BLISS x Neo4j: Powering GenAI with Knowledge Graphs

Spaces are limited, please sign up over luma under the following link:

https://lu.ma/7j77q70m

Abstract
​Generative AI models have the potential to increase productivity and provide access to data, but they need good context to be truly useful.
​In this hands-on workshop, you will learn how Knowledge Graphs and Retrieval Augmented Generation (RAG) can help your GenAI projects avoid hallucination and provide access to reliable data.
​In this hands-on workshop, you will:
​- Learn about Large Language Models (LLMs), hallucination and integrating knowledge graphs
​- Explore Retrieval Augmented Generation (RAG) and GraphRAG and their role in
​- Use vector indexes and embeddings to find similar data
​- Query graphs using natural language
​- Use Python and OpenAI to create GraphRAG retrievers and GenAI applications

​This workshop will put you on the path to controlling Generative AI applications and integrating them into your projects.

​Logistics
​The workshop will be delivered using Neo4j's GraphAcademy platform, learners will only need a laptop and an internet connection. Everything else will be provided.
​The only prerequisite for this workshop is a knowledge of Python and being capable of reading simple programs.

​About the speaker
​Martin is an experienced computer science educator and open source software developer.
​Martin creates educational content for Neo4j and supports developers in using graph technology to understand their data.
​As a child he wanted to be either a Computer Scientist, Astronaut or Snowboard Instructor.