Vector RAG Versus Graph RAG: Solving The Unstructured Data Problem

Enterprise data has a strange habit of being both everywhere and surprisingly difficult to use. The information an AI system needs might be spread across policy documents, contracts, technical manuals, emails, meeting notes, support tickets and years of internal reports. People inside the business may know that information exists. 

Finding it, connecting it and making it usable by AI is another problem entirely. BARC's 2026 Harnessing Unstructured Data for AI Innovation study gives that problem some scale. Based on 225 responses from data, analytics and AI leaders, it found that 70 per cent of organisations could discover and use less than half of their unstructured data for analytics or AI. 

Only 29 per cent fully knew where their AI-relevant unstructured data was located. Retrieval-augmented generation (RAG) helped close part of this gap by allowing large language models (LLMs) to retrieve information from external data before producing an answer. 

Instead of asking a model to rely only on what it learnt during training, organisations could give it access to their own knowledge. For many teams, that has meant some form of Vector RAG. It works well, it's relatively straightforward to implement and it can be remarkably good at finding information based on meaning rather than exact words.

The difficulty starts when finding the right information isn't enough. Sometimes an AI system needs to know how several pieces of information connect before it can answer correctly. That's why Graph RAG is attracting so much attention. But the real choice isn't as simple as picking the newer architecture. 

It starts with understanding what kind of retrieval problem you're actually trying to solve.

What Is Vector RAG and Where Does It Work?

Vector RAG is built around a fairly simple idea, even if the technology behind it sounds intimidating at first. A document is usually divided into smaller sections called chunks. Each chunk is converted into an embedding, which is a numerical representation of its meaning, and stored in a vector database. 

When somebody asks a question, that question is converted into an embedding too. The system then looks for chunks that sit closest to the question in this mathematical representation of meaning. 

So if an employee searches for "time off after having a baby", vector search could still retrieve a parental leave policy that mostly uses terms like "maternity", "paternity" and "adoption leave". The words don't need to match perfectly because the system is looking for semantic similarity.

That makes Vector RAG particularly effective when the answer already exists in one or a few relevant passages. An enterprise AI assistant could use it to retrieve clauses from contracts, answer questions from product documentation, search internal policies or find troubleshooting instructions across thousands of support articles. 

The information might be spread across a very large collection, but the retrieval problem itself is still relatively contained: find the text that's most relevant to this question. There's nothing outdated about that approach. 

Even Microsoft's current GraphRAG implementation includes a basic vector search option for queries that are best answered by conventional top-k retrieval. The trouble begins when relevance and completeness stop being the same thing.

Where Vector Retrieval Starts To Struggle

Imagine a manufacturer asking its AI system a slightly different kind of question: Which products sold in Germany would be affected if Supplier A's factory closed? There might not be a document anywhere in the organisation containing the complete answer. One source could tell you that Supplier A makes Component B. 

Another could show which products use Component B. Sales records could identify where those products are sold, while a contract somewhere else explains whether another supplier can replace it. Vector search might retrieve several useful pieces because they're semantically related to the question. But the answer depends on connecting them.

Supplier A connects to Component B. Component B connects to Products C and D. Those products connect to customers and markets. Contracts connect possible alternatives back to the supplier relationship. The problem has shifted from finding relevant information to reasoning across relationships.

Researchers behind the MultiHop-RAG benchmark identified this as a particular weakness in conventional RAG systems. Multi-hop queries require systems to retrieve and reason across several pieces of supporting evidence, and their experiments found existing RAG approaches performed poorly on these more complex questions.

That doesn't mean Vector RAG can't support multi-step reasoning. Retrieval systems can be improved with metadata, re-ranking, query decomposition and other techniques. But semantic similarity doesn't inherently describe the relationships connecting the information it retrieves.

Graph RAG gives those relationships a more explicit role.

What Does Graph RAG Do Differently?

A graph represents information as things and the connections between them. Those things might be customers, suppliers, products, employees, applications, incidents or policies. The connections explain how they're related. An employee reports to a manager. A supplier provides a component. 

A component belongs to a product. An application depends on another service. A knowledge graph captures these entities and relationships in a form software can query and follow. Graph RAG brings that structure into retrieval. Instead of only asking which text looks most similar to a question, the system can use relationships to gather connected context.

Microsoft's GraphRAG implementation shows how far this can go. Its indexing pipeline extracts entities, relationships and claims from unstructured text, groups related entities into communities, creates summaries of those communities and also embeds text into vector space. The result supports different kinds of questions.

Microsoft's Local Search combines knowledge graph data with the original source material when answering questions about particular entities and their connections. Global Search works differently, using summaries created across graph communities to answer questions about an entire dataset, such as identifying its major themes.

That second capability solves a different version of the retrieval problem. Asking "What are the main themes across five years of customer complaints?" doesn't give vector search an obvious handful of passages to find. The answer has to be built across the collection.

Research behind Microsoft's GraphRAG found substantial improvements over a conventional RAG baseline for the comprehensiveness and diversity of answers to this kind of global question across datasets of roughly one million tokens.

There isn't one universal Graph RAG architecture, though. Different systems construct, retrieve and use graph information differently. And adding relationships to retrieval comes with its own price.

Graph RAG Isn't Automatically Better

It's tempting to look at Graph RAG as Vector RAG with extra intelligence added. If it can retrieve relevant information and understand relationships, why wouldn't you use it for everything? Because somebody has to create and maintain those relationships first.

