Neo4j Integration with LangChain for Graph RAG

ShopMax India's data team stores supplier contracts, product warranties, and store inventory relationships in Neo4j. LangChain's Neo4jGraph integration allows an LLM to generate Cypher queries from natural language questions, execute them against Neo4j, and return results as context for a final answer - without analysts knowing any Cypher syntax.

LangChain provides GraphCypherQAChain which takes a natural language question, uses the graph schema to generate a Cypher query, runs it against Neo4j, and feeds the results to an LLM for a final answer. The chain automatically inspects Neo4j's schema (node labels, relationship types, properties) to generate valid queries. You configure it with a Neo4jGraph connection and an LLM.

The below example shows how ShopMax India's support team queries supplier and warranty data in Neo4j using natural language via LangChain's GraphCypherQAChain.

It gives the following output,

Generated Cypher:
MATCH (s:Supplier)-[:SUPPLIES]->(p:Product)-[:HAS_CLAIM]->(c:Claim)
WHERE c.city = 'Bangalore'
RETURN DISTINCT s.name, COUNT(c) AS claim_count
ORDER BY claim_count DESC

Answer: Three suppliers have warranty claims in Bangalore: Sunrise Electronics
(18 claims), Harshad Distributors (11 claims), and Kumar Tech Solutions
(7 claims). Sunrise Electronics has the highest volume, primarily for
laptop and tablet products.

LLM-generated Cypher can contain errors on complex schemas - add allow_dangerous_requests=True only after validating query patterns in a test environment. For ShopMax India, filter the schema exposed to LangChain to only relevant node types by excluding internal audit nodes. Log all generated Cypher queries for auditing and add a query timeout in Neo4j to prevent runaway traversals on large graphs.