# GraphRAG

> Retrieval-Augmented Generation approach that combines graph databases with vector search for enhanced context retrieval. Uses graph structures to capture relationships between entities while leveraging vector embeddings for semantic search.

- **URL:** https://docs.cloud.google.com/spanner/docs/graph/perform-vector-similarity-search
- **Category:** Concepts & Definitions
- **Tags:** Rag, Graph Database, hybrid-approach
- **Updated:** 2026-03-26 13:53
- **Canonical page:** https://vectordb.ever.works/ar/items/graphrag

## Details

## Overview

GraphRAG is an advanced Retrieval-Augmented Generation approach that combines graph databases with vector search capabilities. It leverages graph structures to capture relationships between entities while using vector embeddings for semantic similarity search.

## How It Works

### Dual Representation
1. **Graph Structure**: Captures entities and their relationships
2. **Vector Embeddings**: Enable semantic similarity search
3. **Combined Retrieval**: Uses both graph traversal and vector search

### Enhanced Context
By traversing the graph structure, GraphRAG can retrieve not just similar documents but also related entities and their connections, providing richer context for generation.

## Key Benefits

### Relationship-Aware Retrieval
- Captures connections between entities
- Enables multi-hop reasoning
- Provides structured context beyond simple similarity

### Improved RAG Quality
- More comprehensive context retrieval
- Better handling of complex queries
- Enhanced accuracy for knowledge-intensive tasks

## Platform Support (2026)

Several platforms now support GraphRAG:
- **Google Spanner**: Vector search with graph capabilities
- **Neo4j**: Vector indexes integrated with graph database
- **Microsoft**: GraphRAG framework and implementations

## Implementation Approaches

### Vector Search in Graph Databases
Add vector search capabilities to existing graph databases (Neo4j, Spanner Graph)

### Hybrid Systems
Combine separate vector databases with graph databases

### Unified Platforms
Platforms that natively support both graph and vector operations

## Use Cases

- Knowledge graph question answering
- Multi-hop reasoning tasks
- Entity-relationship aware search
- Complex domain knowledge retrieval
- Enterprise knowledge management

## Recent Developments (2025-2026)

In 2025, Google Spanner added functionality for using graphs with vector search specifically for GraphRAG use-cases, with "a lot more exciting capabilities lined up for 2026."

## Comparison with Traditional RAG

### Traditional RAG
- Vector similarity search only
- Document-level retrieval
- Limited relationship awareness

### GraphRAG
- Combined graph + vector search
- Entity and relationship retrieval
- Multi-hop reasoning capability
- Structured knowledge integration

## Architecture Patterns

1. **Graph-First**: Start with graph traversal, use vectors for refinement
2. **Vector-First**: Initial vector search, expand via graph connections
3. **Parallel**: Query both systems and merge results
4. **Hybrid Scoring**: Combine graph distance and vector similarity

## Pricing

Implementation-dependent - varies by platform and architecture chosen.

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