DataOps-platform/app/config/config.py

import os
import platform

def get_environment():
    """
    获取当前运行环境
    优先级：
    1. 环境变量 FLASK_ENV
    2. 根据操作系统自动判断（Windows -> development, Linux -> production）
    """
    # 首先检查环境变量
    env = os.environ.get('FLASK_ENV')
    if env:
        return env.lower()
    
    # 根据操作系统判断
    system = platform.system().lower()
    if system == 'windows':
        return 'development'
    elif system == 'linux':
        return 'production'
    else:
        return 'development'  # 其他系统默认使用开发环境

class BaseConfig:
    """基础配置类，包含所有环境共享的配置"""
    SECRET_KEY = os.environ.get('SECRET_KEY') or 'you-will-never-guess'
    JSON_AS_ASCII = False
    JSONIFY_PRETTYPRINT_REGULAR = True
    JSON_SORT_KEYS = False
    
    # 平台特定配置
    PLATFORM = platform.system().lower()
    
    # 文件上传配置
    ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif', 'xlsx', 'xls', 'csv', 'sql', 'dll'}
    
    # 数据抽取配置
    DATA_EXTRACT_BATCH_SIZE = 1000  # 每批处理的记录数
    
    # PostgreSQL 基础配置
    SQLALCHEMY_ENGINE_OPTIONS = {
        'pool_pre_ping': True,
        'pool_recycle': 300,
        'pool_size': 10,
        'max_overflow': 20
    }
    
    # LLM基础配置
    LLM_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
    LLM_MODEL_NAME = "qwen-turbo"
    # LLM_API_KEY = os.environ.get('LLM_API_KEY', "sk-86d4622141d74e9a8d7c38ee873c4d91")
    LLM_API_KEY = os.environ.get('LLM_API_KEY', "sk-db68e37f00974031935395315bfe07f0")
    
    # Qwen VL模型配置 - 用于图片解析
    QWEN_API_KEY = os.environ.get('QWEN_API_KEY', "sk-db68e37f00974031935395315bfe07f0")
    QWEN_API_URL = "https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation"
    
    # Qwen API配置 - 用于文本生成（替代Deepseek）
    QWEN_TEXT_API_KEY = os.environ.get('QWEN_TEXT_API_KEY', "sk-db68e37f00974031935395315bfe07f0")
    QWEN_TEXT_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
    QWEN_TEXT_MODEL = "qwen-turbo"
    
    # 日志基础配置
    LOG_FORMAT = '%(asctime)s - %(levelname)s - %(filename)s - %(funcName)s - %(lineno)s - %(message)s'
    LOG_ENCODING = 'UTF-8'
    LOG_ENABLED = True
    

class DevelopmentConfig(BaseConfig):
    """Windows 开发环境配置"""
    FLASK_ENV = 'development'
    DEBUG = True
    PORT = 5500
    
    # 开发环境 MinIO 配置
    MINIO_HOST = 'localhost:9000'
    MINIO_USER = 'citu-test'
    MINIO_PASSWORD = 'citu-test'
    MINIO_SECURE = False
    MINIO_BUCKET = 'dataops-bucket'
    PREFIX = ''
    
    # 开发环境 PostgreSQL 配置
    SQLALCHEMY_DATABASE_URI = 'postgresql://postgres:postgres@localhost:5432/dataops'
    
    # 开发环境 Neo4j 配置
    NEO4J_URI = "bolt://localhost:7687"
    NEO4J_HTTP_URI = "http://localhost:7474"
    NEO4J_USER = "neo4j"
    NEO4J_PASSWORD = "Passw0rd"
    NEO4J_ENCRYPTED = False
    
    # 开发环境文件路径配置
    UPLOAD_BASE_PATH = 'C:\\tmp\\upload'
    ARCHIVE_BASE_PATH = 'C:\\tmp\\archive'
    
    # 开发环境日志配置
    LOG_LEVEL = 'DEBUG'
    LOG_FILE = 'flask_development.log'
    LOG_TO_CONSOLE = True
    
    # 开发环境 Airflow 配置
    AIRFLOW_BASE_URL = 'http://localhost:8080'
    AIRFLOW_AUTH_USER = 'admin'
    AIRFLOW_AUTH_PASSWORD = 'admin'

class ProductionConfig(BaseConfig):
    """Linux 生产环境配置"""
    FLASK_ENV = 'production'
    DEBUG = False
    PORT = 80
    
    # 生产环境 MinIO 配置
    MINIO_HOST = 'company.citupro.com:9000'
    MINIO_USER = 'citu-dataops-acc-key'
    MINIO_PASSWORD = 'citu-dataops-secret-key'
    MINIO_SECURE = False
    MINIO_BUCKET = 'dataops-bucket'
    PREFIX = ''
    
    # 生产环境 PostgreSQL 配置
    SQLALCHEMY_DATABASE_URI = os.environ.get('DATABASE_URL', 'postgresql://postgres:dataOps@192.168.3.143:5432/dataops')
    
    # 生产环境 Neo4j 配置
    NEO4J_URI = os.environ.get('NEO4J_URI', "bolt://192.168.3.143:7687")
    NEO4J_HTTP_URI = os.environ.get('NEO4J_HTTP_URI', "http://192.168.3.143:7474")
    NEO4J_USER = os.environ.get('NEO4J_USER', "neo4j")
    NEO4J_PASSWORD = os.environ.get('NEO4J_PASSWORD', "cituneo4j")
    NEO4J_ENCRYPTED = False
    
    # 生产环境文件路径配置
    UPLOAD_BASE_PATH = '/data/upload'
    ARCHIVE_BASE_PATH = '/data/archive'
    
    # 生产环境日志配置
    LOG_LEVEL = 'INFO'
    LOG_FILE = 'flask_production.log'
    LOG_TO_CONSOLE = False
    
    # 生产环境 Airflow 配置
    AIRFLOW_BASE_URL = os.environ.get('AIRFLOW_BASE_URL', 'http://192.168.3.143:8080')
    AIRFLOW_AUTH_USER = os.environ.get('AIRFLOW_AUTH_USER', 'admin')
    AIRFLOW_AUTH_PASSWORD = os.environ.get('AIRFLOW_AUTH_PASSWORD', 'admin')

# 配置字典
config = {
    'development': DevelopmentConfig,
    'production': ProductionConfig,
    'default': DevelopmentConfig
}

# 获取当前环境
current_env = get_environment()