from pgvector.django import VectorExtension

class Migration(migrations.Migration):
    operations = [
        VectorExtension()
    ]

from pgvector.django import VectorField

class Item(models.Model):
    factors = VectorField(dimensions=3)

item = Item(factors=[1, 2, 3])
item.save()

from pgvector.django import L2Distance

Item.objects.order_by(L2Distance('factors', [3, 1, 2]))[:5]

from pgvector.django import IvfflatIndex

class Item(models.Model):
    class Meta:
        indexes = [
            IvfflatIndex(
                name='my_index',
                fields=['factors'],
                lists=100,
                opclasses=['vector_l2_ops']
            )
        ]

from pgvector.sqlalchemy import Vector

class Item(Base):
    factors = Column(Vector(3))

item = Item(factors=[1, 2, 3])
session.add(item)
session.commit()

session.query(Item).order_by(Item.factors.l2_distance([3, 1, 2])).limit(5).all()

index = Index('my_index', Item.factors,
    postgresql_using='ivfflat',
    postgresql_with={'lists': 100},
    postgresql_ops={'factors': 'vector_l2_ops'}
)
index.create(engine)

from pgvector.psycopg2 import register_vector

register_vector(conn)

factors = np.array([1, 2, 3])
cur.execute('INSERT INTO item (factors) VALUES (%s)', (factors,))

cur.execute('SELECT * FROM item ORDER BY factors <-> %s LIMIT 5', (factors,))
cur.fetchall()

from pgvector.psycopg3 import register_vector

register_vector(conn)

factors = np.array([1, 2, 3])
cur.execute('INSERT INTO item (factors) VALUES (%s)', (factors,))

cur.execute('SELECT * FROM item ORDER BY factors <-> %s LIMIT 5', (factors,)).fetchall()

from pgvector.asyncpg import register_vector

await register_vector(conn)

factors = np.array([1, 2, 3])
await conn.execute('INSERT INTO item (factors) VALUES ($1)', factors)

await conn.fetch('SELECT * FROM item ORDER BY factors <-> $1 LIMIT 5', factors)