Discover how to make knowledge graphs for RAG using the Neo4j LLM Knowledge Graph Builder with this simple guide. Improve your RAG model's performance by using this up-to-date method of organizing data.

Knowledge graphs are now crucial for handling and examining data. They help link and visualize complicated data. Neo4j, a top graph database platform, has launched the LLM Knowledge Graph Builder to make creating knowledge graphs for Retrieval-Augmented Generation (RAG) easier and to help understand these graphs better.

In this beginner's guide, I’ll lead you through the steps of making knowledge graphs using Neo4j's new tool. If you're new to graph databases or an expert in RAG, you'll learn how to:

• Install Neo4j LLM Knowledge Graph Builder
• Make your first knowledge graph for RAG
• See the graph you made and discover useful information The aim is to teach you how to use Neo4j's tool to create knowledge graphs that enhance your data analysis and information retrieval. We'll begin with the basics and move through the main features.

What are knowledge graphs when talking about RAG?

Knowledge graphs show information in an organized way, highlighting connections between different things. They have nodes (standing for ideas or things) and edges (showing how these things are related). Basically, knowledge graphs arrange data similarly to how people understand and link information.

When discussing Retrieval Augmented Generation (RAG), knowledge graphs work as a sophisticated system for storing and finding information. RAG is a method that helps language models by giving them useful external knowledge while creating content. Knowledge graphs add to the more common, only vector-based RAG by:

• Arranging Information: They organize data so it's easy to search and navigate.
• Making Connections: They clearly show relationships between different pieces of information.
• Giving Context: They provide a wider view of how various facts or ideas are connected to each other.

They promise to improve RAG systems in the following way:

• Improved Accuracy—Knowledge graphs, by furnishing information that is both structured and relevant, will allow an RAG system to generate answers that are more accurate and contextually appropriate.
• Enhanced Reasoning—This results from the relational structuring provided by knowledge graphs, which attach many components of disintegrated information.
• Flexibility—They can be readily updated and extended, through which RAGs could incorporate new information over time.
• Explainability—The presence of explicit relationships in knowledge graphs allows one to trace more easily how a RAG system came to a certain output, making it more transparent and interpretable.
• Reduced Hallucination—Since a knowledge graph helps to ground the language models into a structured knowledge base, it might mitigate the chance of RAG systems generating false or inconsistent information.