Search is a core and high user impact feature in many Django apps. However, queryset filters and keyword search in Django apps often fails to meet user expectations for search relevance, speed, and personalization.

This talk introduces vector search — a powerful new paradigm that goes beyond keywords to search, recommend, and analyze your data. You’ll learn the basics of vector embeddings, how vector search works, and how to integrate vector databases into your Django models using the django-semantic-search package — with practical code examples.

We'll show a demo of product search/recommendations in a Django e-commerce app, then briefly explore other use cases like multi-modal search, content discovery, clustering, and retrieval-augmented generation (RAG). Finally, we’ll cover the trade-offs of vector search to help you decide when it’s the right tool for the job.