DataMate/runtime/datamate-python/app/core/config.py

from pydantic_settings import BaseSettings
from pydantic import SecretStr, model_validator
from typing import Optional
import logging
import os

_logger = logging.getLogger(__name__)

# 已知的弱默认凭据，生产环境禁止使用
_BLOCKED_DEFAULT_PASSWORDS = {"password", "123456", "admin", "root", "datamate123"}
_BLOCKED_DEFAULT_TOKENS = {"abc123abc123", "EMPTY"}


class Settings(BaseSettings):
    """应用程序配置"""

    class Config:
        env_file = ".env"
        case_sensitive = False
        extra = 'ignore'

    # Service
    app_name: str = "DataMate Python Backend"
    app_version: str = "1.0.0"
    app_description: str = "Adapter for integrating Data Management System with Label Studio"

    host: str = "0.0.0.0"
    port: int = 18000

    # CORS
    # allowed_origins: List[str] = ["*"]
    # allowed_methods: List[str] = ["*"]
    # allowed_headers: List[str] = ["*"]

    # Log
    log_level: str = "INFO"
    debug: bool = True
    log_file_dir: str = "/var/log/datamate/backend-python"

    # Database
    mysql_host: str = "datamate-database"
    mysql_port: int = 3306
    mysql_user: str = "root"
    mysql_password: str = "password"
    mysql_database: str = "datamate"

    database_url: str = ""  # Will be overridden by build_database_url() if not provided

    @model_validator(mode='after')
    def build_database_url(self):
        """如果没有提供 database_url，则根据 MySQL 配置构建"""
        if not self.database_url:
            if self.mysql_password and self.mysql_user:
                self.database_url = f"mysql+aiomysql://{self.mysql_user}:{self.mysql_password}@{self.mysql_host}:{self.mysql_port}/{self.mysql_database}"
            else:
                self.database_url = f"mysql+aiomysql://{self.mysql_host}:{self.mysql_port}/{self.mysql_database}"
        return self


    # Label Studio
    label_studio_base_url: str = "http://label-studio:8000"
    label_studio_username: Optional[str] = "admin@demo.com"
    label_studio_password: Optional[str] = "demoadmin"
    label_studio_user_token: Optional[str] = "abc123abc123"  # Legacy Token

    label_studio_local_document_root: str = "/label-studio/local"  # Label Studio local file storage path
    label_studio_file_path_prefix: str = "/data/local-files/?d="  # Label Studio local file serving URL prefix

    ls_task_page_size: int = 1000

    # DataMate
    dm_file_path_prefix: str = "/dataset"  # DM存储文件夹前缀

    # DataMate Backend (Java) - 用于通过"下载/预览接口"读取文件内容
    datamate_backend_base_url: str = "http://datamate-backend:8080/api"

    # Knowledge Graph - LLM 三元组抽取配置
    kg_llm_api_key: SecretStr = SecretStr("EMPTY")
    kg_llm_base_url: Optional[str] = None
    kg_llm_model: str = "gpt-4o-mini"
    kg_llm_temperature: float = 0.0
    kg_llm_timeout_seconds: int = 60
    kg_llm_max_retries: int = 2

    # Knowledge Graph - 实体对齐配置
    kg_alignment_enabled: bool = False
    kg_alignment_embedding_model: str = "text-embedding-3-small"
    kg_alignment_vector_threshold: float = 0.92
    kg_alignment_llm_threshold: float = 0.78

    # GraphRAG 融合查询配置
    graphrag_enabled: bool = False
    graphrag_milvus_uri: str = "http://milvus-standalone:19530"
    graphrag_kg_service_url: str = "http://datamate-backend:8080"
    graphrag_kg_internal_token: str = ""

    # GraphRAG - 检索策略默认值
    graphrag_vector_top_k: int = 5
    graphrag_graph_depth: int = 2
    graphrag_graph_max_entities: int = 20
    graphrag_vector_weight: float = 0.6
    graphrag_graph_weight: float = 0.4

    # GraphRAG - LLM（空则复用 kg_llm_* 配置）
    graphrag_llm_model: str = ""
    graphrag_llm_base_url: Optional[str] = None
    graphrag_llm_api_key: SecretStr = SecretStr("EMPTY")
    graphrag_llm_temperature: float = 0.1
    graphrag_llm_timeout_seconds: int = 60

    # GraphRAG - Embedding（空则复用 kg_alignment_embedding_* 配置）
    graphrag_embedding_model: str = ""

    # GraphRAG - 缓存配置
    graphrag_cache_enabled: bool = True
    graphrag_cache_kg_maxsize: int = 256
    graphrag_cache_kg_ttl: int = 300
    graphrag_cache_embedding_maxsize: int = 512
    graphrag_cache_embedding_ttl: int = 600

    # 标注编辑器（Label Studio Editor）相关
    editor_max_text_bytes: int = 0  # <=0 表示不限制，正数为最大字节数

    @model_validator(mode='after')
    def check_default_credentials(self):
        """生产环境下检测弱默认凭据，拒绝启动。

        通过环境变量 DATAMATE_ENV 判断环境：
        - dev/test/local: 仅发出警告
        - 其他（prod/staging 等）: 抛出异常阻止启动
        """
        env = os.environ.get("DATAMATE_ENV", "dev").lower()
        is_dev = env in ("dev", "test", "local", "development")
        issues: list[str] = []

        if self.mysql_password in _BLOCKED_DEFAULT_PASSWORDS:
            issues.append(f"mysql_password is set to a weak default ('{self.mysql_password}')")

        if self.label_studio_password and self.label_studio_password in _BLOCKED_DEFAULT_PASSWORDS:
            issues.append("label_studio_password is set to a weak default")

        if self.label_studio_user_token and self.label_studio_user_token in _BLOCKED_DEFAULT_TOKENS:
            issues.append("label_studio_user_token is set to a weak default")

        if issues:
            msg = "SECURITY: Weak default credentials detected: " + "; ".join(issues)
            if is_dev:
                _logger.warning(msg + " (acceptable in dev/test, MUST change for production)")
            else:
                raise ValueError(
                    msg + ". Set proper credentials via environment variables "
                    "before deploying to production."
                )
        return self

# 全局设置实例
settings = Settings()