GenAI Beyond Chat with RAG, Knowledge Graphs, and Python

Session Track: AI

Session Time: 04:00 pm - 04:30 pm November 7

Session description

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 LLMs, hallucination, and integrating knowledge graphs - explore 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.