If you are a CTO, Chief of AI, VP of Product/Data or AI, Staff+ AI Engineers, Data Scientist or anyone tasked with getting real results from your SQL sources by building RAG beyond vector search, this is for you.

Most enterprise context lives in SQL but GraphRAG needs structure. In this live technical session, we’ll (re)introduce the Memgraph AI Toolkit, unpack the core challenges of turning relational data into production-ready knowledge graphs (entity modeling, FK → edge induction, denormalization, provenance, performance), and then build it live with SQL2Graph.

We’ll look under the hood and see how HyGM (Hypothetical Graph Model) proposes, explains, and iteratively refines a graph design from your schema so that you can validate assumptions before migrating to Memgraph. We’ll run through Automatic and Incremental modes, validate the model, then query the result for GraphRAG.

Why GraphRAG needs graph structure (not just embeddings) to deliver multi-hop, explainable answers

Hypothesizes an initial graph from SQL schemas

Validates before migration to reduce rework

How SQL2Graph works - handling PK/FK patterns junction and wide tables as well as denormalisation trade-offs

From Tables to Knowledge Graphs: SQL2Graph for GraphRAG