Create a free graph database instance in Neo4j AuraDB
TL;DR: Neo4j now has a first‑class Vector type for storing embedding vectors. It’s supported end‑to‑end (drivers → Bolt → Cypher → storage → constraints). Your app code gets simpler, your data integrity gets stronger, and future vector‑specific optimizations become possible.
A vector embedding is a fixed‑length, single dtype numeric array (for example, VECTOR(1024)) used in semantic search, GraphRAG, and many other GenAI pattern...