postgresml-django

postgresml-django is a Python module that integrates PostgresML with Django ORM, enabling automatic in-database embedding of Django models. It simplifies the process of creating and searching vector embeddings for your text data.

Introduction

This module provides a seamless way to:

Automatically generate in-databse embeddings for specified fields in your Django models
Perform vector similarity searches in-database

Installation

Ensure you have pgml installed and configured in your database. The easiest way to do that is to sign up for a free serverless database at postgresml.org. You can also host it your self.
Install the package using pip:
```
pip install postgresml-django
```

You are ready to go!

Usage Examples

Example 1: Using intfloat/e5-small-v2

This example demonstrates using the intfloat/e5-small-v2 transformer, which has an embedding size of 384.

from django.db import models
from postgresml_django import VectorField, Embed

class Document(Embed):
    text = models.TextField()
    text_embedding = VectorField(
        field_to_embed="text",
        dimensions=384,
        transformer="intfloat/e5-small-v2"
    )

# Searching
results = Document.vector_search("text_embedding", "some query to search against")

Example 2: Using mixedbread-ai/mxbai-embed-large-v1

This example shows how to use the mixedbread-ai/mxbai-embed-large-v1 transformer, which has an embedding size of 1024 and requires specific parameters for recall.

from django.db import models
from postgresml_django import VectorField, Embed

class Article(Embed):
    content = models.TextField()
    content_embedding = VectorField(
        field_to_embed="content",
        dimensions=1024,
        transformer="mixedbread-ai/mxbai-embed-large-v1",
        transformer_recall_parameters={
            "prompt": "Represent this sentence for searching relevant passages: "
        }
    )

# Searching
results = Article.vector_search("content_embedding", "some query to search against")

Note the differences between the two examples:

The dimensions parameter is set to 384 for intfloat/e5-small-v2 and 1024 for mixedbread-ai/mxbai-embed-large-v1.
The mixedbread-ai/mxbai-embed-large-v1 transformer requires additional parameters for recall, which are specified in the transformer_recall_parameters argument.

Both examples will automatically generate embeddings when instances are saved and allow for vector similarity searches using the vector_search method.

Contributing

We welcome contributions to postgresml-django! Whether it's bug reports, feature requests, documentation improvements, or code contributions, your input is valuable to us. Feel free to open issues or submit pull requests on our GitHub repository.

License

postgresml/postgresml-django

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