GraphRAG: Stop LLM Hallucinations with Knowledge Graphs

Vector search is good at finding similar text. It's bad at reasoning about relationships. This video explains why knowledge graphs change the retrieval picture for structured domains.

What's Wrong with Standard RAG

Standard RAG treats your documents as a bag of text chunks. You embed them, you embed the query, you find the nearest neighbors. This works fine when the question and the answer are semantically similar.

It fails when answering the question requires connecting facts across multiple documents, following causal chains, or reasoning about entity relationships. Financial analysis is full of these patterns: "How does the change in revenue from Q3 relate to the guidance update in the investor call?" No single chunk contains that answer.

Knowledge Graphs as a Retrieval Layer

GraphRAG structures your document knowledge as a graph — entities as nodes, relationships as edges. When you retrieve, you're not just finding similar text, you're traversing relationships.

For financial documents: companies, subsidiaries, executives, and metrics become nodes. Acquisitions, earnings revisions, and regulatory filings become edges. A query about "how the acquisition affected margins" becomes a graph traversal, not a nearest-neighbor search.

Where This Connects to VeNRA

VeNRA's Universal Fact Ledger is a specialized form of this idea: instead of a general knowledge graph, it's a typed ledger of numerical facts with explicit source attributions. The determinism comes from restricting the graph to facts that can be verified — not inferred, not paraphrased, not interpolated.

GraphRAG is the general case. The Fact Ledger is the constrained-for-finance case.

Practical Considerations

The main cost of GraphRAG is graph construction. You need to extract entities and relationships from your documents — which requires its own NLP pipeline. For high-stakes domains where retrieval quality matters more than indexing speed, it's worth it. For general Q&A, standard RAG is probably sufficient.