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test_sqlmodel.py
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239 lines (200 loc) · 9.61 KB
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import numpy as np
from pgvector import HalfVector, SparseVector
from pgvector.sqlalchemy import VECTOR, HALFVEC, BIT, SPARSEVEC, avg, sum
import pytest
from sqlalchemy.exc import StatementError
from sqlmodel import Field, Index, Session, SQLModel, create_engine, delete, select, text
from typing import Any, Optional
engine = create_engine('postgresql+psycopg2://localhost/pgvector_python_test')
with Session(engine) as session:
session.exec(text('CREATE EXTENSION IF NOT EXISTS vector'))
class Item(SQLModel, table=True):
__tablename__ = 'sqlmodel_item'
id: Optional[int] = Field(default=None, primary_key=True)
embedding: Optional[Any] = Field(default=None, sa_type=VECTOR(3))
half_embedding: Optional[Any] = Field(default=None, sa_type=HALFVEC(3))
binary_embedding: Optional[Any] = Field(default=None, sa_type=BIT(3))
sparse_embedding: Optional[Any] = Field(default=None, sa_type=SPARSEVEC(3))
SQLModel.metadata.drop_all(engine)
SQLModel.metadata.create_all(engine)
index = Index(
'sqlmodel_index',
Item.embedding,
postgresql_using='hnsw',
postgresql_with={'m': 16, 'ef_construction': 64},
postgresql_ops={'embedding': 'vector_l2_ops'}
)
index.create(engine)
def create_items():
with Session(engine) as session:
session.add(Item(id=1, embedding=[1, 1, 1], half_embedding=[1, 1, 1], binary_embedding='000', sparse_embedding=SparseVector([1, 1, 1])))
session.add(Item(id=2, embedding=[2, 2, 2], half_embedding=[2, 2, 2], binary_embedding='101', sparse_embedding=SparseVector([2, 2, 2])))
session.add(Item(id=3, embedding=[1, 1, 2], half_embedding=[1, 1, 2], binary_embedding='111', sparse_embedding=SparseVector([1, 1, 2])))
session.commit()
class TestSqlmodel:
def setup_method(self):
with Session(engine) as session:
session.exec(delete(Item))
session.commit()
def test_orm(self):
item = Item(embedding=[1.5, 2, 3])
item2 = Item(embedding=[4, 5, 6])
item3 = Item()
with Session(engine) as session:
session.add(item)
session.add(item2)
session.add(item3)
session.commit()
stmt = select(Item)
with Session(engine) as session:
items = session.exec(stmt).all()
assert items[0].id == 1
assert items[1].id == 2
assert items[2].id == 3
assert np.array_equal(items[0].embedding, np.array([1.5, 2, 3]))
assert items[0].embedding.dtype == np.float32
assert np.array_equal(items[1].embedding, np.array([4, 5, 6]))
assert items[1].embedding.dtype == np.float32
assert items[2].embedding is None
def test_vector(self):
with Session(engine) as session:
session.add(Item(id=1, embedding=[1, 2, 3]))
session.commit()
item = session.get(Item, 1)
assert np.array_equal(item.embedding, np.array([1, 2, 3]))
def test_vector_l2_distance(self):
create_items()
with Session(engine) as session:
items = session.exec(select(Item).order_by(Item.embedding.l2_distance([1, 1, 1])))
assert [v.id for v in items] == [1, 3, 2]
def test_vector_max_inner_product(self):
create_items()
with Session(engine) as session:
items = session.exec(select(Item).order_by(Item.embedding.max_inner_product([1, 1, 1])))
assert [v.id for v in items] == [2, 3, 1]
def test_vector_cosine_distance(self):
create_items()
with Session(engine) as session:
items = session.exec(select(Item).order_by(Item.embedding.cosine_distance([1, 1, 1])))
assert [v.id for v in items] == [1, 2, 3]
def test_vector_l1_distance(self):
create_items()
with Session(engine) as session:
items = session.exec(select(Item).order_by(Item.embedding.l1_distance([1, 1, 1])))
assert [v.id for v in items] == [1, 3, 2]
def test_halfvec(self):
with Session(engine) as session:
session.add(Item(id=1, half_embedding=[1, 2, 3]))
session.commit()
item = session.get(Item, 1)
assert item.half_embedding == HalfVector([1, 2, 3])
def test_halfvec_l2_distance(self):
create_items()
with Session(engine) as session:
items = session.exec(select(Item).order_by(Item.half_embedding.l2_distance([1, 1, 1])))
assert [v.id for v in items] == [1, 3, 2]
def test_halfvec_max_inner_product(self):
create_items()
with Session(engine) as session:
