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.
In this GenAI workshop, you will learn how Knowledge Graphs and Retrieval Augmented Generation (RAG) can support your GenAI projects.
GenAI and Large Language Models (LLMs) have the potential to increase productivity and provide access to data, but they need grounding and good context to be truly useful:
In this workshop, 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.
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