Graph Database - Mexico

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/

This session is designed to demonstrate how GraphRAG architectures can be applied to healthcare and other regulated domains where data is heterogeneous, distributed, and subject to stringent governance requirements. You will explore how knowledge graphs, graph neural networks (GNNs), and LLM-based agents collaborate to overcome the limitations of traditional text-centric RAG approaches, particularly in terms of temporal reasoning, explainability, and compliance.
The workshop introduces an agent-driven GraphRAG architecture built on Neo4j that unifies medical ontologies, clinical documents, and research publications in a materialized knowledge graph, while keeping sensitive electronic health record (EHR) data virtually integrated rather than centralized. Through architectural walkthroughs and examples, you will see how agents plan and orchestrate graph queries, apply GNN-based relevance and similarity scoring, and enforce controlled access to regulated data sources.
By grounding retrieval and reasoning in graph structure, this approach enables multi-hop, temporally aware reasoning with traceable and explainable outputs. You will leave with a clear understanding of when and how to combine knowledge graphs, GNNs, and agents to design GraphRAG systems suitable for high-trust environments such as healthcare.
You will learn:

• How to design an agent-driven GraphRAG architecture for regulated medical domains
• How knowledge graphs unify ontologies, documents, and reports while preserving data privacy
• How GNNs support relevance, similarity scoring, and multi-hop reasoning
• How to separate structural knowledge from patient-level data for governance and compliance
• How graph-powered RAG enables explainable, temporally grounded answers

This session is designed to show you how to accelerate Neo4j GenAI projects by using the Model Context Protocol (MCP) to create a fully integrated developer workspace. Proof-of-Value projects are often where ideas slow down, as teams move from data exploration to modeling, ingestion, and validation. In this workshop, you will see how an MCP-based IDE template streamlines this entire workflow and significantly reduces time-to-value.
The workshop demonstrates how to combine Neo4j and third-party MCP servers to support every stage of development, from data discovery and graph modeling to ingestion and use case verification. You will explore how this approach enables rapid prototyping across both structured and unstructured data, making it easier to test and validate the broad range of use cases supported by Neo4j.
Through practical examples and walkthroughs, you will learn how to apply this end-to-end workspace pattern to your own GenAI initiatives. You will leave with a clear understanding of how to adopt MCP in Neo4j projects to move faster from idea to working solution.
You will learn:

• How to use MCP to build an end-to-end Neo4j GenAI developer workspace
• How to accelerate data discovery, modeling, and ingestion for Proof-of-Value projects
• How to prototype and validate GenAI use cases across structured and unstructured data
• How to reduce time-to-value using an MCP-based development approach