Is graph RAG better than vector RAG?

Not in general. Graph RAG is better for multi-hop and relationship questions, where facts from several documents must be connected. Vector RAG is better, and much cheaper, for direct lookups, which make up most questions. Most production systems use vector search by default and add a graph where evaluation shows relationship questions failing.

Do I need a graph database for knowledge graph RAG?

Not necessarily. A dedicated graph database helps with large graphs and complex traversals. Smaller graphs can live in a relational database with an edges table, or in memory. Choose storage by graph size and query patterns, and keep the link from each fact back to the passage it was extracted from.

How is the knowledge graph built?

Usually by running a language model over each document to extract entities and relationships, then merging duplicate entities across documents. Some domains can use structured sources directly, such as org charts or service dependency manifests, which are more accurate than extraction. Mixing structured sources with extracted facts often works best.

GraphRAG is a widely used name for knowledge graph based retrieval augmented generation, and also the name of specific open-source implementations. The common idea is extracting entities and relationships from documents into a graph, sometimes with summaries of related clusters, and using that structure to retrieve context for answers that span many documents.

What Is Knowledge Graph RAG?

Knowledge graph RAG is retrieval augmented generation that uses a graph of entities and their relationships, rather than only text chunks, to find context. The system extracts entities and relations from documents, stores them as a graph, and at query time traverses connections to gather related facts. It suits multi-hop and relationship questions that similarity search handles poorly.

How it works

Standard RAG splits documents into chunks, embeds them, and retrieves the chunks most similar to the question. Knowledge graph RAG adds a structured layer.

1. Extraction

A model reads each document and extracts entities, such as people, companies, products, or components, and the relationships between them, such as owns, depends on, or reported to. Each extracted fact keeps a link back to the text it came from.

2. Graph construction

Entities become nodes and relationships become edges. The same entity mentioned in many documents is merged into one node, which is where the graph gains its power, and where much of its difficulty lies.

3. Retrieval

At query time the system identifies entities in the question, finds them in the graph, and traverses nearby connections to collect related facts. Some approaches also build summaries of clusters of related entities for broad questions about a whole corpus.

4. Generation

The retrieved facts, often with the source passages behind them, are given to the model to compose an answer.

What it is good at

Multi-hop questions. Which suppliers of our delayed components also supply our main competitor? Answering requires joining facts from different documents. Similarity search may find each fact separately but rarely assembles the chain. A graph traversal follows it directly.

Relationship questions. Who reports to whom, which services depend on this database, which contracts reference this clause. The answer is a structure, and the graph holds structure.

Corpus-wide themes. Questions about a whole collection, such as the main risks mentioned across all incident reports, can use summaries of graph communities rather than the handful of chunks vector search returns.

What it costs

Extraction is expensive. Running a model over every document to pull out entities and relations costs tokens and time, and must be repeated as documents change.

Entity resolution is hard. Is ACME Corp the same as Acme Inc? Is the payments service the same as payment-svc? Mistakes here either split one entity into several or merge different ones, and both corrupt answers.

Errors look authoritative. A wrong edge produced during extraction is retrieved as a fact. Unlike a slightly off text passage, which the model can read critically, a structured fact is easy to trust.

Many questions do not need it. Asking what a policy says about refunds is a lookup, and vector search handles it well at a fraction of the cost.

A worked example

Take an engineering organisation with design documents, incident reports, and service manifests. Someone asks: which teams would be affected if the customer database went down?

Vector search retrieves passages that mention the customer database. It may find the two services that document the dependency in prose, and miss the four whose dependency appears only in a manifest or an incident report about something else.

A graph built from those sources holds edges such as service reads from database and team owns service. The query finds the database node, follows reads-from edges back to every dependent service, then follows owns edges to their teams. The answer is a complete list, and each edge links to the document that established it, so an engineer can check any surprising entry.

The same graph does nothing useful for a question like what is our on-call escalation policy. That is a passage lookup, and vector search answers it directly.

When to use it

Start with good vector retrieval and measure which questions fail. If failures cluster around multi-hop and relationship questions, add a graph for the entities that matter, often a narrow domain such as systems and dependencies or people and organisations, rather than extracting everything. Keep links from every graph fact back to its source passage, so answers can be checked and extraction errors found. Re-run extraction when source documents change, or the graph drifts from the text it claims to represent.

Frequently asked questions

Is graph RAG better than vector RAG?: Not in general. Graph RAG is better for multi-hop and relationship questions, where facts from several documents must be connected. Vector RAG is better, and much cheaper, for direct lookups, which make up most questions. Most production systems use vector search by default and add a graph where evaluation shows relationship questions failing.
Do I need a graph database for knowledge graph RAG?: Not necessarily. A dedicated graph database helps with large graphs and complex traversals. Smaller graphs can live in a relational database with an edges table, or in memory. Choose storage by graph size and query patterns, and keep the link from each fact back to the passage it was extracted from.
How is the knowledge graph built?: Usually by running a language model over each document to extract entities and relationships, then merging duplicate entities across documents. Some domains can use structured sources directly, such as org charts or service dependency manifests, which are more accurate than extraction. Mixing structured sources with extracted facts often works best.
What is GraphRAG?: GraphRAG is a widely used name for knowledge graph based retrieval augmented generation, and also the name of specific open-source implementations. The common idea is extracting entities and relationships from documents into a graph, sometimes with summaries of related clusters, and using that structure to retrieve context for answers that span many documents.