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65 lines
2.3 KiB
Python
65 lines
2.3 KiB
Python
from sqlalchemy import create_engine, Column, Integer, String, Text, DateTime, ForeignKey, LargeBinary
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from sqlalchemy.orm import declarative_base, sessionmaker, relationship
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from datetime import datetime
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Base = declarative_base()
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class KnowledgeBase(Base):
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__tablename__ = 'kb'
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id = Column(Integer, primary_key=True, index=True)
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name = Column(String, index=True)
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description = Column(Text)
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created_at = Column(DateTime, default=datetime.utcnow)
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embedding_model = Column(String, default='') # 默认嵌入模型
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top_k = Column(Integer, default=5) # 默认返回的top_k数量
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files = relationship('File', back_populates='knowledge_base')
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class File(Base):
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__tablename__ = 'file'
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id = Column(Integer, primary_key=True, index=True)
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kb_id = Column(Integer, ForeignKey('kb.id'))
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file_name = Column(String)
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path = Column(String)
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created_at = Column(DateTime, default=datetime.utcnow)
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file_type = Column(String)
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status = Column(Integer, default=0) # 0: 未处理, 1: 处理中, 2: 已处理, 3: 错误
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knowledge_base = relationship('KnowledgeBase', back_populates='files')
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chunks = relationship('Chunk', back_populates='file')
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class Chunk(Base):
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__tablename__ = 'chunks'
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id = Column(Integer, primary_key=True, index=True)
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file_id = Column(Integer, ForeignKey('file.id'))
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text = Column(Text)
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file = relationship('File', back_populates='chunks')
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vector = relationship('Vector', uselist=False, back_populates='chunk') # One-to-one
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class Vector(Base):
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__tablename__ = 'vectors'
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id = Column(Integer, primary_key=True, index=True)
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chunk_id = Column(Integer, ForeignKey('chunks.id'), unique=True)
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embedding = Column(LargeBinary) # Store embeddings as binary
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chunk = relationship('Chunk', back_populates='vector')
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# 数据库连接
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DATABASE_URL = 'sqlite:///./knowledge_base.db' # 生产环境请更换为 PostgreSQL/MySQL
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engine = create_engine(DATABASE_URL, connect_args={'check_same_thread': False} if 'sqlite' in DATABASE_URL else {})
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SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
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# 创建所有表 (可以在应用启动时执行一次)
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def create_db_and_tables():
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Base.metadata.create_all(bind=engine)
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print('Database tables created/checked.')
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# 定义嵌入维度（请根据你实际使用的模型调整）
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EMBEDDING_DIM = 1024
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