42 lines
1.6 KiB
Python
42 lines
1.6 KiB
Python
from sqlalchemy import create_engine, MetaData, Table
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from pgvector.sqlalchemy import Vector
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from .config import settings
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import logging
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# 获取当前模块的专用 Logger
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# __name__ 会自动识别为 "backend.services.crawler_service" 这样的路径
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logger = logging.getLogger(__name__)
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class Database:
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"""
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数据库单例类
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负责初始化连接池并反射加载现有的表结构
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"""
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def __init__(self):
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# 1. 创建引擎
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# pool_pre_ping=True 用于解决数据库连接长时间空闲后断开的问题
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self.engine = create_engine(settings.DATABASE_URL, pool_pre_ping=True)
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# 2. 注册 pgvector 类型
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# 这是为了让 SQLAlchemy 反射机制能识别数据库中的 'vector' 类型
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self.engine.dialect.ischema_names['vector'] = Vector
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self.metadata = MetaData()
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self.tasks = None
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self.queue = None
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self.chunks = None
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self._reflect_tables()
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def _reflect_tables(self):
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"""自动从数据库加载表定义"""
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try:
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# autoload_with 会查询数据库元数据，自动填充 Column 信息
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self.tasks = Table('crawl_tasks', self.metadata, autoload_with=self.engine)
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self.queue = Table('crawl_queue', self.metadata, autoload_with=self.engine)
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self.chunks = Table('knowledge_chunks', self.metadata, autoload_with=self.engine)
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logger.info("Database tables reflected successfully.")
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except Exception as e:
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logger.error(f"Failed to reflect tables: {e}")
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# 全局数据库实例
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db = Database() |