Hands-on-lab: GraphRAG: building AI applications with a knowledge graph

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 hands-on workshop, you will: – Learn about Large Language Models (LLMs), hallucination and integrating knowledge graphs – Explore Retrieval Augmented Generation (RAG) and its role in grounding LLM-generated content – 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 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.