mirror of
https://github.com/langbot-app/LangBot.git
synced 2025-11-25 19:37:36 +08:00
57 lines
2.3 KiB
Python
57 lines
2.3 KiB
Python
from sqlalchemy import create_engine, Column, Integer, String, Text, DateTime, ForeignKey, LargeBinary
|
|
from sqlalchemy.orm import declarative_base, sessionmaker, relationship
|
|
from datetime import datetime
|
|
import numpy as np # 用于处理从LargeBinary转换回来的embedding
|
|
|
|
Base = declarative_base()
|
|
|
|
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)
|
|
|
|
files = relationship("File", back_populates="knowledge_base")
|
|
|
|
class File(Base):
|
|
__tablename__ = 'file'
|
|
id = Column(Integer, primary_key=True, index=True)
|
|
kb_id = Column(Integer, ForeignKey('kb.id'))
|
|
file_name = Column(String)
|
|
path = Column(String)
|
|
created_at = Column(DateTime, default=datetime.utcnow)
|
|
file_type = Column(String)
|
|
status = Column(Integer, default=0) # 0: 未处理, 1: 处理中, 2: 已处理, 3: 错误
|
|
knowledge_base = relationship("KnowledgeBase", back_populates="files")
|
|
chunks = relationship("Chunk", back_populates="file")
|
|
|
|
class Chunk(Base):
|
|
__tablename__ = 'chunks'
|
|
id = Column(Integer, primary_key=True, index=True)
|
|
file_id = Column(Integer, ForeignKey('file.id'))
|
|
text = Column(Text)
|
|
|
|
file = relationship("File", back_populates="chunks")
|
|
vector = relationship("Vector", uselist=False, back_populates="chunk") # One-to-one
|
|
|
|
class Vector(Base):
|
|
__tablename__ = 'vectors'
|
|
id = Column(Integer, primary_key=True, index=True)
|
|
chunk_id = Column(Integer, ForeignKey('chunks.id'), unique=True)
|
|
embedding = Column(LargeBinary) # Store embeddings as binary
|
|
|
|
chunk = relationship("Chunk", back_populates="vector")
|
|
|
|
# 数据库连接
|
|
DATABASE_URL = "sqlite:///./knowledge_base.db" # 生产环境请更换为 PostgreSQL/MySQL
|
|
engine = create_engine(DATABASE_URL, connect_args={"check_same_thread": False} if "sqlite" in DATABASE_URL else {})
|
|
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
|
|
|
|
# 创建所有表 (可以在应用启动时执行一次)
|
|
def create_db_and_tables():
|
|
Base.metadata.create_all(bind=engine)
|
|
print("Database tables created/checked.")
|
|
|
|
# 定义嵌入维度（请根据你实际使用的模型调整）
|
|
EMBEDDING_DIM = 1024 |