1# Define schema
2class Doc(Base):
3    __tablename__ = "doc"
4    id = Column(Integer, primary_key=True)
5    content = Column(Text)
6    embedding = Column(VectorType(dim=3))
7
8# Create table and index
9Base.metadata.create_all(engine)
10VectorAdaptor(engine).create_vector_index(
11    Doc.embedding, tidb_vector.DistanceMetric.L2
12)
13
14# Insert
15with Session(engine) as session:
16    session.add(Doc(content="dog",  embedding=[1, 2, 1]))
17    session.add(Doc(content="fish", embedding=[1, 2, 4]))
18    session.commit()
19
20# Search nearest 1 embedding using L2 distance
21with Session(engine) as session:
22    results = session.execute(
23        select(Doc.content)
24        .order_by(Doc.embedding.l2_distance([1, 2, 3]))
25        .limit(1)
26    ).all()
27    print(results)