RAG With Knowledge Graph — Relationship-Aware Retrieval

I will build a knowledge graph-augmented RAG system — extracting entities and relationships from your documents, building a graph structure, and implementing GraphRAG retrieval that combines vector similarity with graph traversal for queries requiring multi-hop reasoning across connected information. Vector-only RAG fails on multi-hop queries — questions that require connecting information across multiple documents through explicit relationships (who reports to whom, which products are affected by which regulations, what events caused which outcomes) require graph traversal that embedding similarity cannot perform; GraphRAG handles these correctly by querying both the vector store and the knowledge graph simultaneously.

The build covers entity and relationship extraction pipeline, graph database construction (Neo4j or lightweight alternative), dual-mode retrieval combining vector and graph queries, result fusion, and multi-hop reasoning evaluation.