Upgrade your RAG applications with the power of knowledge graphs.

Retrieval Augmented Generation (RAG) is a great way to harness the power of generative AI for information not contained in a LLM's training data and to avoid depending on LLM for factual information. However, RAG only works when you can quickly identify and supply the most relevant context to your LLM. Essential GraphRAG shows you how to use knowledge graphs to model your RAG data and deliver better performance, accuracy, traceability, and completeness.

Inside Essential GraphRAG you'll learn:

• The benefits of using Knowledge Graphs in a RAG system
• How to implement a GraphRAG system from scratch
• The process of building a fully working production RAG system
• Constructing knowledge graphs using LLMs
• Evaluating performance of a RAG pipeline

Essential GraphRAG is a practical guide to empowering LLMs with RAG. You'll learn to deliver vector similarity-based approaches to find relevant information, as well as work with semantic layers, and generate Cypher statements to retrieve data from a knowledge graph.