Enterprise Java

Getting Started with LangChain4j and Spring Boot

The LangChain4j framework is an open-source library designed to seamlessly integrate Language Learning Models (LLMs) into Java applications. Drawing inspiration from the widely-used LangChain framework in the Python ecosystem, LangChain4j aims to simplify development workflows and provide intuitive APIs. For a deeper understanding of LangChain4j’s capabilities and theoretical underpinnings, you can explore its official GitHub page, where detailed features and other conceptual information are available. Let us delve into understanding LangChain4j API with Spring boot.

1. LangChain4j API

LangChain4j is a Java library designed to facilitate the integration of Language Chain Models into Java applications. It provides a straightforward API for interacting with various language models, allowing developers to build sophisticated natural language processing applications with ease. LangChain4j API simplifies the process of integrating large language models (LLMs) into Java applications. It offers high-level abstractions and helper methods to interact with different LLMs seamlessly.

1.1 Benefits of LangChain4j API

  • Integration with Java: Enables seamless integration with Java applications.
  • Scalability: Designed to handle large-scale applications and data processing.
  • Flexibility: Provides a range of tools and options for developers to customize their implementations.
  • Support for Modern Development Practices: Compatible with modern Java frameworks and development practices.
  • Enhanced Performance: Optimized for high performance, making it suitable for enterprise-level applications.

1.2 Advantages of LangChain4j API

  • Robust Ecosystem: Part of a mature ecosystem with extensive documentation and community support.
  • High Compatibility: Works well with various Java frameworks and libraries.
  • Modular Design: Allows developers to use only the components they need, reducing overhead.
  • Security: Includes built-in security features to protect applications and data.
  • Ease of Use: Designed to be developer-friendly with straightforward API interfaces.

1.3 Disadvantages of LangChain4j API

  • Steep Learning Curve: This may require a significant amount of time to learn and master.
  • Resource Intensive: Can be resource-intensive, requiring significant computational power and memory.
  • Dependency on Java: Exclusively tied to the Java ecosystem, limiting its use with non-Java technologies.
  • Complexity: The extensive features and options might overwhelm new users or those with simple needs.
  • Limited Cross-Language Support: Not designed for seamless integration with languages other than Java.

1.4 Use Cases of LangChain4j API

  • Data Processing Pipelines: Build complex data processing pipelines that can handle large volumes of data efficiently.
  • Machine Learning Model Deployment: Deploy machine learning models within Java applications, allowing for real-time predictions and analytics.
  • Enterprise Application Integration: Integrate various enterprise systems and applications, facilitating seamless data exchange and process automation.
  • Natural Language Processing (NLP) Applications: Develop NLP applications such as chatbots, sentiment analysis tools, and language translation services.
  • Financial Data Analysis: Analyze and process financial data for applications like fraud detection, risk management, and investment analysis.
  • IoT Data Management: Manage and process data from Internet of Things (IoT) devices, enabling real-time monitoring and control.
  • Real-Time Analytics: Perform real-time data analytics to support decision-making processes in various industries such as healthcare, finance, and logistics.
  • Custom Middleware Development: Create custom middleware solutions that connect different software components and services within an architecture.
  • Automated Reporting Systems: Build automated systems for generating and distributing reports based on processed data.
  • Big Data Analytics: Leverage big data technologies to analyze massive datasets, uncovering insights and driving business intelligence.

1.5 LangChain4j LLM Models

Model TypeDescriptionUse CasesStrengthsLimitations
ChatLanguageModelSpecialized for interactive and context-aware conversations.Customer service, personal assistants, interactive agents.Maintains conversation context, provides relevant responses.May require more computational power for complex interactions.
StreamingChatLanguageModelSupports real-time streaming conversations with low latency.Live chat support, real-time interactive systems.Real-time processing, low latency.Requires robust infrastructure for real-time performance.
EmbeddingModelGenerates dense vector representations of text for various NLP tasks.Semantic search, text clustering, recommendation systems.Efficient text representation, useful for downstream tasks.Performance depends on the quality of training data.
ImageModelProcesses and analyzes image data for various applications.Image classification, object detection, image captioning.High accuracy in visual tasks, versatile image processing.Computationally intensive, requires large datasets.
ModerationModelIdentifies and filters inappropriate or harmful content.Content moderation, social media monitoring, compliance.Effective at filtering harmful content.May produce false positives/negatives, requires continuous updates.
ScoringModelEvaluates and scores text based on specific criteria.Content quality assessment, relevance scoring, sentiment analysis.Provides quantitative evaluation, adaptable to different criteria.Scoring criteria must be well-defined and relevant.

