Amazon Neptune for Graph RAG

ShopMax India's AWS-based data warehousing team needs Graph RAG on top of product catalog and supplier relationship data stored in Amazon Neptune. Neptune is AWS's managed graph database supporting openCypher queries. LangChain provides a Neptune Analytics integration that lets an LLM generate and execute openCypher queries for natural language analytics over graph data.

Amazon Neptune Analytics supports openCypher which LangChain uses for graph RAG via NeptuneAnalyticsCypherQAChain. You connect via LangChain's NeptuneAnalyticsGraph, expose the schema to the LLM, and the chain handles the full query-execute-answer cycle. Neptune manages graph storage and traversal at scale without infrastructure management on the ShopMax India team.

The below example shows how ShopMax India's AWS team queries product-supplier relationships in Amazon Neptune Analytics using natural language.

It gives the following output,

Q: Which product categories have the most returns in Chennai?
A: In Chennai, televisions lead with 34 returns, followed by smartphones
(28 returns) and laptops (19 returns). Most TV returns cite packaging
damage on delivery.

Q: List suppliers who provide both TVs and laptops to ShopMax India
A: Three suppliers provide both product categories: Mehta Electronics,
Sunrise Distributors, and Kumar Tech Solutions. Kumar Tech Solutions
joined the laptop category in January 2026.

Neptune charges per query and per GB stored - keep graph data lean by storing only entities and relationships needed for analytics rather than full document text. For ShopMax India, sync Neptune daily from the operational database rather than in real time. Use Neptune's built-in graph algorithms like PageRank and community detection to score supplier reliability, which the RAG agent can surface in answers to analyst queries.