Sep 10, 2024 · Aug 22, 2024 · Aug 28, 2024 · Sep 10, 2024
diff --git a/pgml-cms/blog/.gitbook/assets/django-pgml_blog-image.png b/pgml-cms/blog/.gitbook/assets/django-pgml_blog-image.png
diff --git a/pgml-cms/blog/SUMMARY.md b/pgml-cms/blog/SUMMARY.md
 # Table of contents

 * [Home](README.md)
 * [Announcing postgresml-django](announcing-postgresml-django.md)
 * [Sudowrite + PostgresML](sudowrite-postgresml.md)
 * [Korvus x Firecrawl: Rag in a single query](korvus-firecrawl-rag-in-a-single-query.md)
 * [A Speed Comparison of the Most Popular Retrieval Systems for RAG](a-speed-comparison-of-the-most-popular-retrieval-systems-for-rag.md)
diff --git a/pgml-cms/blog/announcing-postgresml-django.md b/pgml-cms/blog/announcing-postgresml-django.md
 ---
 description: The Python module that seamlessly integrates PostgresML and Django ORM
 featured: true
 tags: [product]
 image: ".gitbook/assets/django-pgml_blog-image.png"
 ---

 # Announcing postgresml-django

 <div align="left">

 <figure><img src=".gitbook/assets/silas.jpg" alt="Author" width="100"><figcaption></figcaption></figure>

 </div>

 Silas Marvin

 September 10, 2024

 We're excited to announce the release of [postgresml-django](https://github.com/postgresml/postgresml-django), a Python module that bridges the gap between PostgresML and Django ORM. This powerful tool enables automatic in-database embedding of Django models, simplifying the process of creating and searching vector embeddings for your text data.

 With postgresml-django, you can:
 - Automatically generate in-database embeddings for specified fields in your Django models
 - Perform vector similarity searches directly in your database
 - Seamlessly integrate advanced machine learning capabilities into your Django projects

 Whether you're building a recommendation system, a semantic search engine, or any application requiring text similarity comparisons, postgresml-django streamlines your workflow and enhances your Django projects with the power of PostgresML.

 ## Quick Start

 Here's a simple example of how to use postgresml-django with a Django model:

 ```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", "query to search against")
 ```

 In this example, we define a `Document` model with a `text` field and a `text_embedding` VectorField. The VectorField automatically generates embeddings for the `text` field using the specified transformer. The `vector_search` method allows for easy similarity searches based on these embeddings.

 ## Why We are Excited About this

 There are ton of reasons we are excited for this release but they can all be summarized by two main points:

 1. Simplicity: postgresml-django integrates advanced machine learning capabilities into Django projects with just a few lines of code, making it accessible to developers of all skill levels.
 2. Performance: By leveraging PostgresML to perform vector operations directly in the database, it significantly improves speed and efficiency, especially when dealing with large datasets.

 By bridging Django ORM and PostgresML, we're opening up new possibilities for building intelligent, data-driven applications with ease.

 ## Recap

 postgresml-django marks a significant step forward in making advanced machine learning capabilities accessible to Django developers. We invite you to try it out and experience the power of seamless vector embeddings and similarity searches in your projects.

 For more detailed information, installation instructions, and advanced usage examples, check out the [postgresml-django GitHub repository](https://github.com/postgresml/postgresml-django). We're eager to hear your feedback and see the innovative ways you'll use postgresml-django in your applications.

 Happy coding!
Original file line number	Diff line number	Diff line change
		@@ -1,6 +1,7 @@
		# Table of contents

		* [Home](README.md)
		* [Announcing postgresml-django](announcing-postgresml-django.md)
		* [Sudowrite + PostgresML](sudowrite-postgresml.md)
		* [Korvus x Firecrawl: Rag in a single query](korvus-firecrawl-rag-in-a-single-query.md)
		* [A Speed Comparison of the Most Popular Retrieval Systems for RAG](a-speed-comparison-of-the-most-popular-retrieval-systems-for-rag.md)
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,66 @@
		---
		description: The Python module that seamlessly integrates PostgresML and Django ORM
		featured: true
		tags: [product]
		image: ".gitbook/assets/django-pgml_blog-image.png"
		---

		# Announcing postgresml-django

		<div align="left">

		<figure><img src=".gitbook/assets/silas.jpg" alt="Author" width="100"><figcaption></figcaption></figure>

		</div>

		Silas Marvin

		September 10, 2024

		We're excited to announce the release of [postgresml-django](https://github.com/postgresml/postgresml-django), a Python module that bridges the gap between PostgresML and Django ORM. This powerful tool enables automatic in-database embedding of Django models, simplifying the process of creating and searching vector embeddings for your text data.

		With postgresml-django, you can:
		- Automatically generate in-database embeddings for specified fields in your Django models
		- Perform vector similarity searches directly in your database
		- Seamlessly integrate advanced machine learning capabilities into your Django projects

		Whether you're building a recommendation system, a semantic search engine, or any application requiring text similarity comparisons, postgresml-django streamlines your workflow and enhances your Django projects with the power of PostgresML.

		## Quick Start

		Here's a simple example of how to use postgresml-django with a Django model:

		```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", "query to search against")
		```

		In this example, we define a `Document` model with a `text` field and a `text_embedding` VectorField. The VectorField automatically generates embeddings for the `text` field using the specified transformer. The `vector_search` method allows for easy similarity searches based on these embeddings.

		## Why We are Excited About this

		There are ton of reasons we are excited for this release but they can all be summarized by two main points:

		1. Simplicity: postgresml-django integrates advanced machine learning capabilities into Django projects with just a few lines of code, making it accessible to developers of all skill levels.
		2. Performance: By leveraging PostgresML to perform vector operations directly in the database, it significantly improves speed and efficiency, especially when dealing with large datasets.

		By bridging Django ORM and PostgresML, we're opening up new possibilities for building intelligent, data-driven applications with ease.

		## Recap

		postgresml-django marks a significant step forward in making advanced machine learning capabilities accessible to Django developers. We invite you to try it out and experience the power of seamless vector embeddings and similarity searches in your projects.

		For more detailed information, installation instructions, and advanced usage examples, check out the [postgresml-django GitHub repository](https://github.com/postgresml/postgresml-django). We're eager to hear your feedback and see the innovative ways you'll use postgresml-django in your applications.

		Happy coding!