items = session.exec(select(Item).order_by(Item.half_embedding.max_inner_product([1, 1, 1])))
assert [v.id for v in items] == [2, 3, 1]
def test_halfvec_cosine_distance(self):
create_items()
with Session(engine) as session:
items = session.exec(select(Item).order_by(Item.half_embedding.cosine_distance([1, 1, 1])))
assert [v.id for v in items] == [1, 2, 3]
def test_halfvec_l1_distance(self):
create_items()
with Session(engine) as session:
items = session.exec(select(Item).order_by(Item.half_embedding.l1_distance([1, 1, 1])))
assert [v.id for v in items] == [1, 3, 2]
def test_bit(self):
with Session(engine) as session:
session.add(Item(id=1, binary_embedding='101'))
session.commit()
item = session.get(Item, 1)
assert item.binary_embedding == '101'
def test_bit_hamming_distance(self):
create_items()
with Session(engine) as session:
items = session.exec(select(Item).order_by(Item.binary_embedding.hamming_distance('101')))
assert [v.id for v in items] == [2, 3, 1]
def test_bit_jaccard_distance(self):
create_items()
with Session(engine) as session:
items = session.exec(select(Item).order_by(Item.binary_embedding.jaccard_distance('101')))
assert [v.id for v in items] == [2, 3, 1]
def test_sparsevec(self):
with Session(engine) as session:
session.add(Item(id=1, sparse_embedding=[1, 2, 3]))
session.commit()
item = session.get(Item, 1)
assert item.sparse_embedding == SparseVector([1, 2, 3])
def test_sparsevec_l2_distance(self):
create_items()
with Session(engine) as session:
items = session.exec(select(Item).order_by(Item.sparse_embedding.l2_distance([1, 1, 1])))
assert [v.id for v in items] == [1, 3, 2]
def test_sparsevec_max_inner_product(self):
create_items()
with Session(engine) as session:
items = session.exec(select(Item).order_by(Item.sparse_embedding.max_inner_product([1, 1, 1])))
assert [v.id for v in items] == [2, 3, 1]
def test_sparsevec_cosine_distance(self):
create_items()
with Session(engine) as session:
items = session.exec(select(Item).order_by(Item.sparse_embedding.cosine_distance([1, 1, 1])))
assert [v.id for v in items] == [1, 2, 3]
def test_sparsevec_l1_distance(self):
create_items()
with Session(engine) as session:
items = session.exec(select(Item).order_by(Item.sparse_embedding.l1_distance([1, 1, 1])))
assert [v.id for v in items] == [1, 3, 2]
def test_filter(self):
create_items()
with Session(engine) as session:
items = session.exec(select(Item).filter(Item.embedding.l2_distance([1, 1, 1]) < 1))
assert [v.id for v in items] == [1]
def test_select(self):
with Session(engine) as session:
session.add(Item(embedding=[2, 3, 3]))
items = session.exec(select(Item.embedding.l2_distance([1, 1, 1]))).all()
assert items[0] == 3
def test_vector_avg(self):
with Session(engine) as session:
res = session.exec(select(avg(Item.embedding))).first()
assert res is None
session.add(Item(embedding=[1, 2, 3]))
session.add(Item(embedding=[4, 5, 6]))
res = session.exec(select(avg(Item.embedding))).first()
assert np.array_equal(res, np.array([2.5, 3.5, 4.5]))
def test_vector_sum(self):
with Session(engine) as session:
res = session.exec(select(sum(Item.embedding))).first()
assert res is None
session.add(Item(embedding=[1, 2, 3]))
session.add(Item(embedding=[4, 5, 6]))
res = session.exec(select(sum(Item.embedding))).first()
assert np.array_equal(res, np.array([5, 7, 9]))
def test_halfvec_avg(self):
with Session(engine) as session:
res = session.exec(select(avg(Item.half_embedding))).first()
assert res is None
session.add(Item(half_embedding=[1, 2, 3]))
session.add(Item(half_embedding=[4, 5, 6]))
res = session.exec(select(avg(Item.half_embedding))).first()
assert res == HalfVector([2.5, 3.5, 4.5])
def test_halfvec_sum(self):
with Session(engine) as session:
res = session.exec(select(sum(Item.half_embedding))).first()
assert res is None
session.add(Item(half_embedding=[1, 2, 3]))
session.add(Item(half_embedding=[4, 5, 6]))
res = session.exec(select(sum(Item.half_embedding))).first()
assert res == HalfVector([5, 7, 9])
def test_bad_dimensions(self):
item = Item(embedding=[1, 2])
with Session(engine) as session:
session.add(item)
with pytest.raises(StatementError, match='expected 3 dimensions, not 2'):
session.commit()