Chapter 48: Knowledge Graphs & GraphRAG

Part 6: AI Native Software Development — Phase 3: Data & Memory

This chapter teaches you how to build knowledge graphs and implement GraphRAG (Graph-based Retrieval-Augmented Generation) for AI agents. You'll learn when graph structures outperform vector similarity, how to model domain knowledge as graphs, and how to combine graph traversal with LLM reasoning.

What You'll Learn

• Knowledge Graph Fundamentals: Nodes, edges, properties, and graph schemas
• Graph Databases: Neo4j and lightweight alternatives for agent applications
• GraphRAG Architecture: Combining graph traversal with vector retrieval
• Entity Extraction: Automatically building graphs from unstructured text
• Multi-hop Reasoning: Traversing relationships for complex queries
• Hybrid RAG: When to use graphs vs vectors vs both

Prerequisites

• Chapter 43: Vector Databases & RAG (vector retrieval foundations)
• Chapter 44: Relational Databases for Agents (data modeling concepts)
• Chapters 34-36: Agent SDK fundamentals

Key Technologies

Technology	Purpose
Neo4j	Production graph database
LangChain GraphRAG	Graph-enhanced retrieval
NetworkX	Lightweight graph operations
Entity Extraction	Building graphs from text

Running Example

Extend the Task API with knowledge graph capabilities:

• Task dependencies as graph relationships
• Project hierarchies and team structures
• Multi-hop queries: "Show all tasks blocking the Q1 release"

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Note: This chapter is under development. Lessons will cover graph fundamentals through production GraphRAG implementation.