Retrieval-Augmented Generation (RAG) has revolutionized how we interact with LLMs, but standard vector search often fails to capture the complex, multi-hop relationships hidden in data.

““GraphRAG”” solves this by grounding answers in a structured Knowledge Graph, But building these graphs from raw unstructured data at scale presents a massive data engineering challenge.

In this session, we will demonstrate how to build a production-grade GraphRAG ingestion and retrieval pipeline using Apache Beam and Google Cloud Spanner Graph.