Stop Drowning in Vectors: How I Built a Graph-Powered RAG That Actually Scales

Traditional vector-based RAG struggles with scaling across hundreds of documents and tracking relationships between content, often missing critical connections or overwhelming context windows. A developer solved this by replacing vector embeddings with a Neo4j graph database that stores documents as hierarchical trees, allowing AI models to navigate document structures directly and traverse cross-document relationships through precise reasoning rather than similarity search.