Beyond Vector Search: Structured Knowledge
Standard RAG retrieves text chunks. GraphRAG converts documents into a Knowledge Graph of entities and relationships, then searches the graph structure itself.
Vector RAG vs GraphRAG
| Dimension | Vector RAG | GraphRAG |
|---|
| Data Structure | Flat text chunks in a vector DB | Entities + relationships in a graph DB |
| Query Type | "What does policy X say about Y?" | "How are departments A, B, and C related?" |
| Reasoning | Local (finds relevant passages) | Global (traverses connections across documents) |
| Cost | Low (embed once, search cheaply) | High (LLM extracts entities, builds graph) |
| Best For | Factual Q&A, document search | Complex analysis, entity relationships, summaries |
How GraphRAG Works
- Entity Extraction: An LLM reads every document and extracts entities (people, orgs, concepts) and relationships.
- Graph Construction: Entities become nodes; relationships become edges. Store in Neo4j, Amazon Neptune, or similar.
- Community Detection: Algorithms cluster tightly-connected entities into "communities" (topics/themes).
- Community Summaries: The LLM generates summaries for each community, capturing global themes.
- Query: For local questions, traverse the graph. For global questions, search community summaries.
// GraphRAG Query Example:
// Question: "What are the main research themes across all 50 papers?"
// Vector RAG: Retrieves 5 random chunks, misses the big picture.
// GraphRAG: Returns community summaries covering ALL themes:
{
"communities": [
{ "theme": "Transformer Architecture", "papers": 12, "key_entities": [...] },
{ "theme": "Reinforcement Learning", "papers": 8, "key_entities": [...] },
{ "theme": "Safety Alignment", "papers": 15, "key_entities": [...] }
]
}
💡 Key Insight: Use Vector RAG for specific, local questions ("What is the refund policy?"). Use GraphRAG for global, analytical questions ("What are the key themes across these 200 documents?"). Many production systems use both together.
Practical Tools for GraphRAG
| Tool | Purpose |
|---|
| Microsoft GraphRAG | Open-source reference implementation |
| Neo4j + LangChain | Graph DB with LLM integration |
| LlamaIndex KG Index | Automated knowledge graph construction |
| Amazon Neptune | Managed graph database service |