For example, let's consider an example of a movie recommendation system. The idea is to represent each movie in our catalog as a vector, a numerical representation of a piece of text.

Once we're done with that and also converted the search phrase into a vector, we step into a whole new realm — a multidimensional space where these vectors replace the original text values. Through some mathematical techniques, we can determine which movie representations are closest to the vector representing our search query!

How do we create such vector representations? We need an AI model trained on vast amounts of data to recognize patterns and effectively convert text phrases into vectors.

All of this you'll learn in this session. We'll try out different data solutions - ClickHouse, OpenSearch and PGVector. We'll also explore different models available depending on your language preference and skills, or, in case you 'd rather not run the model locally, what APIs you can use to do data inference for free.

Plenty of demos and a bit of coding for each of the options. This session will be useful for anyone who is intrigued by contextual search and usage of AI, but might find themselves overwhelmed by the complexities to get started.