Introduction to Graph RAG

What and Why

Standard RAG embeds chunks of text and retrieves the ones most similar to a query. This works for “find me a passage about X” but stumbles on questions that need to combine facts spread across many documents: “Which company that acquired a payments startup in 2023 also rebranded last quarter?” That is a multi-hop question requiring chained reasoning.

Graph RAG layers a knowledge graph on top of your corpus. Entities and relationships extracted from documents become nodes and edges. At query time, the system finds relevant nodes, walks the graph, collects evidence, and feeds that structured context to the LLM. The graph encodes connections that text similarity cannot see.

Mental Model

Vector RAG is a librarian who reads many passages and hands you the ones that sound like your question. Graph RAG is a detective who keeps an evidence board: faces, places, and lines of red string connecting them. When you ask a question, the detective traces the strings between the clues you mentioned and reports back the connected story.

Vector RAG is good at “what does the document say”; Graph RAG is good at “how are these things related.”

Hands-on Example

A minimal Graph RAG pipeline has three phases: extraction, storage, retrieval. Extraction uses an LLM to pull (entity, relation, entity) triples from each chunk:

prompt = """Extract triples as JSON list of [subject, predicate, object].
Text: {chunk}"""
triples = llm(prompt.format(chunk=text))

Store them in a graph database such as Neo4j:

MERGE (a:Entity {name: $s})
MERGE (b:Entity {name: $o})
MERGE (a)-[:REL {type: $p}]->(b)

At query time, identify entities in the question, find matching nodes, expand the neighborhood, and pass both the subgraph and the original chunks to the LLM.

Documents
 |
 v
+-----------+    triples    +-----------+
| Extractor | ------------> |  Graph DB |
+-----------+               +-----------+
                                ^
Query --> [Entity linker] --------+
                                |
                                v
                        [Subgraph + chunks]
                                |
                                v
                               LLM
                                |
                                v
                            Answer

Microsoft’s GraphRAG framework adds a clever step: it clusters the graph into communities, then summarizes each community offline. At query time the LLM picks relevant community summaries instead of walking the raw graph. This is how Graph RAG scales to large corpora and handles broad questions.

Trade-offs

Graph RAG is powerful but expensive. Extracting triples from every document costs many LLM calls. The graph also needs maintenance: when source documents change, related nodes and edges must be updated or deleted. For most teams, vanilla RAG handles 80 percent of queries at a fraction of the cost.

Quality depends heavily on the extractor. Sloppy extraction creates noisy edges that send the traversal off in wrong directions. Domain-specific schemas (predefining allowed relationship types) help, but limit flexibility.

Graph traversal can also blow up. A few hops in a dense graph pulls in thousands of nodes. You need pruning rules: limit depth, score edges by frequency, or rank nodes by centrality.

Finally, Graph RAG adds operational complexity: another database to run, another extraction pipeline, another failure mode. Reach for it only when the questions you want to answer truly demand it.

Practical Tips

• Start with vanilla RAG. Switch to Graph RAG only when you can name three real questions it would unlock.
• Constrain the extractor with a relationship schema. Free-form triples become noise quickly.
• Cache and version the graph. Rebuilds are expensive; incremental updates are valuable.
• Combine graph context with text chunks in the final prompt. The graph gives structure, the text gives nuance.
• Use community detection or hierarchical summaries for broad questions and direct traversal for narrow ones.
• Monitor extraction quality with spot checks. A 5 percent error rate in triples can sink retrieval.

Wrap-up

Graph RAG shines on multi-hop and relationship-heavy questions where vector search alone falls short. By turning unstructured text into a navigable graph of entities and relations, it gives the LLM structured context to reason with. It costs more to build and maintain, so use it where its strengths matter most: connected, evolving domains like enterprise knowledge, research, and intelligence.