Framework
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Intermediate
Knowledge Graphs vs. Vector RAG: The Fundamental Difference
Why GraphRAG is 3.4x more accurate than classic RAG - and when you need which approach.
15 min read11 viewsUpdated: 12/2/2025
TL;DR
Vector RAG finds similar texts. Knowledge Graphs understand relationships. GraphRAG combines both = 3.4x more accurate, 100x fewer tokens, multi-hop queries possible.
Key Takeaways
Vector RAG
Findet ähnliche Texte - gut für Fragen zu Dokumenten, schwach bei Beziehungen
Knowledge Graphs
Versteht Beziehungen - perfekt für 'Wer kennt wen? Was hängt womit zusammen?'
GraphRAG
Kombiniert beide Ansätze - 3,4x genauer als Vector RAG allein
Business-Relevanz
Für CRM, ERP und Prozesse sind Beziehungen wichtiger als Textähnlichkeit
The Problem with "Simple" RAG
Vector RAG finds similar text passages through semantic search. But when you ask questions like "Why is the deal with customer Miller stalled?" - you need relationship knowledge, not text similarity.
How It Works
- Vector RAG: Converts text to vectors, finds similar chunks by cosine similarity
- Knowledge Graphs: Stores entities and relationships, traverses connections
- GraphRAG: Combines both - relationship knowledge + semantic search = 3.4x accuracy
The Numbers
- Accuracy: 3.4x higher with GraphRAG
- Hallucination reduction: 6-90%
- Token usage: 100x less
- Multi-hop queries: Only possible with Knowledge Graphs
Use Cases
- RAG Architektur-Entscheidung
- LLM Integration Planning
- Knowledge Base Design
Prerequisites
- Grundverständnis von Embeddings
- Basis-Wissen zu LLMs