Traditional RAG systems rely on retrieving individual text chunks using vector similarity. While this works well for many tasks, it can struggle with questions that require understanding relationships between entities, reasoning across multiple documents, or connecting pieces of information spread throughout a knowledge base.

This is where GraphRAG and knowledge graphs become powerful. Instead of treating information as isolated text chunks, knowledge graphs represent data as entities and relationships, forming a structured network of connected information. This structure allows the system to navigate relationships, aggregate related facts, and retrieve more meaningful context for complex queries.

In this chapter, we will explore how knowledge graphs work, how they integrate with RAG systems, and how GraphRAG can help build applications that answer more complex, relationship-heavy questions with greater accuracy.