GraphRAG: Why Knowledge Graphs Make RAG Actually Work

Retrieval-Augmented Generation solved the hallucination problem for questions that have a clear answer in a specific document chunk. Ask “what is the refund policy?” and vector search retrieves the right paragraph. Done.

But ask “how do our European operations affect our compliance posture?” and standard RAG falls apart. The answer spans multiple documents, requires understanding relationships between entities, and depends on context that no single chunk contains. The system retrieves a few tangentially related paragraphs and the LLM does its best to stitch together something plausible. Sometimes it works. Often it does not.

This is the gap that GraphRAG closes.

The limits of vector-only RAG

Vector similarity search finds text that looks like the query. That is all it does. It converts your question into an embedding, scans a vector store for chunks with similar embeddings, and returns the closest matches. For direct, factual questions this works well enough.

It does not understand entities. It does not understand relationships. It does not understand structure. It cannot follow chains of reasoning across documents. It cannot connect a person mentioned in one document to a project described in another to a risk identified in a third.

Standard RAG retrieves fragments, not understanding. When your question requires synthesizing information across document boundaries, similarity search hits a hard ceiling. No amount of prompt engineering or chunk-size tuning fixes this. The architecture itself is the constraint.

How AOSentry implements GraphRAG

AOSentry’s GraphRAG pipeline starts with automatic entity and relationship extraction. As documents are ingested, the system identifies entities — people, organizations, places, concepts, products, events — and the relationships between them. Each relationship carries a type label and a strength score derived from how prominently and frequently the connection appears in the source material.

The knowledge graph builds itself from your documents. No manual ontology design. No schema mapping. The extraction process uses LLM-powered analysis to identify what matters and how things connect, then structures that information into a traversable graph.

This graph sits alongside the vector store, not instead of it. Both retrieval methods have strengths. The key is combining them.

Community detection and global search

Raw entity-relationship graphs can contain thousands of nodes. To make this structure useful for retrieval, AOSentry applies the Louvain algorithm to cluster related entities into topic communities. These communities represent natural groupings — a set of people, projects, and concepts that are tightly interconnected.

Community detection enables what Microsoft Research calls “global search” — answering questions that require understanding the overall structure of a domain. Questions like “what are the major risk themes across our portfolio?” or “summarize our AI capabilities” cannot be answered by retrieving a few text chunks. They require a map of the territory. Community summaries provide that map.

Each community gets a generated summary that captures its key entities, relationships, and themes. These summaries become retrievable artifacts themselves, giving the system a way to reason about broad topics without scanning every document.

Hybrid retrieval with Reciprocal Rank Fusion

Three retrieval methods run in parallel for every query. Vector similarity search finds relevant text chunks. Graph traversal follows entity relationships from query-relevant nodes to discover connected knowledge. Community search matches the query against topic cluster summaries.

Each method returns a ranked list of results. These lists are merged using Reciprocal Rank Fusion scoring. RRF is simple and effective: it combines rankings from multiple sources without requiring score normalization. A result that appears near the top of two different retrieval methods will rank higher than a result that tops only one. The best evidence surfaces regardless of which method found it.

This hybrid approach means the system does not have to choose between precision and breadth. Vector search provides precision for direct factual queries. Graph traversal provides the multi-hop reasoning path. Community search provides the structural overview. RRF lets the right mix emerge naturally for each question.

Graph traversal for multi-hop questions

Graph traversal is where the real power lives. Starting from entities identified in the query, the system follows relationships outward with configurable depth limits. Each hop discovers connected entities and the documents that mention them.

Ask about a specific vendor and the traversal finds that vendor’s contracts, the projects those contracts support, the compliance requirements those projects must meet, and the regulatory frameworks governing those requirements. Four hops. Four document boundaries crossed. Standard RAG would have returned the vendor’s homepage blurb from your procurement folder.

Depth limits matter. Unconstrained traversal produces noise. AOSentry defaults to two hops for most queries and extends to three or four when the query signals a need for broader exploration. The system balances discovery against relevance automatically.

What this means in practice

For enterprise knowledge bases with hundreds or thousands of documents, GraphRAG changes what questions the AI can answer. It is no longer limited to “what does document X say about topic Y.” It can answer how things relate to each other. How a policy change in one department affects operations in another. How a technical decision connects to a business outcome three layers removed.

This is the difference between a search engine and an analyst. A search engine finds documents. An analyst understands how the pieces fit together, follows threads across sources, and synthesizes a picture that no single document contains. GraphRAG gives the AI that capability.

The effect compounds with scale. The more documents in the system, the richer the knowledge graph, the more relationships available for traversal, and the wider the gap between what GraphRAG can answer and what vector-only RAG cannot.

RAG gave AI access to your documents. GraphRAG gives it understanding of the relationships between them. That is not an incremental improvement. It is a different category of capability.