GraphRAG Visualised follows one question: why is the Bristol launch at risk? Press Play to see it move through a 3D graph, collect source passages and form a cited example answer. Graph hops and Passage budget are the two settings. Play resumes a paused sequence or starts it again after completion; changing either setting prepares a fresh run.
The example follows a fictional UK operations team. A Bristol launch needs a sensor delivery, engineers are shared with Leeds, and a component substitution requires assurance approval. The graph connects these entities to the documents that describe their relationships.
Local retrieval starts at the entity named in the question. Graph hops determines how far it explores the connected neighbourhood. The animation names the new entities as they join the search. Their linked passages become retrieval candidates, ranked here using toy cosine similarity.
Passage budget determines how many of those candidates enter context. More graph exploration can find more candidate evidence, but a small budget can still exclude an important passage. Try one hop with four passages, then increase the budget and watch the answer change. The graph and answer use the same selected evidence.
Source passages appear as the run progresses. A graph path points towards possible evidence; it does not prove a claim. Each authored answer point declares its required passages. The example includes it only if those sources are present in context and identifies the evidence missing from withheld points.
This is a simplified local GraphRAG example. The documents, entity matches, graph relationships, vectors and candidate answers are authored fixtures. No language model is called. Real GraphRAG systems also support community-report approaches for broader questions and need evaluation of extraction quality, retrieval relevance and whether the cited text actually supports the answer.
The maths
- Toy passage ranking
The local retrieval example ranks candidate passages with cosine similarity. These three-dimensional vectors are authored examples, not embeddings produced by a language model.
- The provenance check
Each authored claim c declares its required passages S. It passes the demonstration check only when every required passage is present in the selected context. A graph path alone cannot pass this check.
Sources and model assumptions
Follow the original mechanism behind this explainer. The interactive examples identify their toy data and simplifying assumptions above.
Related terms
- Context windowThe context window is the maximum amount of text, measured in tokens, that a model can consider for a single request. It is a hard ceiling, and it is the main resource you manage when working with an agent.
- GraphRAGGraphRAG retrieves over a graph of entities and relationships rather than a flat pile of chunks, so the model can follow connections between facts. It answers questions that need several hops, which similarity search cannot reach.
- Retrieval-augmented generation (RAG)RAG is the workhorse pattern of context engineering: retrieve the material relevant to a request, put it in the context, and let the model generate an answer grounded in it rather than guessing from memory.
- Contextual retrievalContextual retrieval prepends a short, generated description of where a chunk came from before embedding it. It fixes the fact that chunking strips away the context a chunk needs in order to be findable.