[Coding Workshop] Build a Recommendation System WebApp using Vector Search and OpenAI embeddings

Available On-demand

We will quickly cover the basics of vector search, OpenAI embedding models, and how to handle large data, especially in real time. Following this will be a guided session where you will develop a functional web application. This application will recommend the top 10 most similar video games to a given input game, utilizing a publicly available Amazon product reviews dataset.

Agenda

Part 1 (20 min)

  • Introduction to Vector Search: Understanding the fundamentals and its application in recommendation systems.
  • Overview of OpenAI Embeddings: Exploring how OpenAI embeddings work
  • Rockset for Data Management: Demonstrating Rockset's features for handling large datasets efficiently.

Part 2 (40 min)

  • Building the Webapp: Guided session on developing a functional web application incorporating the above concepts.

Speakers

Ankit Khare
Ankit Khare currently leads the development of the Developer Relations function at Rockset. Previously, he served as the Head of Content and Developer Relations at Abacus.AI and as Developer Experience Lead at Twelve Labs Inc. His extensive experience also includes a tenure as the Head of Deep Learning R&D at Third Insight and a role as an AI Researcher at the Robotics Lab at UT Arlington.

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