Microsoft's standard GraphRAG pipeline uses an LLM to extract entities, identify relationships, consolidate their descriptions and generate summaries. That creates a richer representation of the underlying information, but it also adds processing and cost before anybody asks the first question.

Microsoft estimates graph extraction accounts for roughly 75 per cent of the indexing cost in its Standard GraphRAG method. Its FastGraphRAG option reduces that expense by replacing some LLM-driven work with conventional natural language processing, but the resulting graph is noisier and contains less detailed entity and relationship information.

Enterprises also have to deal with entity resolution, relationship modelling and keeping the graph accurate as the organisation changes. A supplier gets replaced. An employee moves teams. A product is retired. A policy changes. If relationships influence AI answers, stale relationships become their own data-quality problem.

Nor does research support a simple claim that Graph RAG always produces better answers. An August 2026 preprint comparing several RAG architectures across different embedders, datasets and evaluation methods found the outcome changed according to the corpus and type of question being tested. 

GraphRAG outperformed conventional RAG across the Wikipedia multi-hop dataset used in the study, tied it for some shorter requirements questions, while agentic approaches performed best on some longer multi-hop queries. The researchers also found that changing how citation quality was measured could change the apparent ranking between architectures.

The lesson for enterprise teams is useful precisely because it isn't dramatic: there isn't one retrieval architecture that's best for every question. And that's increasingly changing what production RAG looks like.

Why Hybrid RAG Is Becoming More Important

Saying "Vector RAG versus Graph RAG" makes the two approaches sound like competing choices. Current development suggests the boundary is becoming much less tidy. Microsoft GraphRAG still embeds text and includes conventional vector retrieval alongside Local, Global and DRIFT search. 

Neo4j's GraphRAG tooling can start with vector similarity and then traverse connected graph data from the results. TigerGraph is making the same distinction in its current enterprise guidance, positioning vector retrieval for broad semantic lookup and graph retrieval for questions involving connected facts, while arguing for hybrid architectures where workloads require both.

Taken together, this points towards a more practical model: use the retrieval method that fits the question. A hybrid system might use vector search to find the most relevant document or entity, then follow graph relationships to gather the connected information needed for an answer. 

More advanced systems can route different questions through different retrieval methods rather than treating every request the same way. That becomes particularly interesting as agentic AI takes on tasks involving several steps. 

An agent investigating an operational issue may need to search documentation, identify an affected system, trace its dependencies and check related policies before deciding what happens next. Agents don't automatically require Graph RAG. Plenty of agentic tasks won't involve complex relationships at all. 

But as the number of sources, dependencies and reasoning steps grows, retrieval has to do more than simply hand the model a few passages that look relevant. So the architecture decision needs to start with the workload, not the technology.

How To Choose Between Vector RAG and Graph RAG

There isn't much value in deploying a more complicated retrieval system because it looks better on an architecture diagram. The useful question is what your users and AI systems actually need to find out.

Start with the questions your AI needs to answer

Look at real queries rather than hypothetical capabilities. Are users mainly asking for policies, definitions, instructions and individual facts? Vector retrieval may already give them what they need.

Do questions regularly cross documents, systems or entities? Do users need to trace dependencies, identify patterns across a collection or understand how several facts connect? That's where graph-based retrieval deserves closer attention.

The number of documents isn't the deciding factor. A vector index can search enormous collections. What changes the requirement is the shape of the answer.

Decide whether relationships are part of the answer

A simple way to separate the options is to ask what the retrieval system has to do before the LLM can respond.

The point isn't to classify every possible question perfectly before building anything. It's to understand which types make up enough of your workload to justify another retrieval capability.

Weigh better context against operational cost

From there, the decision becomes an enterprise architecture question. Consider whether you already have mature graph data, how difficult your relationships will be to model, how quickly that information changes and what it will cost to maintain. Look at latency and query volumes too, particularly where more complex retrieval introduces additional processing.

Accuracy and explainability requirements will also change the calculation. A customer FAQ assistant and an AI system tracing financial relationships aren't carrying the same risk if retrieval goes wrong. Most importantly, test with the questions your organisation will actually ask.

Generic benchmarks can tell you whether an approach has potential. They can't tell you whether it works across your contracts, product structures, dependencies, terminology and data quality. Your own representative queries can.

Final Thoughts: The Right RAG Architecture Depends On The Question

Making unstructured data useful to enterprise AI isn't simply a matter of putting documents somewhere a model can search them. Sometimes search is enough. Vector RAG remains a strong choice when semantic similarity can reliably bring the right context into the model. 

When an answer depends on understanding how information connects, Graph RAG adds a different capability by making those relationships available during retrieval. The mistake would be treating either architecture as the default because one feels simpler or the other feels more advanced.

Hybrid and routed approaches point towards something more flexible. Instead of forcing every request through the same retrieval process, enterprise AI can increasingly match retrieval to the task it's being asked to perform. That becomes more important as AI moves from answering isolated questions towards handling longer, more connected pieces of work. 

The challenge won't only be knowing what information the organisation has. It will be knowing when the AI needs to find it, when it needs to connect it and when it needs to reason across both. Which leaves enterprise teams with a useful question of their own: was their current RAG architecture designed for the questions their AI needs to answer now, or for the much simpler ones it started with?

As retrieval, knowledge architecture and agentic AI continue to converge, EM360Tech will keep following how those choices change the way enterprise systems access and use the information organisations already depend on.