What is LangChain4j's support for graph-based RAG or knowledge graph integration?

Standard vector similarity RAG retrieves semantically similar text chunks, but it struggles with multi-hop reasoning — questions like "What are all the direct reports of the manager of the product that had the most returns in Q3?" require traversing multiple relationships, not just finding similar text. Graph-based RAG addresses this by integrating a knowledge graph (like Neo4j) as a content retriever alongside or instead of a vector store.

LangChain4j supports this through the ContentRetriever abstraction. You can implement a Neo4jContentRetriever (or similar) that translates the user's natural language query into a Cypher query using the LLM, executes it against Neo4j, and returns the structured results as text for context injection:

class Neo4jContentRetriever implements ContentRetriever {
    private final Driver neo4jDriver;
    private final ChatLanguageModel queryGeneratorModel;

    @Override
    public List<Content> retrieve(Query query) {
        // Step 1: LLM generates Cypher from natural language
        String cypher = queryGeneratorModel.generate(
            "Convert this to a Cypher query: " + query.text()
        );

        // Step 2: Execute against Neo4j
        try (Session session = neo4jDriver.session()) {
            Result result = session.run(cypher);
            String resultText = result.list().toString();
            return List.of(Content.from(resultText));
        }
    }
}

// Use alongside vector retrieval
RetrievalAugmentor augmentor = DefaultRetrievalAugmentor.builder()
    .contentRetriever(new Neo4jContentRetriever(driver, chatModel))
    .build();

The pattern is often called "GraphRAG" or "Text2Cypher RAG". For production, add query validation (reject Cypher that includes WRITE operations), result size limits, and retry logic for LLM-generated invalid Cypher. LangChain4j's modular ContentRetriever design makes this a clean extension point — no framework modification required.