Description:

In this GenAI workshop, you will learn how Knowledge Graphs and Retrieval Augmented Generation (RAG) can support your GenAI projects.

You will: - Use Vector indexes and embeddings in Neo4j to perform similarity and keyword search - Use Python, LangChain and OpenAI to create a Knowledge Graph of unstructured data - Learn about Large Language Models (LLMs), hallucination and integrating knowledge graphs - Explore Retrieval Augmented Generation (RAG) and its role in grounding LLM-generated content

After completing this workshop, you will be able to explain the terms LLM, RAG, grounding, and knowledge graphs. You will also have the knowledge and skills to create simple LLM-based applications using Neo4j and Python.

This workshop will put you on the path to controlling LLMs and enabling their integration into your projects.

Prerequisites:

Before taking this course, you should have: - Knowledge of Python and capable of reading simple programs

Video URL:

https://drive.google.com/file/d/1oGPTD6bnWl4CQvbJevbM7saLtBKrrW5U/view?usp=sharing

Speaker Info:

Martin O'Hanlon

I am a highly experienced computer science educator with over a decades experience in delivering face to face and online learning experiences. I am renowned for my knowledge and execution of good computer science pedagogy - I run fun, inclusive, supported but stretching workshops.

I understand the barriers to a successful workshop (initial setup, access to learning materials, pace, support and stretch).

I work in Developer Relations for Neo4j, a lot of my work involves creating educational material including online courses through Neo4js GraphAcademy platform.

Speaker Links:

https://ohanlonweb.com https://twitter.com/martinohanlon https://github.com/martinohanlon https://www.linkedin.com/in/martin-o-hanlon-3466a45/