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https://github.com/postgresml/postgresml-django
postgresml-django is a Python module that integrates PostgresML with Django ORM, enabling automatic in-database embedding of Django models.
https://github.com/postgresml/postgresml-django
django embeddings llm postgresql
Last synced: 3 months ago
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postgresml-django is a Python module that integrates PostgresML with Django ORM, enabling automatic in-database embedding of Django models.
- Host: GitHub
- URL: https://github.com/postgresml/postgresml-django
- Owner: postgresml
- License: mit
- Created: 2024-08-21T22:14:33.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-08-21T22:48:05.000Z (6 months ago)
- Last Synced: 2024-09-27T21:18:29.822Z (5 months ago)
- Topics: django, embeddings, llm, postgresql
- Language: Python
- Homepage: https://postgresml.org
- Size: 3.91 KB
- Stars: 18
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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
1. Ensure you have [pgml](https://github.com/postgresml/postgresml) installed and configured in your database. The easiest way to do that is to sign up for a free serverless database at [postgresml.org](https://postgresml.org). You can also host it your self.
2. 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.
```python
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.
```python
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:
1. The `dimensions` parameter is set to 384 for `intfloat/e5-small-v2` and 1024 for `mixedbread-ai/mxbai-embed-large-v1`.
2. 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.