# Knowledge Graphs vs. Vector RAG: The Difference | APRIXITY > Source: https://aprixity.run/en/wissen/framework/knowledge-graphs-vs-vector-rag ### Cookie Settings We use cookies to improve your experience and optimize our service. #### Essential Always active These cookies are required for the basic functionality of the website. #### Analytics & Performance These cookies help us understand how you use our website to improve it. - Google Analytics 4 (visitor statistics) - Page views and navigation - Interactions and events Accept AllSave PreferencesReject All For more information, see our [Privacy Policy](/en/privacy) [Skip to main content](#main-content) [![APRIXITY Logo](/logo-64.png)APRIXITY](/en/) [AI consulting](/en/ai-consulting-hannover)[Renovation](/en/sanierung)[Real Estate](/en/osiris)MethodKnowledge[About me](/en/#about) 🇺🇸English [Check](/en/bottleneck-check)[Conversation](https://cal.aprixity.run/melvin-aprixity.run/catch-up) 🇺🇸English [View All](/en/wissen) Framework For Customers Intermediate # Knowledge Graphs vs. Vector RAG: The Fundamental Difference When graph methods beat classic RAG, when they do not, and what the benchmarks actually measure. 5 min read1381 viewsUpdated: 7/30/2026 ## TL;DR Vector RAG finds similar text, knowledge graphs store relationships. On plain fact lookup classic RAG is ahead (60.9 to 60.1 on GraphRAG-Bench). On questions spanning several steps graph methods win (53.4 to 42.9). The kind of question decides, not the technology. ## Key Takeaways ### Vector RAG Findet ähnliche Texte. Stark beim Nachschlagen einzelner Fakten, schwach bei Verkettungen. ### Knowledge Graphs Speichern, wer mit wem und was mit was verbunden ist. Der Pfad zur Antwort bleibt sichtbar. ### Der Vorteil ist eng Graphverfahren gewinnen beim mehrstufigen Schließen, nicht beim einfachen Faktenabruf. ### Entscheidungskriterium Nicht die Datenmenge entscheidet, sondern ob eure Fragen Beziehungen überspringen müssen. ## 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 - Simple to implement - Strong when the answer lives in one passage ("What does the contract say about X?") ### Weaknesses - **No relationships:** "Customer Miller" and "Deal Mozart Street" are separate chunks. The system does not know they are connected - **Lost context:** Chunks are isolated from the document they came from - **Chaining is expensive:** "Show me all deals from Hamburg customers" takes several steps. Vector RAG can do it through repeated retrieval, but it needs multiple passes - **Silent failure:** If the right chunk is not retrieved, nothing signals that. The LLM answers anyway ## 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 \[Notary: Schmidt\] \[Document: Energy certificate\] **Traversal instead of search.** To find out why the deal stalled, the system follows the edges: 1. Find customer Miller 2. Follow INTERESTED\_IN to property Mozart Street 3. Follow MISSING\_DOCUMENT to the energy certificate 4. → "The deal is stalled because the energy certificate is missing" ### 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 absence:** Either the relationship exists or it does not. When it is missing, the system says so instead of guessing - **Traceability:** The path through the graph shows how the answer was reached ### 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 - **No semantic matching:** Finds exact matches, not "similar" concepts - **Weaker at plain lookup:** When the answer sits in one paragraph, the graph is a detour ## 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 wins at plain fact retrieval.** GraphRAG-Bench compares graph methods and classic RAG on identical tasks. On straightforward fact lookup, classic RAG with reranking scores 60.9, the best graph method 60.1, and Microsoft's GraphRAG in global mode 36.9. **Chained questions reverse the order.** On the same data, for questions that must connect several facts: 53.4 for the best graph method against 42.9 for classic RAG. About ten points, on one task type. **The gain depends heavily on the dataset.** HippoRAG lifts Recall@5 on 2WikiMultiHopQA from 68.2 to 89.5. On MuSiQue, also multi-hop, only from 49.2 to 52.1. Same method, very different effect. **Structured representation beats raw database querying.** 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. **Give classic RAG a fair token budget and the gap shrinks.