GraphRAG with Neo4j + Qdrant: Hybrid Graph-Vector Retrieval for Multi-Hop Reasoning Failures in Production RAG

Qdrant Documentation·high signal

Vector-only RAG fails on multi-hop queries (who manages the team that owns the service that handles payments?) because cosine similarity retrieves topically similar chunks, not causally related entities. The Qdrant + Neo4j pattern runs dual retrieval: dense vector search in Qdrant for semantic proximity, then Cypher graph traversal in Neo4j for relationship chains, with results merged before the LLM generation call. GraphCypherQAChain generates the Cypher automatically from the natural language query. Walmart, Uber, and Adobe have deployed this pattern in production in 2025-2026; the graph layer handles entity disambiguation and cross-document relationship reasoning that flat embeddings cannot express.