Knowledge Graphs vs. Vector RAG: The Fundamental Difference

The Problem with "Simple" RAG

RAG (Retrieval Augmented Generation) is the standard way to connect an LLM to external knowledge. Before the model answers, the system retrieves relevant documents and passes them as context.

This works as long as the answer sits inside a single passage. For these questions it does not:

• "Why is the deal with customer Miller stalled?"
• "Which properties have problems similar to Mozart Street?"
• "Who is the notary for the Hamburg deals?"

These need relationship knowledge. They ask about connections spread across several documents, not about one document.

How Vector RAG works

Step 1: Indexing. Documents are split into chunks (say 500 tokens). An embedding model turns each chunk into a vector, a mathematical representation of its meaning.

Step 2: Retrieval. Your question becomes a vector too. The system finds the chunks whose vectors are closest by cosine similarity.

Step 3: Generation. Those chunks go to the LLM as context, and it answers.

Strengths

• Finds semantically similar content, not just keyword matches
• Often a simpler starting point for document search; effort depends on data and requirements
• A suitable starting point when the answer lives in one passage ("What does the contract say about X?")

Weaknesses

• Relationships need logic: Separate chunks do not automatically join "Customer Miller" and "Deal Mozart Street". Additional retrieval or query logic can establish that relationship.
• Possible context loss: Chunking can weaken document-level context; overlap, metadata, and retrieval logic affect the result.
• Chaining needs orchestration: "Show me all deals from Hamburg customers" takes several steps. Vector RAG can do it through repeated retrieval, but it needs multiple passes
• Gap handling: If the right chunk is not retrieved, whether the application signals a gap depends on its evidence and answer logic.

How Knowledge Graphs work

A knowledge graph stores entities as nodes and relationships as edges:

[Customer: Miller] --INTERESTED_IN--> [Property: Mozart Street]
       |                                      |
       |                                      |
 --HAS_NOTARY-->                       --MISSING_DOCUMENT-->
       |                                      |
       v                                      v
[Notary: Schmidt]                   [Document: Energy certificate]

Fictional, simplified example: The following edges illustrate a query. Its result would be a hypothesis to check; the graph path alone does not prove a cause.

1. Find customer Miller
2. Follow INTERESTED_IN to property Mozart Street
3. Follow MISSING_DOCUMENT to the energy certificate
4. → Hypothesis: "The missing energy certificate may be delaying the deal."

Strengths

• Relationship knowledge: Stores who is connected to whom, and what to what
• Chained questions: "Show me all deals whose customer's notary is in Hamburg"
• Explicit gaps: A graph can expose a missing relationship. Whether the application reports the gap depends on its query and answer logic.
• Traceability: An emitted query path can show which nodes and edges were used. By itself, it proves neither cause nor correctness.

Weaknesses

• Needs structure: Unstructured text has to be extracted first
• Schema design: The ontology has to be thought through, and changing it later is expensive
• Semantic search: A plain node-and-edge query needs added search logic or embeddings to find similar terms.
• Lookup overhead: When the answer sits in one paragraph, the added value of a graph structure still needs to be shown.

GraphRAG: both methods together

GraphRAG combines a knowledge graph with vector retrieval: extract the entities in the question, traverse the graph for connected facts, compress that subgraph into context, and let the LLM answer from it.

What the measurements actually show

Many articles put a number like "3.4x more accurate than Vector RAG" here. That figure does not hold up, and we removed it from this page. Here is what can be supported.

Classic RAG leads on plain fact retrieval. Table 2 of GraphRAG-Bench v1 reports, on the Novel dataset and its stated accuracy metric, 60.9 for RAG with reranking and 60.1 for HippoRAG2; Microsoft GraphRAG global appears in a separate row at 36.9. These are results for this dataset and these methods, not a universal ranking.

Chained questions reverse the order on the same Novel dataset. Table 2 of GraphRAG-Bench v1 reports 53.4 for HippoRAG2 and 42.9 for RAG with reranking. The difference is scoped to this task type, metric, and version.

The effect depends on the dataset and retriever configuration. HippoRAG v3 reports Recall@5 for ColBERTv2 versus HippoRAG using Contriever: 68.2 versus 89.5 on 2WikiMultiHopQA and 49.2 versus 52.1 on MuSiQue. This is not a controlled test of graph structure alone.

One benchmark compares two querying approaches. Across 43 questions against an insurance schema, GPT-4 answered 16.7% correctly straight from SQL and 54.2% through a knowledge graph representation of the same data. That comparison is text-to-SQL against text-to-SPARQL, not graph against vector search.

The token comparison is narrowly scoped too. Figure 8 of GraphRAG-Bench v1 reports average prompt tokens per query on the Novel dataset: 879 for V-RAG, 38,707 for Microsoft GraphRAG local, and 331,375 for Microsoft GraphRAG global. This setup establishes neither total operating cost nor a universal token rule.

Treat evaluation by a second language model with care. Zeng et al. v1, section 5.1, compared two identical LightRAG instances and the protocol produced a 90:10 split. The authors attribute it to position and single-trial bias.

Microsoft uses a second language model to judge several answer-quality dimensions in the cited study. Such model judgments depend on the protocol and do not establish a universal ranking.

Which one do you need?

The question decides, not the technology

When users search documents and look up single facts, Vector RAG is often the simpler starting architecture. Cost and suitability depend on the data, queries, quality requirements, and operation. A knowledge graph may fit when questions regularly join several relationships or the used nodes and edges need to be visible.

CRM and ERP data still require a decision per use case. A graph can model relationships explicitly; that does not establish general superiority or a ready-made integration.

The Osiris target architecture described on this website proposes Neo4j for graph-based queries. The actual data scope, connectors, and operating status must be checked for each project.

→ How Osiris implements GraphRAG