Vector RAG vs Graph RAG - When to Use Which

ShopMax India's engineering team needs to choose the right retrieval architecture for different AI features. Vector RAG works well for semantic similarity queries like finding products similar to a description, while Graph RAG excels at relationship-based questions like which suppliers have the highest return rates in Delhi. Understanding when to use each prevents over-engineering simple use cases.

Vector RAG converts text chunks into embeddings and retrieves the most similar ones at query time. It is fast to set up and works well when answers live within single document chunks. Graph RAG extracts entities and relationships into a graph and traverses connections at query time. It handles multi-hop questions that span multiple entities and documents but requires more setup and indexing cost.

The below example benchmarks both approaches on ShopMax India query types to show where each retrieval method excels.

It gives the following output,

Vector RAG: ShopMax India accepts laptop returns within 10 days of purchase
with original packaging. A technical inspection is required before refund
approval for all laptops and mobile devices.

Graph RAG: Based on the relationship data, Mehta Electronics leads with
47 returns in Delhi, followed by Harshad Distributors (31) and Kumar Tech
(28). All three primarily supply mid-range Android smartphones.

Use Vector RAG when answers fit in a single document chunk, setup speed matters, or budget is tight. Use Graph RAG when questions require joining information across multiple entities or you need supplier-product-claim chain analysis. For ShopMax India, use Vector RAG for product FAQ and Graph RAG for supplier analytics and warranty chain reporting.