Vector search has revolutionized semantic retrieval, letting systems surface relevant results from natural language input. But when users ask nuanced, open-ended questions—like “Which sessions match my interests and are presented by speakers I haven’t seen before?”—semantic similarity alone falls short. It retrieves results, but can’t reason over relationships, enforce constraints, or explain why something was chosen.

In this session, we explore how combining vector search, knowledge graphs, and multi-hop reasoning creates a more intelligent and trustworthy retrieval experience. Using examples from EventKernel, an event intelligence platform built on Neo4j, we demonstrate how dynamic user queries are interpreted into hybrid retrieval flows:

- Vector-only: fast, flexible, but opaque
- GraphRAG: vector search anchored in Neo4j for semantically grounded and structurally accurate answers
- Graph Reasoning: deterministic Cypher-based lookups where logic, depth, and context matter

Attendees will learn how to structure queries dynamically, blend embeddings with graph traversals, and enforce explainability in AI assistants—all while keeping performance and safety in check.