How do you use pgvector with Django?

# pip install pgvector django psycopg2-binary

# settings.py - make sure django uses PostgreSQL:
DATABASES = {
    "default": {
        "ENGINE": "django.db.backends.postgresql",
        "NAME": "mydb",
        "USER": "user",
        "PASSWORD": "pass",
        "HOST": "localhost",
    }
}

# models.py
from django.db import models
from pgvector.django import VectorField, HnswIndex

class Document(models.Model):
    content   = models.TextField()
    embedding = VectorField(dimensions=1536)  # pgvector field

    class Meta:
        indexes = [
            HnswIndex(
                name="document_embedding_hnsw",
                fields=["embedding"],
                m=16,
                ef_construction=64,
                opclasses=["vector_cosine_ops"],
            )
        ]

# Migration: generates CREATE EXTENSION vector + table
# python manage.py makemigrations && python manage.py migrate

# views.py / management commands:
from pgvector.django import CosineDistance

# Insert
Document.objects.create(content="Example", embedding=[0.1, 0.2, ...])

# Query: 5 most similar documents
query_vector = get_embedding("search query")
results = Document.objects.annotate(
    distance=CosineDistance("embedding", query_vector)
).order_by("distance")[:5]

for doc in results:
    print(doc.content, doc.distance)