Rapid AI: Powered Applications With the Django-MongoDB Backend and Voyage API

This article is written by Marko Aleksendric (Data Analyst)

If you’ve got an appetite for learning about the latest in AI and how you can use it to transform the way you work, then this article is for you. Discover how the powerful combination of Django, MongoDB’s new AI smarts, and Voyage AI can turn your leftover ingredients into delicious dinner ideas – no grocery run needed!

MongoDB is enhancing its platform with AI-powered search and retrieval features, like embeddings and reranking, to make data processing smarter and more efficient. These tools are key in systems like retrieval-augmented generation (RAG), which improve AI outputs by fetching the most relevant data to ground those responses. By integrating these capabilities directly into the database layer, MongoDB is simplifying application stacks, reducing the need for complex AI pipelines, and improving data accuracy. In this project, we’ll build a RAG-like system to demonstrate how these features work in practice!

A Brief History

Django is one of the most mature and actively maintained web frameworks in the Python ecosystem, renowned for its “batteries included” philosophy. It enables rapid development of secure, maintainable web applications by offering built-in support for URL routing, middleware, template rendering, form handling, authentication, and database abstraction via a powerful Object-Relational Mapper (ORM). Django projects follow the Model–View–Template (MVT) architecture, where models define the data schema, views handle the application logic, and templates control the presentation layer.

Though initially designed to render HTML on the server side, Django has evolved to support modern web development patterns. Tools like Django REST Framework and Django-Ninja allow developers to expose APIs in JSON format, enabling Django to serve as a backend for single-page applications and mobile apps. In addition, its extensible admin interface—automatically generated from your models—provides powerful CRUD capabilities out of the box. Combined with robust security defaults, scalability features, a vast third-party package ecosystem, and thorough documentation, Django remains a top choice for Python developers building anything from simple websites to enterprise-grade applications.

Learn more in-depth about the history here!

A Needed Integration

The Django MongoDB Backend is a new official integration from MongoDB that lets Django developers use MongoDB as the database behind their projects. This backend offers deep support for core Django features like models, migrations, and the admin panel—while still giving access to MongoDB’s advanced capabilities like aggregations and vector search. It’s easy to set up using Django’s standard settings system, and while it’s still in public preview and not yet recommended for production, it opens up exciting possibilities for building modern, AI-powered web apps with Django and MongoDB together!

The project: A Smart Recipe Application

We’ll use Django and MongoDB to cook up a smart recipe app that finds dishes you can make with what’s already in your fridge. In this project, you’ll work with several cutting-edge technologies to build an AI-powered recipe app. Here’s a quick rundown of what you’ll be using and learning:

  • Django for creating the web application
  • Django MongoDB Backend to integrate MongoDB seamlessly
  • PyMongo for direct interaction with MongoDB
  • Voyage AI for generating embeddings of recipe ingredients
  • Claude LLM by Anthropic for smart recipe suggestions

Prerequisites

Before diving in, make sure you have these resources ready:

With these tools in hand, you’ll be ready to start building your very own AI-powered recipe app! Head over to this GitHub repo to find all the resources you need to start building or follow along with the step-by-step tutorial.

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