# Graph RAG

> RAG architecture that combines knowledge graphs with vector databases, enabling multi-hop reasoning, relationship traversal, and structured knowledge representation for more accurate and explainable AI responses.

- **URL:** https://machinelearningmastery.com/vector-databases-vs-graph-rag-for-agent-memory-when-to-use-which/
- **Category:** Concepts & Definitions
- **Tags:** Knowledge Graph, Rag, relationships
- **Updated:** 2026-03-18 13:02
- **Canonical page:** https://vectordb.ever.works/fr/items/graph-rag

## Details

## Overview

Graph RAG is an architecture that combines knowledge graphs with large language models (LLMs), providing structured memory architecture where entities and their relationships form a graph that the LLM can traverse and reason over.

## Key Advantages

- **Multi-hop Reasoning**: Follow relationship chains across multiple nodes
- **Explainable Retrieval**: Clear paths through knowledge graph
- **Structured Knowledge**: Entities and relationships explicitly modeled
- **Factual Accuracy**: Reduced hallucinations through structured data
- **Relationship-Aware**: Understands connections between concepts

## Graph RAG vs Vector Databases

**Vector Databases**: Best for broad similarity matching and unstructured data retrieval

**Graph RAG**: Excels when:
- Context windows are limited
- Multi-hop relationships are important
- Factual accuracy is critical
- Complex hierarchical structures exist
- Explainability is required

## Architecture Components

1. **Knowledge Graph**: Stores entities and relationships
2. **Vector Embeddings**: For semantic similarity within graph
3. **Query Engine**: Translates natural language to graph traversal
4. **Reasoning Engine**: Performs multi-hop inference

## 2026 Developments

One of the biggest breakthroughs in 2026 is the rise of graph-enhanced vector retrieval, combining the strengths of both approaches for more sophisticated AI applications.

## Use Cases

- Biomedical research (drug-disease relationships)
- Enterprise knowledge management
- Supply chain analysis
- Fraud detection (transaction patterns)
- Scientific paper analysis
- Legal document reasoning

## Implementation Examples

- **Neo4j + Vector Index**: Graph database with vector search
- **KRAGEN**: Graph-of-thoughts with Weaviate
- **LangChain GraphRAG**: Integration with knowledge graphs
- **Microsoft GraphRAG**: Entity extraction and graph construction

## Benefits

- Better handling of complex relationships
- Reduced hallucinations through factual grounding
- Explainable AI through graph visualization
- Temporal reasoning (relationship evolution over time)
- Multi-entity queries with relationship constraints

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