LangChain4j – [ Bringing LLM Power to Java Developers ]

OVO

AI development has been exploding in recent years, with frameworks like LangChain becoming the goto tool for building applications powered by large language models (LLMs).

But most of the action has been happening in Python.

If you’re a Java developer, you may have felt left out – until now.

Enter LangChain4j : a first java library that brings the same capabilities (chatbots, Retrieval-Augmented Generation, memory, and more) into the JVM world.

What You Will Learn
  1. 1. What is LangChain4j ?
  2. 2. Prerequisites
  3. 3. Hello World with LangChain4j
  4. 4. Retrieval-Augmented Generation (RAG)
  5. 5. Real-World Use Cases

1. What is LangChain4j ?

LangChain4j is a Java implementation of the LangChain framework, designed to make it easy for developers to integrate LLMs like GPT-4, Claude, or Gemini into their applications.

It gives you:

  • High-level APIs – abstracted interfaces to talk to LLMs like a service;
  • Low-level APIs – direct control for custom use cases;
  • Integrations – with vector databases, embeddings, and memory;
  • Enterprise readiness – perfect for Spring Boot or Quarkus projects.

If you build enterprise applications in Java, this is a game-changer with the power of LLM.

2. Prerequisites

Before coding, make sure you have :

  • Java 17+ (Java 21 recommended);
  • Maven or Gradle;
  • An IDE (IntelliJ IDEA preferred);
  • An LLM provider API key (eg. OpenAI)

Tip : store your API keys in environment variables, not in source code.

3. Hello World with LangChain4j

3.1 High-Level API

The High-Level API is designed to make working with LLMs as simple as possible.

  • You define an interface with methods;
  • Annotate or simply declare the methods;
  • LangChain4j generates an AI-powered implementation behind the scenes.

Think of it like magic glue that turns your Java interfaces into AI assistants.

import dev.langchain4j.service.*;

public class HighLevelExample {

    interface Assistant {
        @SystemMessage("You are a helpful assistant.")
        String chat(@UserMessage String message);
    }

    public static void main(String[] args) {
        // Build the service (LangChain4j generates the implementation)
        Assistant assistant = AiServices.create(Assistant.class,
                OpenAiChatModel.withApiKey(System.getenv("OPENAI_API_KEY")));

        // Ask a question
        String response = assistant.chat("Who won the FIFA World Cup in 2022?");
        System.out.println("AI: " + response);
    }
}
/*
 * Output:
 *     AI: Argentina won the FIFA World Cup in 2022.
 */

3.2 Low-Level API

The Low-Level API gives you full control over prompts, memory, and responses.

  • You call the model directly (generate, chat, embed, etc.);
  • You decide exactly how the request is structured;
  • More verbose, but also more flexible.
import dev.langchain4j.model.openai.OpenAiChatModel;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.chain.ConversationalChain;

public class LowLevelExample {
    public static void main(String[] args) {
        // 1. Create a model
        OpenAiChatModel model = OpenAiChatModel.builder()
                .apiKey(System.getenv("OPENAI_API_KEY"))
                .build();

        // 2. Add memory (stores conversation history)
        MessageWindowChatMemory memory = MessageWindowChatMemory.withMaxMessages(10);

        // 3. Build a conversational chain
        ConversationalChain chain = ConversationalChain.builder()
                .chatLanguageModel(model)
                .chatMemory(memory)
                .build();

        // 4. Ask a question
        String answer = chain.execute("Who won the FIFA World Cup in 2022?");
        System.out.println("AI: " + answer);
    }
}
/*
 * Output:
 *     AI: Argentina won the FIFA World Cup in 2022.
 */

4. Retrieval-Augmented Generation (RAG)

Now let’s step into something enterprise developers really care about : using your own data.

4.1 What is a Vector Database ?

A vector database is a special type of database designed to store and search embeddings.

Embeddings are numerical representations of text, images, or audio.

Example : “Paris is the capital of France.” => [0.12, -0.85, 0.33 …] (a vector in high-dimensional space).

