Road to NODES AI - GenAI and Graph Foundations: Building GraphRAG with Neo4j

This workshop is designed to introduce you to Generative AI, Retrieval-Augmented Generation (RAG), and GraphRAG, and show how these techniques can be combined with Neo4j to build intelligent, graph-powered applications. You will explore how large language models (LLMs) can be grounded in both structured and unstructured data to produce more accurate, relevant, and explainable responses.
The workshop includes hands-on exercises where you will build a knowledge graph from unstructured documents and structured data, enrich it with embeddings, and use vector indexes in Neo4j to perform similarity search. You will create multiple retriever patterns—including vector-based, vector plus Cypher, and text-to-Cypher retrievers—and see how GraphRAG improves retrieval quality by leveraging graph structure.
By the end of the session, you will build a conversational agent using Neo4j, Python, and LangChain. You will leave with practical experience designing GraphRAG pipelines and a solid foundation for applying these techniques to real-world use cases.

You will learn:

• The fundamentals of Generative AI and large language models (LLMs)
• What RAG is and why it is essential for grounding LLMs
• How GraphRAG improves retrieval and response quality
• How to build knowledge graphs from unstructured documents and structured data
• How to use vectors and embeddings in Neo4j for similarity search
• How to implement different retriever patterns using the neo4j-graphrag Python package
• How to build a conversational agent with Neo4j, Python, and LangChain