Files
LangBot/pkg/entity/persistence/rag.py
2025-07-09 22:09:46 +08:00

59 lines
1.6 KiB
Python

from sqlalchemy import create_engine, Column, Integer, String, Text, DateTime, LargeBinary
from sqlalchemy.orm import declarative_base, sessionmaker
from datetime import datetime
import os
Base = declarative_base()
DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///./rag_knowledge.db")
engine = create_engine(
DATABASE_URL,
connect_args={"check_same_thread": False}
)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
def create_db_and_tables():
"""Creates all database tables defined in the Base."""
Base.metadata.create_all(bind=engine)
print("Database tables created or already exist.")
class KnowledgeBase(Base):
__tablename__ = 'kb'
id = Column(Integer, primary_key=True, index=True)
name = Column(String, index=True)
description = Column(Text)
created_at = Column(DateTime, default=datetime.utcnow)
embedding_model = Column(String, default='')
top_k = Column(Integer, default=5)
class File(Base):
__tablename__ = 'file'
id = Column(Integer, primary_key=True, index=True)
kb_id = Column(Integer, nullable=True)
file_name = Column(String)
path = Column(String)
created_at = Column(DateTime, default=datetime.utcnow)
file_type = Column(String)
status = Column(Integer, default=0)
class Chunk(Base):
__tablename__ = 'chunks'
id = Column(Integer, primary_key=True, index=True)
file_id = Column(Integer, nullable=True)
text = Column(Text)
class Vector(Base):
__tablename__ = 'vectors'
id = Column(Integer, primary_key=True, index=True)
chunk_id = Column(Integer, nullable=True)
embedding = Column(LargeBinary)