# What is Graph RAG? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Agentic Systems  
> **Canonical Citation:** [https://spidits.com/ai-glossary/graph-rag](https://spidits.com/ai-glossary/graph-rag)

## Definition
Graph RAG (Graph Retrieval-Augmented Generation) is an advanced retrieval technique that couples vector similarity search with structured Knowledge Graphs. It extracts entities and relationships from documents, building a network to answer complex, global queries.

## Architectural Mechanics & Deep Dive
Graph RAG (Graph Retrieval-Augmented Generation) is an advanced RAG methodology that replaces flat vector databases with structured knowledge graphs. By indexing documents as entities and relationships, Graph RAG enables LLMs to perform complex, multi-hop reasoning and holistic summaries of massive codebases or corporate archives, connecting disparate pieces of information.

## Industry Applications & Real-World Use Cases
Complex document analysis, multi-document summarization, and connection mapping.

## Frequently Asked Questions

### How does Graph RAG differ from standard vector RAG?
Standard RAG searches for isolated semantic text chunks. Graph RAG understands the global network of entities and how they relate across all documents.

### What is the main challenge of Graph RAG?
The high upfront computation cost and complexity of building and updating the knowledge graph using an LLM.

## Related Terminology
- [rag](https://spidits.com/ai-glossary/rag)
- [embedding](https://spidits.com/ai-glossary/embedding)
- [semantic-search](https://spidits.com/ai-glossary/semantic-search)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*