Let's demystify Gen AI and see how we can apply it to fun, approachable solutions, with a magical twist. We'll first explore how vector embeddings and LLMs work, before we set off to build our search solution (with a live demo).
Using the Elastic Python clients we first create indexes for Harry Potter characters, and film subtitles. We can import compatible 3rd party LLMs through an enriching pipeline; allowing us to add sentiment analysis and embeddings to our text. We’ll build a semantic search engine that can browse the books better than the ultimate fan.
