Building a Foundational Understanding of Knowledge Graph for Intelligent Applications
In the previous chapter, we looked at what RAG is and at a few simple examples of how we can implement RAG flow, along with LLMs. In this chapter, we will take a look at what knowledge graphs are and how graphs can make Retrieval-Augmented Generation (RAG) more effective. We will explore how to model knowledge graphs and how Neo4j can be used for this purpose. We will look at how data modeling with the Neo4j data persistence approach can help build more powerful knowledge graphs. We will also look at data store persistence approaches, from Relational Database Management Systems (RDBMSs) to Neo4j knowledge graphs, to get a better understanding of data using various data models.
We will embark on an exciting journey to understand how the fusion of RAG models and Neo4j’s robust graph database capabilities enables the creation of intelligent applications that leverage structured knowledge...
