RAG (Retrieval-Augmented Generation) in Spring AI: Grounding AI in Your Data

RAG (Retrieval-Augmented Generation) in Spring AI: Grounding AI in Your Data

RAG separates hallucinating chatbots from trustworthy AI systems. Instead of relying solely on LLM training data, RAG retrieves relevant information from your knowledge base to ground responses in facts.

Spring AI provides elegant abstractions for building RAG systems, from embedding documents to retrieval and response generation.

The RAG Architecture

User Query → Embedding → Vector Search → Retrieve Context → LLM + Context → Grounded Response

Why RAG Matters

Without RAG, the AI generates plausible-sounding but potentially outdated information. With RAG, responses are grounded in your actual data.

Implementing RAG with Spring AI

@Service
public class RAGService {
    private final ChatClient chatClient;
    private final VectorStore vectorStore;

    public String answerWithRAG(String userQuery) {
        // Retrieve relevant documents
        List<Document> relevant = vectorStore.similaritySearch(userQuery, 5);

        // Format context from retrieved documents
        String context = formatContext(relevant);

        // Create prompt with query + context
        String prompt = buildRAGPrompt(userQuery, context);

        // Generate response grounded in retrieved context
        return this.chatClient
            .prompt()
            .user(prompt)
            .call()
            .content();
    }

    private String buildRAGPrompt(String query, String context) {
        return String.format(
            "Answer using ONLY the provided context. If not found, say 'I don't have info on this'.\n\n" +
            "Context: %s\n\n" +
            "Question: %s",
            context, query
        );
    }
}

Chunking Strategy

Large documents need to be split into manageable chunks:

public List<String> chunkDocument(String text) {
    String[] sentences = text.split("(?<=[.!?])\\s+");
    List<String> chunks = new ArrayList<>();
    StringBuilder chunk = new StringBuilder();

    for (String sentence : sentences) {
        if (chunk.length() + sentence.length() > 512) {
            chunks.add(chunk.toString());
            chunk = new StringBuilder();
        }
        chunk.append(sentence).append(" ");
    }

    return chunks;
}

Hybrid Search (Semantic + BM25)

Combine vector search with keyword matching for better results:

public List<Document> hybridSearch(String query) {
    // Semantic search via vectors
    List<Document> semantic = vectorStore.similaritySearch(query, 5);

    // Lexical search via keywords  
    List<Document> lexical = repository.searchByKeywords(query, 5);

    // Merge and rank by combined score
    // Return top results
}

Best Practices

1. Choose the right embedding model - balance quality vs speed
2. Add metadata tags - source, date, category for better retrieval
3. Monitor retrieval quality - track precision and recall
4. Implement fallbacks - handle cases with no relevant docs
5. Refresh embeddings - reindex when source docs change
6. Attribute sources - always cite where information came from

Conclusion

RAG transforms LLMs from hallucination machines into grounded, trustworthy assistants. By augmenting prompts with retrieved context, responses are factual, up-to-date, and traceable to sources.