build(deployment): 优化 Docker 部署配置
- 新增 .dockerignore 文件,排除不必要的文件和目录 - 修改 Dockerfile,使用环境变量 SERVER_PORT 替代固定端口 5000 - 更新 docker-compose.yaml,添加容器名称、调整端口映射并启用自动重启 - 优化 main.py 中的 chat_completions 函数逻辑
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.dockerignore
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5
.dockerignore
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@ -0,0 +1,5 @@
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.env.example
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.git
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.gitignore
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*.md
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images/
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@ -15,11 +15,11 @@ COPY . .
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# 暴露 Flask 应用监听的端口
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# 注意:EXPOSE 只是声明端口,不会实际发布端口,需要在运行容器时进行端口映射
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EXPOSE 5000
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EXPOSE ${SERVER_PORT}
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# 定义容器启动时执行的命令
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# 这里使用 Gunicorn 作为生产级的 WSGI 服务器,而不是 Flask 内置的开发服务器
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# 你需要先在 requirements.txt 中添加 gunicorn
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# CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:5000", "app:app"]
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# 如果只是测试或简单应用,也可以直接用 Flask 开发服务器
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CMD ["python", "main.py"]
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CMD ["python", "main.py"]
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@ -1,22 +1,11 @@
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services:
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web:
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build: ./ # 指定 Dockerfile 的构建上下文路径
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container_name: OpenDify
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ports:
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- "181:5000" # 端口映射:主机端口:容器端口
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- "${SERVER_PORT}:${SERVER_PORT}" # 端口映射:主机端口:容器端口
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restart: always
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volumes:
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- ./:/app # 挂载本地代码到容器,方便开发时修改代码立即生效
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environment: # 环境变量,例如 Flask 的开发模式
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FLASK_ENV: development
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# depends_on: # 如果有其他服务,例如数据库,可以添加依赖
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# - db
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# db:
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# image: postgres:13-alpine
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# environment:
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# POSTGRES_DB: mydatabase
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# POSTGRES_USER: user
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# POSTGRES_PASSWORD: password
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# volumes:
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# - db_data:/var/lib/postgresql/data
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# volumes: # 如果有持久化数据需求,例如数据库数据
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# db_data:
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FLASK_ENV: development
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8
main.py
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main.py
@ -517,14 +517,14 @@ def chat_completions():
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) as response:
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generate.message_id = None
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buffer = ""
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for raw_bytes in response.iter_raw():
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if not raw_bytes:
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continue
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try:
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buffer += raw_bytes.decode('utf-8')
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while '\n' in buffer:
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line, buffer = buffer.split('\n', 1)
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line = line.strip()
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@ -558,8 +558,10 @@ def chat_completions():
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# time.sleep(delay)
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if current_answer == "<think>\n\n":
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current_answer = "<think>"
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yield send_char(current_answer, message_id)
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current_answer = "\n"
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yield send_char(current_answer, message_id)
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logger.info(f"message_id: {message_id}")
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# 立即继续处理下一个请求
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continue
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