Stop Using Raw Vector Search: Implement GraphRAG with Spring AI and Neo4j

Stop Using Raw Vector Search: Implement GraphRAG with Spring AI and Neo4j

If your enterprise AI pipeline is still relying on basic cosine similarity over flat chunked vectors, you are serving hallucination-prone garbage to your users. In 2026, production-grade RAG demands GraphRAG to bridge the gap between raw semantic search and deep, interconnected relational context.

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Why Most Developers Get This Wrong

• 
  Siloing data: Treating knowledge graphs and vector databases as separate infrastructure, which introduces massive double-query latency.


• 
  Blind Cypher generation: Relying on LLMs to write raw Cypher queries without schema constraints, leading to frequent syntax failures in production.


• 
  Ignoring graph depth: Using vector search to retrieve isolated text chunks while ignoring the rich 2-hop or 3-hop relationships that actually define enterprise data.

The Right Way

Implement a hybrid retrieval pipeline where Neo4j acts as both your vector index and graph database, orchestrated by Spring AI's fluent APIs.

• 
  Seed with Vectors: Use Neo4jVectorStore to find the initial "anchor" nodes based on semantic similarity.


• 
  Structured Cypher Generation: Leverage Spring AI's ChatClient with structured output specs to dynamically generate deterministic Cypher path queries based on your schema.


• 
  Contextual Traversal: Query the graph 2-3 hops deep from those anchors to pull highly relevant relational context (e.g., Service -> Depends On -> Database).


• 
  Hybrid Ranking: Merge vector similarity scores with graph centrality metrics to prioritize the final LLM prompt context.

Show Me The Code

Here is how you build a hybrid GraphRAG retrieval pipeline using Spring AI's fluent ChatClient and Neo4jVectorStore:

@Service

public class GraphRagService {

    private final Neo4jVectorStore vectorStore;

    private final ChatClient chatClient;



    public List<String> retrieveContext(String query) {

        // 1. Vector search for anchor nodes

        var anchors = vectorStore.similaritySearch(SearchRequest.query(query).withTopK(3));

        var anchorIds = anchors.stream().map(Document::getId).toList();



        // 2. Spring AI ChatClient generates constrained Cypher query

        String cypher = chatClient.prompt()

            .user("Generate Cypher path retrieval for node IDs: " + anchorIds)

            .call().entity(String.class);



        return executeCypher(cypher); // Returns deep relational context

    }

}

Key Takeaways

• Flat vectors lose relationships; GraphRAG preserves enterprise domain semantics.


• Spring AI's ChatClient simplifies Cypher generation when combined with strict schema prompts.


• Neo4j's native vector index allows you to perform both vector and graph operations in a single database round-trip.