未来
17e78955a9
- fix: MCP插件管理接口改为后台任务，修复TimeoutError - fix: MCP连接失败后上下文清理的cancel scope错误 - feat: MCP插件后台注册添加重试机制 - fix: 限制每章自动创建伏笔数量上限 - fix: 修复JSON非法转义字符清洗 - fix: SSE流式生成添加心跳保活 - fix: 职业生成改用POST请求避免URL长度限制 - perf: 使用torch CPU版本加速Docker构建 - fix: 自动修复JSON字符串值中的裸换行符 - feat: 集成json5容错解析器
127 lines
3.8 KiB
Docker
127 lines
3.8 KiB
Docker
# 多阶段构建 Dockerfile for AI Story Creator
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# 支持多架构构建: linux/amd64, linux/arm64
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# 构建参数
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ARG USE_CN_MIRROR=false
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# 阶段1: 构建前端
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FROM node:22-alpine AS frontend-builder
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ARG USE_CN_MIRROR
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WORKDIR /frontend
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# 复制前端依赖文件
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COPY frontend/package*.json ./
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# 根据参数决定是否使用国内npm镜像
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RUN if [ "$USE_CN_MIRROR" = "true" ]; then \
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npm config set registry https://registry.npmmirror.com; \
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fi
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# 删除 package-lock.json 以避免因镜像源不一致导致的 404 错误
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RUN rm -f package-lock.json
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# 安装依赖
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RUN npm install
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# 复制前端源代码
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COPY frontend/ ./
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# 临时修改vite配置，使其输出到dist目录（而不是../backend/static）
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RUN sed -i "s|outDir: '../backend/static'|outDir: 'dist'|g" vite.config.ts
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# 构建前端
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RUN npm run build
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# 阶段2: 构建最终镜像
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FROM python:3.11-slim
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ARG USE_CN_MIRROR
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ARG TARGETPLATFORM
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ARG TARGETARCH
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# 设置工作目录
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WORKDIR /app
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# 根据参数决定是否使用国内镜像源
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RUN if [ "$USE_CN_MIRROR" = "true" ]; then \
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sed -i 's/deb.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources && \
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sed -i 's/security.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources; \
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fi
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# 安装系统依赖（添加数据库工具）
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RUN apt-get update && apt-get install -y \
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gcc \
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postgresql-client \
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netcat-traditional \
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&& rm -rf /var/lib/apt/lists/*
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# 复制后端依赖文件
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COPY backend/requirements.txt ./
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# 安装 Python 依赖
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# 先安装 torch CPU版本（~200MB vs 完整版~2GB，节省90%下载时间）
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# 对于embedding场景，CPU版本完全够用
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RUN if [ "$USE_CN_MIRROR" = "true" ]; then \
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pip install --no-cache-dir torch==2.8.0 --index-url https://mirrors.aliyun.com/pypi/simple/ --extra-index-url https://download.pytorch.org/whl/cpu && \
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pip install --no-cache-dir -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/; \
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else \
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pip install --no-cache-dir torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu && \
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pip install --no-cache-dir -r requirements.txt; \
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fi
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# 创建embedding目录
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RUN mkdir -p /app/embedding
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# 设置 Sentence-Transformers 缓存目录
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ENV SENTENCE_TRANSFORMERS_HOME=/app/embedding
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# 下载 embedding 模型（从 HuggingFace）
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# 使用 Python 脚本预下载模型，这样运行时不需要网络
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RUN python -c "\
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from sentence_transformers import SentenceTransformer; \
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import os; \
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os.environ['SENTENCE_TRANSFORMERS_HOME'] = '/app/embedding'; \
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print('Downloading sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2...'); \
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model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'); \
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print('Model downloaded successfully!'); \
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"
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# 复制后端代码（不包含embedding，因为已经下载了）
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COPY backend/ ./
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# 从前端构建阶段复制构建好的静态文件
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COPY --from=frontend-builder /frontend/dist ./static
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# 复制 Alembic 迁移配置和脚本（PostgreSQL）
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COPY backend/alembic-postgres.ini ./alembic.ini
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COPY backend/alembic/postgres ./alembic
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COPY backend/scripts/entrypoint.sh /app/entrypoint.sh
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COPY backend/scripts/migrate.py ./scripts/migrate.py
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# 赋予执行权限
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RUN chmod +x /app/entrypoint.sh
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# 创建必要的目录
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RUN mkdir -p /app/data /app/logs
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# 暴露端口
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EXPOSE 8000
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# 设置环境变量
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ENV PYTHONUNBUFFERED=1
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ENV APP_HOST=0.0.0.0
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ENV APP_PORT=8000
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# 设置运行时为离线模式（模型已在构建时下载）
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ENV TRANSFORMERS_OFFLINE=1
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ENV HF_DATASETS_OFFLINE=1
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ENV HF_HUB_OFFLINE=1
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# 健康检查
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HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
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CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')" || exit 1
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# 使用 entrypoint 脚本启动（自动执行迁移）
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ENTRYPOINT ["/app/entrypoint.sh"] |