feat: 添加 Dockerfile 和 docker-compose.yml 部署方案

.dockerignore

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+__pycache__/
+*.pyc
+.venv/
+.pytest_cache/
+.ruff_cache/
+.mypy_cache/
+.vscode/
+.git/
+.gitignore
+.env
+data/
+*.egg-info/
+dist/
+build/
+.coverage
+coverage.xml

Dockerfile

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+# Dockerfile — md-vector-db 生产镜像
+FROM python:3.13-slim-bookworm
+
+LABEL org.opencontainers.image.title="md-vector-db"
+LABEL org.opencontainers.image.description="Markdown 文档向量数据库"
+
+# 系统依赖
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# 先复制依赖文件以利用 Docker 层缓存
+COPY pyproject.toml uv.lock ./
+
+# 安装 uv 并同步依赖（CPU 模式）
+RUN pip install --no-cache-dir uv \
+    && uv sync --frozen --no-dev \
+    && uv cache clean
+
+# 复制源码和配置
+COPY config.yaml .env.example ./
+COPY src/ ./src/
+COPY scripts/ ./scripts/
+
+# 创建数据目录
+RUN mkdir -p /app/data
+
+# 暴露端口
+EXPOSE 8000
+
+# 健康检查
+HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
+    CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/api/v1/health')" || exit 1
+
+# 默认启动 HTTP 服务
+CMD ["uv", "run", "md-vector-db", "serve", "--port", "8000"]

docker-compose.yml

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+# docker-compose.yml — md-vector-db 本地开发与生产部署
+version: "3.8"
+
+services:
+  md-vector-db:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    image: md-vector-db:latest
+    container_name: md-vector-db
+    restart: unless-stopped
+    ports:
+      - "${MD_VECTOR_PORT:-8000}:8000"
+    volumes:
+      # 持久化 ChromaDB 数据
+      - ./data:/app/data
+      # 挂载配置文件
+      - ./config.yaml:/app/config.yaml:ro
+      # 挂载待入库文档目录
+      - ${MD_VECTOR_DOCS_DIR:-./md_docs}:/app/md_docs:ro
+    environment:
+      - MD_VECTOR_CONFIG=/app/config.yaml
+      - MD_VECTOR_DB_DATA_DIR=/app/data
+      - MD_VECTOR_API_KEY=${MD_VECTOR_API_KEY:-}
+      - EMBED_API_KEY=${EMBED_API_KEY:-}
+      - CORS_ORIGINS=${CORS_ORIGINS:-http://localhost:3000}
+      - MAX_REQUEST_BODY_SIZE=${MAX_REQUEST_BODY_SIZE:-10485760}
+    env_file:
+      - .env
+    healthcheck:
+      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/api/v1/health')"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 60s
+
+  # 可选：GPU 版本（需 nvidia-container-toolkit）
+  md-vector-db-gpu:
+    profiles: ["gpu"]
+    build:
+      context: .
+      dockerfile: Dockerfile
+    image: md-vector-db:latest
+    container_name: md-vector-db-gpu
+    restart: unless-stopped
+    ports:
+      - "${MD_VECTOR_PORT:-8000}:8000"
+    volumes:
+      - ./data:/app/data
+      - ./config.yaml:/app/config.yaml:ro
+      - ${MD_VECTOR_DOCS_DIR:-./md_docs}:/app/md_docs:ro
+    environment:
+      - MD_VECTOR_CONFIG=/app/config.yaml
+      - MD_VECTOR_DB_DATA_DIR=/app/data
+      - MD_VECTOR_API_KEY=${MD_VECTOR_API_KEY:-}
+      - EMBED_API_KEY=${EMBED_API_KEY:-}
+      - CORS_ORIGINS=${CORS_ORIGINS:-http://localhost:3000}
+    env_file:
+      - .env
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]