So when a user asks : “capital of France”, the system embeds it into a vector and finds the closest vectors in the database.

Unlike SQL databases (which do keyword search), vector databases perform semantic similarity search.

Similar meaning = similar vectors (close in space).

Popular Vector DBs (all supported by LangChain4j) :

  • Pinecone – managed, scalable, cloud-first;
  • Weaviate – open source, supports hybrid search;
  • Qdrant – fast, open source, Rust-based;
  • Milvus – enterprise-grade, distributed;
  • PGVector – Postgres extension for vectors;
  • MongoDB Atlas Vector Search – built into MongoDB Atlas.

4.2 What is RAG ?

Large Language Models (LLMs) like GPT-4 are powerful, but they have two major limitations :

  • Their knowledge is frozen at the time of training;
  • They sometimes hallucinate (make up facts).

Retrieval-Augmented Generation solves this by combining :

  • A retriever – fetches relevant documents from a knowledge base;
  • The LLM – generates a natural-language answer based on those documents.

Think of it like this :

  • The LLM is a smart librarian;
  • The retriever (vector database) is the library catalog;
  • Together, they let you ask questions in plain English and get answers based on your data.

Flow of RAG :

  • User asks a question;
  • The system turns the question into an embedding (vector);
  • A vector database searches for the most relevant documents;
  • The retrieved documents are sent to the LLM;
  • The LLM synthesizes an answer using the retrieved knowledge.

This means your assistant can answer questions about your company docs, product manuals, policies, or datasets – not just what’s in GPT’s training.

4.3 High-Level API with RAG Example

Here’s how you can build an AI assistant that retrieves knowledge from a document store (like a vector database) and augments responses.

import dev.langchain4j.model.openai.OpenAiChatModel;
import dev.langchain4j.rag.content.Document;
import dev.langchain4j.rag.content.ContentRetriever;
import dev.langchain4j.rag.vectorstore.InMemoryEmbeddingStore;
import dev.langchain4j.rag.vectorstore.VectorStoreRetriever;
import dev.langchain4j.service.*;

import java.util.List;

public class HighLevelRagExample {

    // Define your assistant interface
    interface ResearchAssistant {
        @SystemMessage("You are a research assistant. Use retrieved documents to answer.")
        String ask(@UserMessage String question);
    }

    public static void main(String[] args) {
        // 1. Create the embedding store (vector DB)
        InMemoryEmbeddingStore<Document> embeddingStore = new InMemoryEmbeddingStore<>();

        // (Normally you'd load docs from PDFs, DBs, etc.)
        embeddingStore.add("The capital of France is Paris.");
        embeddingStore.add("The Eiffel Tower is one of the most famous landmarks in Paris.");
        embeddingStore.add("France won the FIFA World Cup in 2018.");

        // 2. Wrap it in a retriever
        ContentRetriever retriever = new VectorStoreRetriever<>(embeddingStore);

        // 3. Build the assistant service with RAG enabled
        ResearchAssistant assistant = AiServices.builder(ResearchAssistant.class)
                .chatLanguageModel(OpenAiChatModel.withApiKey(System.getenv("OPENAI_API_KEY")))
                .contentRetriever(retriever)   // enables RAG automatically
                .build();

        // 4. Ask a question (the model will pull context from docs)
        String answer = assistant.ask("What is the capital of France and a famous landmark there?");
        System.out.println("AI: " + answer);
    }
}
/*
 * Output:
 *     AI: The capital of France is Paris, and a famous landmark there is the Eiffel Tower.
 */

5. Real-World Use Cases

  • Chatbots for customer support;
  • Enterprise knowledge assistants (ask about company policies, docs, manuals);
  • Code helpers for Java dev teams;
  • Summarization tools for long reports.

LangChain4j makes it possible for Java developers to ride the AI wave without leaving the JVM.

Whether you’re building a chatbot, integrating RAG into your enterprise app, or experimenting with AI in your side projects, LangChain4j gives you the tools you need.

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