Speaker:
Prashanth Rao, AI Engineer, Kùzu, Inc.
Abstract:
In this session, we will introduce Kùzu, a highly scalable, extremely fast, easy-to-use, open source embedded graph database designed for analytical query workloads. Users who are familiar with DuckDB in the SQL world will find Kùzu to be a refreshingly familiar graph analogue. A number of state-of-the-art methods from graph database research are highlighted.
The workshop will include a practical component that showcases how simple and easy-to-use Kùzu is for data scientists and engineers. We will demonstrate popular use cases by transforming a relational dataset (in the form of tables) into a knowledge graph, run Cypher queries on the graph, analyze the dataset using graph algorithms, and train a simple graph neural network using PyTorch Geometric to compute node embeddings and store them in the graph database for downstream use cases. We will end by summarizing how these methods can help build advanced RAG systems that can be coupled with an LLM downstream.
Additional notes
In addition to the workshop where we go into the hands-on concepts of knowledge graphs and how to use them, we'd very much like to have a 30-minute talk that introduces the idea of Kùzu and how it's different from other graph databases, and the core innovations under the hood. If the organizers feel that the content is better separated into two parts (a separate talk on the main stage and the workshop with the practical component), that's perfectly fine as well. For this reason, I've opted for any of the available presentation times.