Knowledge Graphs vs RAG: Structured Graphs, GraphRAG and When to Combine Them

Knowledge graphs and RAG solve different problems that happen to overlap. RAG retrieves the most relevant chunks of text to ground an answer, while a knowledge graph stores entities and the relationships between them so you can traverse connections a passage search would miss. GraphRAG tries to get the best of both. This article explains what each approach is actually good at, where plain RAG falls short, and how to combine them without over-engineering your stack.

What RAG actually retrieves

Standard RAG works on unstructured text. Documents are split into chunks, each chunk is embedded (and usually indexed for keyword search too), and at query time the system finds the chunks most similar to the question. The language model then writes an answer grounded in those passages, ideally with citations back to the source. This works well when the answer lives inside a contiguous block of text: a clause in a contract, a paragraph in a policy, a step in a procedure.

The weakness shows up with questions that require connecting facts scattered across many documents. "Which suppliers used by our three biggest clients also appear on the sanctions list?" is not answerable by finding one good passage, because the answer requires joining several separate facts. Pure vector or hybrid search can surface each fact individually but has no native concept of "supplier of", "client of" or "appears on". For a deeper look at how retrieval itself works, see our guide on hybrid search combining BM25 and vectors, and on how documents get split in the first place in chunking strategies for RAG.

What a knowledge graph adds

A knowledge graph represents information as nodes (entities: people, companies, products, clauses) and edges (relationships: employs, supplies, cites, amends). Instead of searching for similar text, you query the graph structurally: "find all companies two hops away from Client X via the supplier relationship" or "list every clause that amends Section 4.2". This is precise, auditable and fast for multi-hop reasoning, because the relationships are explicit rather than implied by proximity in text.

The cost is construction. Someone, or some extraction pipeline, has to decide what counts as an entity, what counts as a relationship, and keep the graph in sync as source documents change. Unlike RAG, where adding a new PDF is mostly automatic, adding a document to a knowledge graph usually means running entity and relation extraction, resolving duplicates (is "Acme Ltd" the same node as "Acme Limited"?), and validating the result. That maintenance overhead is the main reason knowledge graphs are less common than RAG for general-purpose document Q&A.

GraphRAG: retrieval with structure on top

GraphRAG is an umbrella term for approaches that build a lightweight knowledge graph from a document set (usually automatically, via an LLM extracting entities and relationships from chunks) and then use that graph to improve retrieval. There are two common patterns worth distinguishing:

1. Graph-augmented retrieval: the graph helps decide which chunks to retrieve. If the question mentions an entity, the system also pulls in chunks linked to that entity's neighbours in the graph, not just chunks that are textually similar to the question.
2. Community summarisation: chunks are clustered into graph communities (groups of densely connected entities), each community gets a summary, and broad questions ("what are the main themes across all supplier contracts?") are answered from these summaries rather than individual passages, because no single passage covers the whole picture.

GraphRAG genuinely helps with two kinds of question that plain RAG struggles with: multi-hop questions that require chaining facts across documents, and aggregate or "global" questions that ask about patterns across an entire corpus rather than a specific fact. It does not replace citation-grounded passage retrieval for straightforward lookup questions, and it adds real engineering and compute cost, since building and maintaining the graph typically means an extra extraction pass over every document.

When plain RAG is genuinely enough

Most document Q&A use cases never need a graph. If your users ask factual lookup questions, one question has one answer that sits in one place, a well-tuned hybrid search index with citations will outperform a hastily built graph every time, because it is simpler, cheaper and easier to audit. This covers the bulk of practical applications: policy lookup, contract clause retrieval, technical documentation search, customer support knowledge bases, regulatory guidance Q&A. If you're unsure whether RAG or simply feeding everything into a long context window suits your case better, our comparison of RAG vs long-context models is a useful starting point before reaching for anything more complex.

Signs you don't need a graph yet:

• Users mostly ask "what does X say about Y" style questions answerable from a single passage.
• Your documents don't have rich, explicit relationships worth modelling (a staff handbook rarely needs an entity graph).
• You need answers live within days, not after weeks of schema design and extraction tuning.
• Citation to source passages matters more to your users than traversal of connections.

When a graph (or GraphRAG) earns its keep

The calculus flips when the value genuinely lives in the connections rather than in any single document. Due diligence work that needs to trace ownership structures across filings, compliance teams mapping obligations that cite and amend each other across multiple regulations, or research teams who need to spot patterns across thousands of incident reports are all cases where a graph layer, or GraphRAG's community summarisation, adds real value that passage retrieval cannot match on its own.

Building a practical hybrid: RAG first, graph where it's worth it

A pragmatic path for most teams is to start with solid hybrid search and citations, measure where it fails, and only add graph structure around the specific failure modes you actually observe. In practice that means:

1. Stand up a RAG base over your documents with hybrid search and inline citations, so every answer is traceable to a source passage.
2. Log the questions that get poor or incomplete answers, particularly ones that require combining facts from more than one document.
3. For that subset of questions, consider whether lightweight metadata filtering solves it, before building an entity graph.
4. If multi-hop or corpus-wide questions remain common, scope a GraphRAG layer around the specific entity types that matter (suppliers, clauses, incidents) rather than trying to model everything.

On the tooling side, Kopik (kopik.io) focuses on the first part of that pipeline: you can create a knowledge base for free by uploading PDFs, Word documents, text or Markdown, and it handles chunking and hybrid indexing so answers come back with cited passages rather than free-floating text. That gives you a solid, auditable baseline to measure graph-worthy gaps against before investing in anything more structural. Bases can stay private for internal use, or be listed in the catalogue if you want to monetise specialist knowledge, which is a separate decision from the retrieval architecture itself.

Cost, accuracy and governance trade-offs

Knowledge graphs and GraphRAG are not free upgrades. Extraction errors compound: if the pipeline misreads "Company A acquired Company B" as the reverse relationship, every downstream query inherits that mistake silently, and errors in a graph are harder to spot than a wrong passage citation, because there's no source text sitting next to the answer for a human to check. Entity resolution (merging "J. Smith", "John Smith" and "Smith, John" into one node) is a classic source of silent duplication or false merging that needs ongoing review.

There's also a plain cost dimension: extracting entities and relationships from every document is an extra LLM pass on top of normal chunking and embedding, and that cost scales with corpus size and update frequency. Our breakdown of what RAG really costs covers ingestion and retrieval economics in detail; GraphRAG adds a further extraction and graph-storage layer on top of that baseline, which is worth quantifying before committing. From a governance angle, UK organisations handling personal data should also revisit data minimisation and retention questions when building any entity graph of people, not just a document index; see our UK GDPR checklist for RAG for the broader list of things to check first.

If you're building any of this with developer tooling rather than off-the-shelf products, it's worth reading a general survey of the open-source RAG stack and deciding build vs buy before adding graph components, since several of the heavier open-source graph libraries bring their own operational burden (a graph database, a schema, an extraction pipeline) that a small team may not want to own.

Terminology worth knowing before you evaluate vendors

If you're reading vendor documentation or academic papers on this topic, a lot of terms get used loosely. Our AI and LLM glossary for professionals covers terms like embeddings, chunking and retrieval in plain language, and our guide to evaluating a RAG system explains how to measure faithfulness and citation accuracy, which matters just as much (arguably more) once you add a graph layer that can obscure where an answer actually came from.