Knowledge Graph For RAG: Step-by-Step Tutorial

Knowledge graph-based retrieval-augmented generation is presented as an alternative or complement to vector search for applications requiring explicit relationships, business-rule filtering, and explainable results. While embeddings support semantic similarity, knowledge graphs represent facts as entity-relation triples that can be queried through graph traversal and constraints, such as identifying suppliers of a product within a particular region. The tutorial demonstrates building a supply-chain question-answering system with Neo4j, Python, OpenAI-based triple extraction from procurement CSV records, Cypher queries, and template-based natural-language responses. It also outlines methods for evaluating graph quality, including coverage, accuracy, completeness, explainability, precision and recall, consistency checks, and manual audits. The discussion concludes that combining language models with graph retrieval can provide structured and reliable answers for domains containing interconnected data, while also mentioning Supermemory as a platform supporting vector and graph-style retrieval.

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