Graph Database - Midwest

This workshop takes you from theory to practice with a fully operational hybrid Retrieval-Augmented Generation (RAG) pipeline - built live in Neo4j. You'll learn how to combine traditional BM25 search with dense vector similarity and graph-native expansion to power more accurate and explainable results.
Over the course of two hours, you’ll create a knowledge graph, run hybrid retrieval directly in the database, and integrate your results into a LangChain-powered RAG application. You’ll leave with a working system you can run on your own machine, complete with a GitHub repo ready for production use.

This hands-on workshop shows you how to build smarter AI agents: ones that don’t lose context. You’ll use Neo4j’s Model Context Protocol (MCP) to give your agents persistent, graph-based memory, allowing for richer, multi-turn interactions and intelligent behavior grounded in your data.

You’ll walk through the full agent-building process: from setting up a knowledge graph, to integrating GraphRAG for multi-hop reasoning, and deploying an agent with contextual memory that evolves over time. By the end of the session, you’ll have a working, production-ready AI agent you can extend for your own use cases.

Ever wanted to build an agent that can understand your data and answer complex questions for you? In this workshop, you’ll roll up your sleeves and create your own agent in Neo4j Aura, complete with a UI and a real use case around legal documents and KYC (Know Your Customers) checks.