** This is the most important objection and it rarely travels with the numbers. Graph methods pull far more context per query. Han et al. gave classic RAG the same budget: on MultiHop-RAG it rises from 65.8 to 71.0, level with the graph method at 71.2. What survives is temporal and comparative questions: 43.7 for budget-matched classic RAG against 49.1 for the graph. **On token usage the common claim is upside down:** graph methods generally use more tokens, not fewer. On GraphRAG-Bench, classic RAG uses 879 tokens per query, Microsoft's GraphRAG 38,707 in local mode and 331,375 in global mode. Microsoft's only documented saving is against map-reduce summarization of source texts (9x to 43x), not against vector search. **Treat LLM-judged win rates with care.** Zeng et al. tested the standard evaluation protocol by running a method against an identical copy of itself. The protocol declared one copy the winner 90 to 10. Position and length effects manufacture advantages that are not there, and every win rate in this literature, Microsoft's included, inherits that caveat. One caveat that rarely travels with the numbers: Microsoft scores answer quality with a second LLM as judge, on comprehensiveness, diversity and empowerment. On directness, Vector RAG wins every comparison. ## Which one do you need? Use case Vector RAG GraphRAG Questions about document contents Strong Comparable Looking up single facts Strong Somewhat weaker Questions spanning several steps Weak Core strength CRM/ERP integration Limited Native Traceable answers Weak Path visible Setup complexity Simple Higher ## The question decides, not the technology If your users search documents and look up single facts, Vector RAG is enough and it is the cheaper option. A knowledge graph earns its cost when your questions regularly hop across several steps, when you need to trace how an answer was reached, or when a wrong result costs more than a missing one. For CRM and ERP data that is usually the case, because the value sits in the connections rather than in the individual records. But it is a decision per use case, not a general superiority. The strongest production evidence comes from LinkedIn. Their support team was split at random, one group with graph-backed access to past tickets and one without. Median handling time per case fell from seven hours to five, a 28.6% reduction. The construction matters: the graph modelled the structure of the tickets and their explicit links, not entities guessed out of prose. A brokerage's case files have that same shape. Osiris uses Neo4j as its knowledge graph and combines both methods. [→ How Osiris implements GraphRAG](/en/osiris) ## Frequently Asked Questions ### Can I not just load all my documents into ChatGPT? Only up to a point. The context window is finite, and position inside it is not neutral: Liu et al. show models use information in the middle of long contexts worse than at either end. For GPT-3.5-Turbo, performance there fell below its own score with no documents at all. ### Is GraphRAG not much more work to implement? Yes, noticeably: schema design and entity extraction are added steps. It pays off when your questions chain several facts together. For plain lookup it does not, and benchmarks put classic RAG slightly ahead there. ### Is GraphRAG really 3.4x more accurate than Vector RAG? No. The figure circulates widely but cannot be sourced. It traces back to a benchmark comparing text-to-SQL against text-to-SPARQL, where the gap was 3x, and was restated along the way as 3.4x and as a comparison against Vector RAG. No vector retriever appeared in that experiment. ### Which knowledge graph database should I use? Neo4j is the industry standard for property graphs. For RDF/SPARQL there is Amazon Neptune or Stardog. At Osiris we use Neo4j for the Cypher query language and the LLM integration. ## Sources 1. [When to use Graphs in RAG: A Comprehensive Analysis (GraphRAG-Bench)](https://arxiv.org/abs/2506.05690) - Xiang et al., ICLR 2026 (2025) 2. [HippoRAG: Neurobiologically Inspired Long-Term Memory for LLMs](https://arxiv.org/abs/2405.14831) - Gutiérrez et al., NeurIPS 2024 (2024) 3. [A Benchmark to Understand the Role of Knowledge Graphs on LLM's Accuracy for Question Answering on Enterprise SQL Databases](https://arxiv.org/abs/2311.07509) - Sequeda, Allemang, Jacob (2023) 4. [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://arxiv.org/abs/2404.16130) - Edge et al., Microsoft Research (2024) 5. [Lost in the Middle: How Language Models Use Long Contexts](https://arxiv.org/abs/2307.03172) - Liu et al., TACL (2024) ### Use Cases - RAG Architektur-Entscheidung - LLM Integration Planning - Knowledge Base Design ### Prerequisites - Grundverständnis von Embeddings - Basis-Wissen zu LLMs Next step ## Knowledge becomes valuable inside a process. Find your binding constraint or explore the method through the Academy. 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