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from math import sqrt

from peewee import Model, PostgresqlDatabase, fn

from pgvector import SparseVector, Vector

from pgvector.peewee import VectorField, HalfVectorField, FixedBitField, SparseVectorField

db = PostgresqlDatabase('pgvector_python_test')

class BaseModel(Model):

class Meta:

database = db

class Item(BaseModel):

id: int # for typing

embedding = VectorField(dimensions=3, null=True)

half_embedding = HalfVectorField(dimensions=3, null=True)

binary_embedding = FixedBitField(max_length=3, null=True)

sparse_embedding = SparseVectorField(dimensions=3, null=True)

distance: float # for typing

class Meta: # type: ignore

table_name = 'peewee_item'

Item.add_index('embedding vector_l2_ops', using='hnsw')

db.connect()

db.execute_sql('CREATE EXTENSION IF NOT EXISTS vector') # type: ignore

db.drop_tables([Item])

db.create_tables([Item])

def create_items() -> None:

Item.create(id=1, embedding=[1, 1, 1], half_embedding=[1, 1, 1], binary_embedding='000', sparse_embedding=SparseVector([1, 1, 1]))

Item.create(id=2, embedding=[2, 2, 2], half_embedding=[2, 2, 2], binary_embedding='101', sparse_embedding=SparseVector([2, 2, 2]))

Item.create(id=3, embedding=[1, 1, 2], half_embedding=[1, 1, 2], binary_embedding='111', sparse_embedding=SparseVector([1, 1, 2]))

class TestPeewee:

def setup_method(self) -> None:

Item.truncate_table()

def test_vector(self) -> None:

Item.create(id=1, embedding=[1, 2, 3])

item = Item.get_by_id(1)

assert item.embedding == [1, 2, 3]

def test_vector_l2_distance(self) -> None:

create_items()

distance = Item.embedding.l2_distance([1, 1, 1])

items = Item.select(Item.id, distance.alias('distance')).order_by(distance).limit(5)

assert [v.id for v in items] == [1, 3, 2]

assert [v.distance for v in items] == [0, 1, sqrt(3)]

def test_vector_max_inner_product(self) -> None:

create_items()

distance = Item.embedding.max_inner_product([1, 1, 1])

items = Item.select(Item.id, distance.alias('distance')).order_by(distance).limit(5)

assert [v.id for v in items] == [2, 3, 1]

assert [v.distance for v in items] == [-6, -4, -3]

def test_vector_cosine_distance(self) -> None:

create_items()

distance = Item.embedding.cosine_distance([1, 1, 1])

items = Item.select(Item.id, distance.alias('distance')).order_by(distance).limit(5)

assert [v.id for v in items] == [1, 2, 3]

assert [v.distance for v in items] == [0, 0, 0.05719095841793653]

def test_vector_l1_distance(self) -> None:

create_items()

distance = Item.embedding.l1_distance([1, 1, 1])

items = Item.select(Item.id, distance.alias('distance')).order_by(distance).limit(5)

assert [v.id for v in items] == [1, 3, 2]

assert [v.distance for v in items] == [0, 1, 3]

def test_halfvec(self) -> None:

Item.create(id=1, half_embedding=[1, 2, 3])

item = Item.get_by_id(1)

assert item.half_embedding == [1, 2, 3]

def test_halfvec_l2_distance(self) -> None:

create_items()

distance = Item.half_embedding.l2_distance([1, 1, 1])

items = Item.select(Item.id, distance.alias('distance')).order_by(distance).limit(5)

assert [v.id for v in items] == [1, 3, 2]

assert [v.distance for v in items] == [0, 1, sqrt(3)]

def test_halfvec_max_inner_product(self) -> None:

create_items()

distance = Item.half_embedding.max_inner_product([1, 1, 1])

items = Item.select(Item.id, distance.alias('distance')).order_by(distance).limit(5)

assert [v.id for v in items] == [2, 3, 1]

assert [v.distance for v in items] == [-6, -4, -3]

def test_halfvec_cosine_distance(self) -> None:

create_items()

distance = Item.half_embedding.cosine_distance([1, 1, 1])

items = Item.select(Item.id, distance.alias('distance')).order_by(distance).limit(5)

assert [v.id for v in items] == [1, 2, 3]

assert [v.distance for v in items] == [0, 0, 0.05719095841793653]

def test_halfvec_l1_distance(self) -> None:

create_items()

distance = Item.half_embedding.l1_distance([1, 1, 1])

items = Item.select(Item.id, distance.alias('distance')).order_by(distance).limit(5)

assert [v.id for v in items] == [1, 3, 2]

assert [v.distance for v in items] == [0, 1, 3]

def test_bit(self) -> None:

