Knowledge graphs for RAG — often called GraphRAG — retrieve information by following the relationships between entities rather than by matching text similarity alone. A standard RAG system chops documents into chunks, embeds them, and pulls back the chunks nearest your query. That works well for "what does this passage say," but stumbles on questions that require connecting facts scattered across many documents. A knowledge graph stores facts as nodes and edges — people, places, concepts, and how they relate — so the retriever can traverse those links.
What changed in 2026
- LLM-built graphs got cheap enough to be practical. Using models to extract entities and relationships from raw text turned graph construction from a manual project into an automated pipeline.
- Hybrid retrieval won. The consensus moved to combining vector search with graph traversal rather than choosing one; each covers the other blind spots.
- Tooling matured. Graph databases added native vector indexes, and several open frameworks standardized the extract, build, and query loop.
Why plain vector RAG struggles
Vector search is excellent at finding text that is locally similar to your query. It falters on three kinds of question:
- Multi-hop questions that require chaining facts, such as "which projects are led by people who reported to the former CTO."
- Global questions about the whole corpus, such as "what are the main themes across all these reports," where no single chunk holds the answer.
- Aggregation that needs structure, such as counting or grouping related entities.
In each case the right answer lives in the connections between passages, not inside any one passage. Vector search cannot see those connections. If you want to understand the retrieval layer it complements, start with embeddings explained in 2026.
How GraphRAG works
- Extract. An LLM or NER pipeline reads your documents and pulls out entities and the relationships between them.
- Build. Those triples become a graph. Optionally, nodes are clustered into communities and each community is summarized so the system can reason at different zoom levels.
- Query. At question time, the system identifies the entities in your query, traverses their neighborhood in the graph, optionally blends in vector search results, and hands the assembled subgraph to the LLM to compose an answer.
Vector RAG vs graph RAG
| Dimension |
Vector RAG |
Graph RAG |
| Retrieval unit |
Text chunks |
Entities and relationships |
| Strength |
Local, passage-level questions |
Multi-hop and global questions |
| Explainability |
Which chunks were used |
Which path of facts was followed |
| Setup cost |
Low |
Higher (extraction and schema) |
| Maintenance |
Re-embed changed text |
Re-extract and reconcile the graph |
| Best paired with |
Reranking |
Vector search (hybrid) |
When to use a knowledge graph
Choose GraphRAG when your data is highly connected, your users ask relationship-heavy or multi-hop questions, or you need explainable, auditable answers for compliance. It is worth the extra effort in domains like medicine, finance, law, and enterprise knowledge bases. Skip it when your corpus is small, your questions are simple lookups, or your content changes so fast that keeping a graph current would cost more than it returns.
Pitfalls to watch
- Extraction errors propagate. A wrong relationship in the graph produces wrong answers downstream, so validate the extraction step.
- Construction and maintenance cost. Graphs need care as source data changes; budget for reconciliation.
- Schema design is real work. Deciding which entities and relationships to model shapes everything the system can answer.
- Staleness. A graph is only as current as its last extraction run.
FAQ
Do I have to choose between vector search and a knowledge graph?
No. The strongest systems in 2026 are hybrid, using vector search for passage-level recall and the graph for relationships and structure.
Do I need a graph database?
It helps at scale, especially now that many graph databases include vector indexes. Small graphs can live in simpler stores.
Can an LLM build the graph automatically?
Yes, and that is what made GraphRAG practical. Still, review the extracted entities and relationships, because errors carry through.
Is GraphRAG slower than vector RAG?
Traversal adds work, but for multi-hop questions it often reaches a correct answer in fewer overall steps than stuffing many chunks into the prompt.
Where to go next