lh "$MODELS_DIR/"
+
+echo ""
+echo "Models included:"
+if [ -d "$MODELS_DIR/models/manifests/registry.ollama.ai/library" ]; then
+    ls -lh "$MODELS_DIR/models/manifests/registry.ollama.ai/library/"
+fi
+
+echo ""
+echo "=========================================="
+echo "Next steps:"
+echo "=========================================="
+echo "1. Build the all-in-one Docker image:"
+echo "   ./build-allinone.sh"
+echo ""
+echo "2. Or manually:"
+echo "   docker build -f Dockerfile.allinone -t system-prompt-optimizer:allinone ."
+echo ""
+echo "3. Export the image:"
+echo "   docker save -o system-prompt-optimizer-allinone.tar system-prompt-optimizer:allinone"
+echo ""
+echo "4. Transfer to target server:"
+echo "   scp system-prompt-optimizer-allinone.tar user@server:/path/"
+echo ""
+echo "Note: The final Docker image will be very large (10-20GB) due to the models."
+
--- a/frontend/opro.html
+++ b/frontend/opro.html
@@ -6,7 +6,7 @@
    <meta http-equiv="Cache-Control" content="no-cache, no-store, must-revalidate">
    <meta http-equiv="Pragma" content="no-cache">
    <meta http-equiv="Expires" content="0">
-    <title>OPRO - System Instruction Optimizer</title>
+    <title>系统提示词优化</title>
    <script crossorigin src="https://unpkg.com/react@18/umd/react.production.min.js"></script>
    <script crossorigin src="https://unpkg.com/react-dom@18/umd/react-dom.production.min.js"></script>
    <script src="https://cdn.tailwindcss.com"></script>
@@ -509,7 +509,7 @@
                    // Header
                    React.createElement('div', { className: 'px-4 py-3 border-b border-gray-200 bg-white flex items-center gap-3' },
                        React.createElement('h1', { className: 'text-lg font-normal text-gray-800' },
-                            'OPRO'
+                            '系统提示词优化'
                        ),
                        currentSessionId && React.createElement('div', { className: 'text-sm text-gray-500' },
                            sessions.find(s => s.session_id === currentSessionId)?.session_name || '当前会话'
--- a/requirements.txt
+++ b/requirements.txt
@@ -0,0 +1,7 @@
+fastapi==0.109.0
+uvicorn==0.27.0
+requests==2.31.0
+numpy==1.26.3
+scikit-learn==1.4.0
+pydantic==2.5.3
+