Developer Builds Hybrid RAG System Merging Neo4j Graph Database with Vector Search

A developer has built a hybrid Retrieval-Augmented Generation (RAG) system that combines Neo4j's graph database with vector memory tools ChromaDB and FAISS, orchestrated via LangChain. The system addresses a core limitation of standard vector-only RAG setups, which struggle with multi-hop reasoning tasks that require linking related entities across multiple information layers. The architecture works in two stages: vector search first narrows down semantically relevant chunks, then graph traversal resolves structured relationships between those entities. Key design choices included extracting entities before storage, capping graph traversal depth at one to two hops, and applying token budgeting to keep LLM context focused. The approach reportedly improved answer accuracy for complex, relationship-dependent queries that pure vector search either answered incompletely or resolved through hallucination.