How does Graph RAG differ from traditional RAG?

Traditional RAG retrieves text fragments by semantic similarity (vectors). Graph RAG also retrieves the explicit relationships between the entities mentioned in those fragments, which allows more complete and traceable answers when the question depends on how several elements are connected to one another.

Is it necessary to replace vector RAG with a graph-based one?

No. The dominant trend, and the one we follow at LurNova, is to combine both approaches: vectors to quickly locate relevant content and the graph to enrich that content with context and relationships.

What role does Graph RAG play in complying with the AI Act?

Regulation (EU) 2024/1689 requires high-risk AI systems to have mechanisms for recording and tracing their decisions. A knowledge graph makes it easier to show, visually and auditably, which documents and relationships led to a specific answer, which helps meet that requirement.

The importance of graphs in RAG systems

Over the last few years, RAG (Retrieval-Augmented Generation) systems have marked a turning point in corporate artificial intelligence. Thanks to them, language models stopped being closed islands trained on static data and became connected systems, capable of consulting information in real time and answering with real, verifiable context.

But as companies have begun to use RAG in critical environments (compliance, healthcare, industry, banking, among others), a limitation has become evident: most current implementations treat information as isolated fragments of text, without taking into account how those fragments relate to one another.

Imagine reading a novel by picking pages at random, without knowing which character is linked to whom or which events follow on from each other. You might understand parts of the story, but not the whole plot. That is what happens with many current RAG systems: they retrieve useful sentences, but do not understand the network of relationships that gives knowledge its meaning.

This is where knowledge graphs come in, turning fragmented information into a connected map of entities and relationships. And that is where a new stage begins: Graph RAG.

From fragments to a map of relationships

A traditional RAG system is like a finely tuned search engine: it divides documents into pieces, converts them into vectors and finds those most similar to the user's query. It is fast and useful, but it is still a system that searches for "pieces of text", not concepts or relationships.

By contrast, a Graph RAG system works like a map.

Each node represents an entity (a product, a person, a law, a symptom).
Each edge represents a relationship (supplies, regulates, belongs to, causes).
And the important part: the system can navigate those connections, not just return a fragment of text.

Let us go back to the example of an industrial plant. You ask: "Does this machine comply with European regulation X?".

With traditional RAG, you would get a paragraph from the manual where the name of the machine appears and perhaps a reference to the regulation.
With Graph RAG, you get much more: the connection between the machine and its supplier, that supplier's certifications, the applicable regulation and the relationship with other machines that share the same requirement.

You no longer have an isolated paragraph. You have a network of knowledge that tells the whole story and allows you to make decisions with confidence.

Why graphs make the difference

The contribution of graphs to RAG is not a minor technical detail: it radically changes the way knowledge is built within the company.

Real precision. Graphs make it possible to overcome the limitations of pure semantic search. A vector can say that two sentences are similar, but it does not know whether they are really related in practice. A graph provides that logical layer: "this product is certified by this supplier under this regulation". It is not just semantics, it is knowledge structure.
Complete context. When you ask something complex, the answer is rarely in a single paragraph. Graphs make it possible to bring in connected information: background, implications, dependencies. This prevents the assistant from stopping at a partial answer and gives you a panoramic view of the problem.
Traceability and explainability. In an environment regulated by the EU AI Act (Regulation (EU) 2024/1689), answering is not enough: high-risk systems must be able to reconstruct how a conclusion was reached and keep records of that traceability. Graphs offer exactly that: you can visualise the network of relationships and documents that led to the conclusion. In this way, the assistant goes from being a "black box" to becoming a transparent and auditable system.
Scalability in complex environments. The larger and more connected the knowledge, the more evident the advantage of graphs. In sectors such as healthcare, industry or banking, where information is not linear but a dense network of dependencies, graphs become the only way to navigate that knowledge without getting lost.

Real stories where graphs are decisive

In healthcare

A doctor asks: "What alternative treatments are associated with type 2 diabetes?". A traditional RAG system returns an article in which a specific treatment is mentioned. A Graph RAG system connects that treatment with the clinical studies in which it appears, with the documented side effects, with similar therapies and with hospitals that use it. The doctor does not just get a paragraph: they get a clinical network of evidence that supports the decision.

In industry

A plant manager wants to know: "What impact would a failure in component X on line Y have?". A flat RAG system would return the component's technical manual. A Graph RAG system shows the connection with the machines that depend on that component, with the supplier that provides it, with the safety regulations that require it to be inspected and with the stock available in the warehouse. The plant manager does not receive a text: they receive a map of risks and dependencies that allows them to act immediately.

In law

A corporate lawyer asks: "Which articles of the GDPR affect consent in digital marketing?". A classic RAG system gives one article of the regulation. A Graph RAG system connects that article with previous case law, opinions from authorities and related articles. The lawyer does not just have a legal data point: they have a connected legal view that allows them to argue soundly.

The challenge of building graphs

Of course, integrating graphs is not simple. It requires preliminary work that is often overlooked:

Extraction of entities and relationships. Indexing documents is not enough: you have to identify actors, concepts, processes and how they are linked to one another.
Organisation of knowledge into communities. Reference approaches such as Microsoft Research's GraphRAG group entities into related communities before generating answers, which makes it possible to answer both very specific questions and broad questions about the whole corpus. There are also open-source frameworks and graph databases, such as Neo4j, that make this construction easier without starting from scratch.
Dynamic maintenance. A graph is not static: it must be updated in real time as suppliers, regulations or processes change.
Balance between text and structure. Not everything should be modelled as a graph; for certain queries, vector chunks are still faster and more efficient.
Latency. Querying very complex graphs can increase response times if they are not optimised.

Even so, companies that invest in graphs soon discover that this initial effort becomes a sustainable competitive advantage.

The future is hybrid: vectors + graphs

The most interesting thing is that it is not a matter of choosing between vector RAG and Graph RAG, but of combining them in a hybrid system:

Vectors are fast at locating the text fragments most similar to a query.
Graphs enrich those fragments with the network of relationships that gives them meaning.

A hybrid assistant can retrieve the most relevant paragraph and, at the same time, show how that paragraph connects with other elements that are critical for the company. The result is accurate, complete and traceable answers.

At LurNova we see it clearly

At LurNova we believe that the future of corporate artificial intelligence lies not only in answering questions, but in understanding how knowledge is connected.

That is why we design hybrid RAG solutions in which graphs play a central role. With them we build assistants that:

Answer with greater precision.
Explain where each piece of data comes from.
Scale in environments where information is dense and critical.
Meet the transparency and traceability requirements demanded by European regulation.

Because the real value of AI does not lie in accumulating loose paragraphs, but in weaving a map of knowledge that reflects the reality of your business.

Rodrigo MartínezLinkedIn

Co-founder and CTO of LurNova, with more than 20 years' experience in developing digital technologies. Specialised in designing secure, private and governed artificial intelligence architectures and solutions for organisations.