MariaDB Vector

Integrate MariaDB's VECTOR type with SQL Alchemy and SQL Model

Installation

pip install mariadb-vector

Usage

Add a vector column

from sqlmodel import SQLModel, Field, Column
from mariadb_vector import Vector


class Item(SQLModel, table=True):
    embedding: list[float] = Field(sa_column=Column(Vector(3)))

Insert a vector

item = Item(embedding=[0.1, 0.2, 0.3])
session.add(item)
session.commit()

Vector Search

The following functions are available for calculating vector distances:

vec_from_seq(array: Sequence)

• Converts a Python list or NumPy array into a database-compatible vector function.

vec_distance(v1, v2)

• Calculates a general distance between two vectors.

vec_distance_euclidean(v1, v2)

• Calculates the Euclidean distance between two vectors.

vec_distance_cosine(v1, v2)

• Calculates the cosine distance between two vectors.

# Assume `engine` is already created and connected to your database.
with Session(engine) as session:
    target_vector = [0.1, 0.2, 0.3]
    query = (
        select(Item.id)
        .order_by(vec_distance_euclidean(Item.embedding, target_vector))
        .limit(2)
    )
    result = session.exec(query).all()
    print(result)

Contributing

• This library is inspired by the pgvector-python and is built to bring similar functionality to MariaDB.
• Any contributions, bug reports, or improvements are welcome!
• Feel free to open issues or submit pull requests.