RAG Needs a Map: Using GraphRAG to Retrieve Connected Context — Nyah Macklin, Neo4j
A hands-on GraphRAG workshop from Neo4j showing how combining vector search with a knowledge graph gives an agent connected context, multi-hop traversal, and explainable answers. It covers the difference between semantic similarity and relationships, LLM-driven entity and relationship extraction under a fixed schema, building an Aura database and Python pipeline, and configuring an Aura agent with similarity search and text-to-Cypher, with a closing check that shows why visible query evidence matters.
