What is Retrieval-Augmented Generation (RAG) in LangChain4j and how do you build a pipeline?

RAG (Retrieval-Augmented Generation) is the technique of enriching an LLM prompt with relevant external content retrieved from a knowledge base before asking the model to generate a response. It solves the core limitation of LLMs — their knowledge is frozen at training time — by dynamically injecting up-to-date or domain-specific content at inference time.

In LangChain4j, a RAG pipeline has two distinct phases:

Ingestion phase (run once or periodically): Load documents → split into chunks → embed each chunk → store vectors in an EmbeddingStore.

Retrieval phase (at query time): Embed the user query → similarity-search the EmbeddingStore → inject top-K relevant chunks into the prompt → call the LLM.

// --- Ingestion ---
EmbeddingModel embeddingModel = new OpenAiEmbeddingModel.Builder()
    .apiKey(apiKey).modelName("text-embedding-ada-002").build();

EmbeddingStore<TextSegment> store = new InMemoryEmbeddingStore<>();

List<Document> docs = FileSystemDocumentLoader.loadDocuments("./docs");
EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder()
    .documentSplitter(DocumentSplitters.recursive(500, 50))
    .embeddingModel(embeddingModel)
    .embeddingStore(store)
    .build();
ingestor.ingest(docs);

// --- Retrieval at query time via AI Services ---
interface Assistant {
    String answer(String question);
}

Assistant assistant = AiServices.builder(Assistant.class)
    .chatLanguageModel(chatModel)
    .contentRetriever(EmbeddingStoreContentRetriever.from(store))
    .build();

String answer = assistant.answer("What are our refund policies?");

LangChain4j also supports advanced RAG patterns like query compression, re-ranking with a cross-encoder, and multiple content retrievers that are combined via a DefaultRetrievalAugmentor. These address quality issues in naive RAG implementations where retrieved chunks are too generic or poorly ranked.

Invest now in Acorns!!! 🚀
Join Acorns and get your $5 bonus!

Acorns is a micro-investing app that automatically invests your "spare change" from daily purchases into diversified, expert-built portfolios of ETFs. It is designed for beginners, allowing you to start investing with as little as $5. The service automates saving and investing. Disclosure: I may receive a referral bonus.

Invest now!!! Get Free equity stock (US, UK only)!

Use Robinhood app to invest in stocks. It is safe and secure. Use the Referral link to claim your free stock when you sign up!.

The Robinhood app makes it easy to trade stocks, crypto and more.

Webull! Receive free stock by signing up using the link: Webull signup.