Graph Database - Atlanta

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

This hands-on lab is designed to demonstrate how to build LLM-powered agents grounded in a Neo4j knowledge graph, without relying on traditional retrieval tools or custom orchestration code. Instead of defining tools and debugging when (or if) they are invoked, you will configure a Neo4j Context Provider that injects graph context automatically before every LLM call.
The workshop walks through three progressive configurations. You will start with full-text search using a Neo4j index to provide immediate, structured context to the agent. Next, you will extend the same pattern to vector search with embeddings for semantic matching. Finally, you will implement graph-enriched retrieval queries that traverse relationships and return connected context that goes beyond what indexes alone can provide.
Through guided exercises, you will build a working self-grounding agent that consistently receives relevant knowledge from a graph on every interaction. You will leave with practical configuration patterns you can adapt to any Neo4j schema and apply to real-world agent architectures.
You will learn:

• How to configure the Neo4j Context Provider for fulltext and vector search
• How to write retrieval queries that traverse graph relationships
• How to tune key parameters such as top_k, message_history_count, and index selection
• How to debug and validate context injection by inspecting what the agent receives

Join us live with the authors of Neo4j: The Definitive Guide - the practical playbook for building production-ready graph apps with Neo4j!

We’ll dive into real-world modeling tips, Cypher performance tricks and proven strategies that take graph projects from proof-of-concept to production.
Whether you’re exploring Neo4j for the first time or leveling up your graph skills, this session will give you actionable insights and answers.

What to expect:
- How to model connected data for maximum clarity
- Cypher query tuning techniques
- Architecting Neo4j into modern systems
- Live Q&A with the authors

Guests: Luanne Misquitta ( https://www.linkedin.com/in/luannemisquitta/ ), Christophe Willemsen ( https://www.linkedin.com/in/christophe-willemsen/ )
Book: https://www.oreilly.com/library/view/neo4j-the-definitive/9781098165642/