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