Today marks an exciting new milestone for Neo4j with the introduction of native vector search. Available to all Neo4j customers, vector search provides a simple approach for quickly finding contextually related information and, in turn, helps teams uncover hidden relationships. This powerful capability, now fully integrated into Neo4j AuraDB and Neo4j Graph Database, enables customers to use vector search to achieve richer insights from generative AI applications by understanding the meaning behind words rather than matching keywords.

For example, a query about budget-friendly cameras for teenagers will return low-cost, durable options in fun colors by interpreting the contextual meaning.

// find products by similarity search in vector index
CALL db.index.vector.queryNodes('products', 5, $embedding) yield node as product, score

// enrich with additional explicit relationships from the knowledge graph
MATCH (product)-[:HAS_CATEGORY]->(cat), (product)-[:BY_BRAND]->(brand)
OPTIONAL MATCH (product)<-[:BOUGHT]-(customer)-[rated:RATED]->(product) 
         WHERE rated.rating = 5
OPTIONAL MATCH (product)-[:HAS_REVIEW]->(review)<-[:WROTE]-(customer) 

RETURN product.Name, product.Description, brand.Name, cat.Name, 
       collect(review { .Date, .Text })[0..5] as reviews

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