Item.create(id=1, binary_embedding='101')

item = Item.get_by_id(1)

assert item.binary_embedding == '101'

def test_bit_hamming_distance(self) -> None:

create_items()

distance = Item.binary_embedding.hamming_distance('101')

items = Item.select(Item.id, distance.alias('distance')).order_by(distance).limit(5)

assert [v.id for v in items] == [2, 3, 1]

assert [v.distance for v in items] == [0, 1, 2]

def test_bit_jaccard_distance(self) -> None:

create_items()

distance = Item.binary_embedding.jaccard_distance('101')

items = Item.select(Item.id, distance.alias('distance')).order_by(distance).limit(5)

assert [v.id for v in items] == [2, 3, 1]

# assert [v.distance for v in items] == [0, 1/3, 1]

def test_sparsevec(self) -> None:

Item.create(id=1, sparse_embedding=[1, 2, 3])

item = Item.get_by_id(1)

assert item.sparse_embedding == SparseVector([1, 2, 3])

def test_sparsevec_l2_distance(self) -> None:

create_items()

distance = Item.sparse_embedding.l2_distance(SparseVector([1, 1, 1]))

items = Item.select(Item.id, distance.alias('distance')).order_by(distance).limit(5)

assert [v.id for v in items] == [1, 3, 2]

assert [v.distance for v in items] == [0, 1, sqrt(3)]

def test_sparsevec_max_inner_product(self) -> None:

create_items()

distance = Item.sparse_embedding.max_inner_product([1, 1, 1])

items = Item.select(Item.id, distance.alias('distance')).order_by(distance).limit(5)

assert [v.id for v in items] == [2, 3, 1]

assert [v.distance for v in items] == [-6, -4, -3]

def test_sparsevec_cosine_distance(self) -> None:

create_items()

distance = Item.sparse_embedding.cosine_distance([1, 1, 1])

items = Item.select(Item.id, distance.alias('distance')).order_by(distance).limit(5)

assert [v.id for v in items] == [1, 2, 3]

assert [v.distance for v in items] == [0, 0, 0.05719095841793653]

def test_sparsevec_l1_distance(self) -> None:

create_items()

distance = Item.sparse_embedding.l1_distance([1, 1, 1])

items = Item.select(Item.id, distance.alias('distance')).order_by(distance).limit(5)

assert [v.id for v in items] == [1, 3, 2]

assert [v.distance for v in items] == [0, 1, 3]

def test_where(self) -> None:

create_items()

items = Item.select().where(Item.embedding.l2_distance([1, 1, 1]) < 1) # type: ignore

assert [v.id for v in items] == [1]

def test_vector_avg(self) -> None:

avg = Item.select(fn.avg(Item.embedding).coerce(True)).scalar()

assert avg is None

Item.create(embedding=[1, 2, 3])

Item.create(embedding=[4, 5, 6])

avg = Item.select(fn.avg(Item.embedding).coerce(True)).scalar()

assert avg == [2.5, 3.5, 4.5]

def test_vector_sum(self) -> None:

sum = Item.select(fn.sum(Item.embedding).coerce(True)).scalar()

assert sum is None

Item.create(embedding=[1, 2, 3])

Item.create(embedding=[4, 5, 6])

sum = Item.select(fn.sum(Item.embedding).coerce(True)).scalar()

assert sum == [5, 7, 9]

def test_halfvec_avg(self) -> None:

avg = Item.select(fn.avg(Item.half_embedding).coerce(True)).scalar()

assert avg is None

Item.create(half_embedding=[1, 2, 3])

Item.create(half_embedding=[4, 5, 6])

avg = Item.select(fn.avg(Item.half_embedding).coerce(True)).scalar()

assert avg == [2.5, 3.5, 4.5]

def test_halfvec_sum(self) -> None:

sum = Item.select(fn.sum(Item.half_embedding).coerce(True)).scalar()

assert sum is None

Item.create(half_embedding=[1, 2, 3])

Item.create(half_embedding=[4, 5, 6])

sum = Item.select(fn.sum(Item.half_embedding).coerce(True)).scalar()

assert sum == [5, 7, 9]

def test_get_or_create(self) -> None:

Item.get_or_create(id=1, defaults={'embedding': [1, 2, 3]}) # type: ignore

Item.get_or_create(embedding=Vector([4, 5, 6])) # type: ignore

Item.get_or_create(embedding=Item.embedding.to_value([7, 8, 9])) # type: ignore

def test_vector_array(self) -> None:

from playhouse.postgres_ext import PostgresqlExtDatabase, ArrayField

ext_db = PostgresqlExtDatabase('pgvector_python_test')

class ExtItem(BaseModel):

embeddings = ArrayField(VectorField, field_kwargs={'dimensions': 3}, index=False) # type: ignore

class Meta: # type: ignore

database = ext_db

table_name = 'peewee_ext_item'

ext_db.connect()

ext_db.drop_tables([ExtItem])

ext_db.create_tables([ExtItem])

# fails with column "embeddings" is of type vector[] but expression is of type text[]

# ExtItem.create(id=1, embeddings=[np.array([1, 2, 3]), np.array([4, 5, 6])])

# item = ExtItem.get_by_id(1)

# assert np.array_equal(item.embeddings[0], [1, 2, 3])

# assert np.array_equal(item.embeddings[1], [4, 5, 6])