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

> Source: <https://dev.to/machinecodingmaster/stop-using-raw-vector-search-implement-graphrag-with-spring-ai-and-neo4j-no6>
> Published: 2026-05-21 06:35:12+00:00

## 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.