2. Code example

Below is an example of a Spring Boot application that integrates LangChain4j API.

2.1 Create a Spring Boot Project

You can use Spring Initializr (https://start.spring.io/) to create a Spring Boot project. Select Maven Project, Java, and the necessary Spring Boot version. To integrate LangChain4j with Spring Boot, we first need to add the necessary dependencies in our pom.xml file:


2.2 Create a Configuration Class

Create a configuration class to configure the LangChain4j API in your Spring Boot application.

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import com.example.langchain4j.openai.OpenAiClient;

public class LangChainConfig {

    public OpenAiClient openAiClient() {
        return new OpenAiClient("YOUR_API_KEY");

This configuration class defines a LangChainConfig class annotated with @Configuration to mark it as a source of bean definitions. The openAiClient() method annotated with @Bean creates and returns an instance of OpenAiClient using your API key. This bean can then be injected into other components of your Spring Boot application.

2.3 Initializing a ChatModel

Once we have configured our Spring Boot application, we can initialize a ChatModel to interact with the language model.

import com.example.langchain4j.openai.ChatModel;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

public class ChatService {

    private final ChatModel chatModel;

    public ChatService(OpenAiClient openAiClient) {
        this.chatModel = openAiClient.createChatModel();

    public String chat(String userPrompt) {
        return chatModel.sendUserPrompt(userPrompt).getResponse();

In this service class, we define a ChatService annotated with @Service to mark it as a Spring service component. The constructor is annotated with @Autowired to inject an instance of OpenAiClient. The chatModel is then initialized using the createChatModel() method of OpenAiClient. The chat() method sends a user prompt to the chat model and returns the response.

2.4 First Call to LLM

With our service set up, we can now make the first call to the language model. Let’s create a simple controller to test this:

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;

public class ChatController {

    private final ChatService chatService;

    public ChatController(ChatService chatService) {
        this.chatService = chatService;

    public String chat(@RequestParam String prompt) {
        return chatService.chat(prompt);

This ChatController class is annotated with @RestController to mark it as a RESTful web service controller. It defines a GET endpoint at /chat that accepts a prompt parameter. The chat() method calls the chat() method of ChatService and returns the response.

2.5 Run Your Application

Now, you can start your Spring Boot application and access the chat endpoint with a prompt:


This will send the prompt “Hello” to the language model and return the response.

Hello! How can I assist you today?

3. Sending System and User Prompts

LangChain4j allows sending both system and user prompts to the language model. System prompts are the instructions or context provided by the system to guide the behavior and responses of the model. User prompts are inputs or queries provided by the user to request information or perform tasks using the model.

Here is how you can send both types of prompts:

import com.example.langchain4j.openai.ChatModel;
import com.example.langchain4j.openai.ChatPrompt;

public class ChatService {

    private final ChatModel chatModel;

    public ChatService(OpenAiClient openAiClient) {
        this.chatModel = openAiClient.createChatModel();

    public String chatWithSystemPrompt(String systemPrompt, String userPrompt) {
        ChatPrompt prompt = new ChatPrompt();
        return chatModel.sendPrompt(prompt).getResponse();

In this updated ChatService class, we define a new method chatWithSystemPrompt() that creates a ChatPrompt object. We use the addSystemPrompt() method to add a system prompt and the addUserPrompt() method to add a user prompt. The combined prompt is then sent to the chat model, and the response is returned.

System prompt: "You are a helpful assistant."
User prompt: "What is the capital of France?"
Response: "The capital of France is Paris."

4. Conclusion

Integrating LangChain4j with Spring Boot provides a robust framework for interacting with language models. By following the steps outlined in this article, you can set up your Spring Boot application to use LangChain4j, initialize a ChatModel, and send prompts to get responses from the language model. This integration enables you to leverage the power of language models in your Java applications efficiently.

Yatin Batra

An experience full-stack engineer well versed with Core Java, Spring/Springboot, MVC, Security, AOP, Frontend (Angular & React), and cloud technologies (such as AWS, GCP, Jenkins, Docker, K8).
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