pgvector support for Python
Great for online recommendations 🎉
Supports Django, SQLAlchemy, Psycopg 3, Psycopg 2, and asyncpg
Run:
pip install pgvectorAnd follow the instructions for your database library:
Or check out some examples:
- Image search with PyTorch
- Implicit feedback recommendations with Implicit
- Explicit feedback recommendations with Surprise
- Recommendations with LightFM
Create the extension
from pgvector.django import VectorExtension
class Migration(migrations.Migration):
operations = [
VectorExtension()
]Add a vector field
from pgvector.django import VectorField
class Item(models.Model):
factors = VectorField(dimensions=3)Insert a vector
item = Item(factors=[1, 2, 3])
item.save()Get the nearest neighbors to a vector
from pgvector.django import L2Distance
Item.objects.order_by(L2Distance('factors', [3, 1, 2]))[:5]Also supports MaxInnerProduct and CosineDistance
Add an approximate index
from pgvector.django import IvfflatIndex
class Item(models.Model):
class Meta:
indexes = [
IvfflatIndex(
name='my_index',
fields=['factors'],
lists=100,
opclasses=['vector_l2_ops']
)
]Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance
Add a vector column
from pgvector.sqlalchemy import Vector
class Item(Base):
factors = Column(Vector(3))Insert a vector
item = Item(factors=[1, 2, 3])
session.add(item)
session.commit()Get the nearest neighbors to a vector
session.query(Item).order_by(Item.factors.l2_distance([3, 1, 2])).limit(5).all()Also supports max_inner_product and cosine_distance
Add an approximate index
index = Index('my_index', Item.factors,
postgresql_using='ivfflat',
postgresql_with={'lists': 100},
postgresql_ops={'factors': 'vector_l2_ops'}
)
index.create(engine)Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance
Register the vector type with your connection
from pgvector.psycopg import register_vector
register_vector(conn)Insert a vector
factors = np.array([1, 2, 3])
conn.execute('INSERT INTO item (factors) VALUES (%s)', (factors,))Get the nearest neighbors to a vector
conn.execute('SELECT * FROM item ORDER BY factors <-> %s LIMIT 5', (factors,)).fetchall()Register the vector type with your connection or cursor
from pgvector.psycopg2 import register_vector
register_vector(conn)Insert a vector
factors = np.array([1, 2, 3])
cur.execute('INSERT INTO item (factors) VALUES (%s)', (factors,))Get the nearest neighbors to a vector
cur.execute('SELECT * FROM item ORDER BY factors <-> %s LIMIT 5', (factors,))
cur.fetchall()Register the vector type with your connection
from pgvector.asyncpg import register_vector
await register_vector(conn)Insert a vector
factors = np.array([1, 2, 3])
await conn.execute('INSERT INTO item (factors) VALUES ($1)', factors)Get the nearest neighbors to a vector
await conn.fetch('SELECT * FROM item ORDER BY factors <-> $1 LIMIT 5', factors)View the changelog
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- Report bugs
- Fix bugs and submit pull requests
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
git clone https://github.com/pgvector/pgvector-python.git
cd pgvector-python
pip install -r requirements.txt
createdb pgvector_python_test
pytest