Even a galactic empire can fail when its AI retrieves the right documents but misses the connections between them. Following one ambitious stormtrooper through a series of increasingly consequential knowledge failures, this talk explores how GraphRAG, graph-based retrieval, and graph memory have become a cornerstone of modern enterprise knowledge systems.

Based on the O’Reilly book GraphRAG: The Definitive Guide by Stephen Chin, Michael Hunger, and Jesús Barrasa, the session provides a practical map of the patterns behind these systems. We begin with the limits of vector-only RAG when questions are connected, multi-hop, dynamic, or require an explainable evidence chain. From there, we cover knowledge graph construction from structured and unstructured data, ontology-guided extraction, entity resolution, and the roles of lexical, domain, and meta graphs. We examine hybrid retrieval using vector search, BM25, graph filtering, guided traversal, Cypher templates, Text2Cypher, graph embeddings, community detection, and query-focused summarization. We then connect retrieval to graph memory and context graphs that preserve conversations, reasoning traces, tool activity, reusable procedures, and changing operational state.

Along the way, we address evaluation, provenance, access control, freshness, governance, and the trade-offs involved in moving these systems into production. Attendees will leave with a decision framework for designing enterprise knowledge systems that are accurate, explainable, adaptive, and capable of supporting agents in large-scale enterprise scenarios.