Django MariaDB Vector

Django ORM support for the MariaDB Vector field (introduced in MariaDB 11.7+).

Why this project exists

MariaDB introduced native vector support, allowing you to store embeddings and perform similarity search directly in the database.

However, Django currently lacks:

• a native VECTOR model field
• ORM support for vector queries
• automatic migration support for vector indexes

This project fills that gap by providing a clean, Django-native way to work with MariaDB vectors.

Features

• Django MariaDBVectorField for VECTOR(n)
• Automatic Django migrations
• Automatic VECTOR INDEX creation
• ORM-friendly similarity queries
• Recommendation Manager for Django models
• No raw SQL required
• Works with MariaDB 11.8.2+
• Optionally optimization for better performance when working with vectors with use package orjson and binary_responce option

Why use this

Without this package, using MariaDB vectors in Django requires:

• manual SQL migrations
• custom query expressions
• fragile schema management

With this package, you get:

• clean Django models
• automatic schema generation
• reusable and maintainable code

Use cases

This package is useful for:

• Retrieval-Augmented Generation (RAG)
• semantic search
• recommendation systems
• document / code similarity search

Typical workflow: text → embedding → store → similarity search

How it works

• Maps Django field → VECTOR(n)
• Extends Django migration system
• Injects MariaDB-specific SQL for:
• vector columns
• vector indexes
• Wraps vector distance functions like:
  • VEC_DISTANCE
  • VEC_DISTANCE_COSINE
  • VEC_DISTANCE_EUCLIDEAN
• Recommendation Manager (bonus)

Requirements

• Python 3.12+
• Django 5.2+, or 6.0+
• MariaDB 11.8.2 or newer

Installation

pip install django-mariadb-vector

Optional: faster JSON serialization

For better performance when working with vectors, install with the orjson extra:

pip install django-mariadb-vector[orjson]

Usage

models.py:

from django.db import models
from django_mariadb_vector import MariaDBVectorField, MariaDBVectorIndex


class MyModel(models.Model):
    embedding = MariaDBVectorField(dimensions=3)

    class Meta:
        indexes = [
            # Vector index (MariaDB 11.8.2+)
            MariaDBVectorIndex(fields=["embedding"], dimensions=3)
        ]

Querying with Vector Functions

You can use VecDistance to perform similarity searches.

from django_mariadb_vector import VecDistance

from .models import MyModel

# Find 5 most similar records to a reference vector
reference_vector = [0.1, 0.2, 0.3]
results = MyModel.objects.annotate(
    distance=VecDistance("embedding", reference_vector)
).order_by("distance")[:5]

Optimization: orlson for better performance

in case of installation django-mariadb-vector library with extra orjson depencedcy

pip install django-mariadb-vector[orjson]

or

uv add django-mariadb-vector[orjson]

Benefits orlson vs json (batch mode)

• Up to ~20× faster compared to the standard json library on generate vector data
• Up to ~8× faster compared to the standard json library on response vector data

Note: Performance was measured using 20,000 iterations (3 runs) in
tests/test_performance.py, with randomly generated vectors of dimension 3072.

Optimization: binary_response for better performance

The binary_response option improves performance when working with vectors by returning data in a compact binary format instead of JSON.

• Vectors are returned as a sequence of little-endian IEEE 754 float32 bytes (4 bytes per value)
  (MariaDB reference)
• Reduces network traffic between MariaDB and your application
• Eliminates JSON parsing overhead on the Python side

Benefits of binary response (batch mode)

• Up to ~16× faster compared to the standard json library
• About ~2× faster compared to orjson
• Lower bandwidth usage for large vector payloads

Note: Performance was measured using 20,000 iterations (3 runs) in
tests/test_performance.py, with randomly generated vectors of dimension 3072.

Usage of binary_response

from django.db import models
from django_mariadb_vector import MariaDBVectorField

class MyModel(models.Model):
    embedding = MariaDBVectorField(dimensions=3, binary_response=True)

Recommendation Manager for Django models

Using a RecommendationManager can simplify vector searches in your application:

models.py:

from django.db import models

from django_mariadb_vector import MariaDBVectorField, MariaDBVectorIndex
from django_mariadb_vector.managers import RecommendationManager


class MyModel(models.Model):
    embedding = MariaDBVectorField(dimensions=3)
    
    objects = RecommendationManager(vector_field="embedding")
    
    class Meta:
        indexes = [
            # Vector index (MariaDB 11.8.2+)
            MariaDBVectorIndex(fields=["embedding"], dimensions=3, m=16),
        ]

Example of usage Manager

from .models import MyModel

reference_vector:list[float] = [0.1, 0.2, 0.3]

# Find 5 most similar records to a reference vector
results = MyModel.objects.similar_to_vector(reference_vector, limit=5)

for item in results:
    print(f"{item.name} - Distance: {item.distance}")

from .models import MyModel

reference_id:int = 1
# Find 5 most similar records to a reference object by id
results = MyModel.objects.similar_to(reference_id, limit=5)

for item in results:
    print(f"{item.name} - Distance: {item.distance}")

Advanced Functions

The following functions are available in django_mariadb_vector.functions:

• Search(expression, vector): Convenient wrapper for COSINE distance search.
• VecDistance(expression, vector): Generic VEC_DISTANCE function.
• VecDistanceCosine(expression, vector): Native MariaDB VEC_DISTANCE_COSINE.
• VecDistanceEuclidean(expression, vector): Native MariaDB VEC_DISTANCE_EUCLIDEAN.
• VecFromText(text): Converts JSON string to MariaDB VECTOR format.
• VecToText(expression): Converts MariaDB VECTOR format to JSON string.

Reference

• https://mariadb.com/docs/server/reference/sql-functions/vector-functions

Demo

A minimal demo project showing how to build article recommendations using vector similarity in Django with MariaDB as the database using the django-mariadb-vector library.

• Demo repo: https://github.com/lexxai/django-mariadb-vector-demo
• Example: https://github.com/lexxai/django-mariadb-vector-demo/tree/main/docs

Docker Testing

You can test the build and run tests in Docker for different Python versions and OS images. The Dockerfile is located in tests/docker/Dockerfile.

Using the helper script (Linux/macOS)

bash tests/docker/run_docker_tests.sh

Manual build example

docker build --build-arg PYTHON_VERSION=3.13 -f tests/docker/Dockerfile -t django-mariadb-vector:test-3.13 .
docker run --rm django-mariadb-vector:test-3.13

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT