For an existing Java application, start with one focused model request, keep provider calls behind a small service, and add memory, tools, or retrieval only when the feature needs them. This tutorial uses LangChain4j with Spring Boot and an OpenAI chat model. The code shows the integration pattern; use dependency versions that are compatible with your application and verify them against the current LangChain4j documentation before building. The cited integration references are version-specific, so this tutorial does not claim a current universal Java or Spring Boot minimum.
What you will build
The example is a small support-answer service: a Java component sends a question to a hosted chat model and returns its answer. The first version makes a single request and has no conversation history or private knowledge base. That makes it easier to see what the model integration does before adding more moving parts.
LangChain4j is a Java-oriented library intended to simplify integrating AI into Java applications. Its introduction describes a unified API for model providers and embedding stores, and names OpenAI and Google Vertex AI as examples. That API goal can help avoid coupling application code directly to one provider’s proprietary API; it does not mean every provider integration has identical features, behavior, or terms. See the official introduction.
LangChain4j documents integrations for Spring Boot, Quarkus, and Helidon. Choose the integration that fits the framework already in your application rather than introducing another framework solely for one model call. The documentation’s integration guides are the place to check current options.
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Check versions and configure credentials
Use the LangChain4j modules from one compatible release line, and confirm the starter and model artifact names in that release’s documentation. Dependency coordinates and framework compatibility can change. A version-specific Spring Boot integration reference has listed Java 17 and Spring Boot 3.2; treat those as requirements for that documented version, not universal requirements for current releases. See the OpenAI integration guide and the Spring Boot integration guide.
For a Spring Boot application, add the documented Spring Boot starter and OpenAI model integration for the same LangChain4j release. Do not copy dependency versions from an unrelated tutorial or mix modules from different releases. The exact coordinates should be taken from the guide for the release you select.
Keep the provider key outside source control. For local development, set an environment variable in your shell or IDE run configuration:
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export OPENAI_API_KEY="your-key-here"Configure the application to read that value from the environment rather than putting the secret in Java code or committing it in a configuration file. In deployed environments, use the secret-management mechanism appropriate to your platform, restrict access, and rotate exposed credentials.
Make one model request
Once the documented dependencies and provider configuration are in place, inject the chat model into a small Spring component. The exact configuration-property names and bean setup depend on the starter version, so follow that version’s Spring Boot guide. The essential interaction is a prompt sent to the model and the returned text passed back to the caller:
@Service
public class SupportAnswerService {
private final ChatLanguageModel model;
public SupportAnswerService(ChatLanguageModel model) {
this.model = model;
}
public String answer(String question) {
return model.generate("Answer this customer question clearly: " + question);
}
}This illustrates the request path, not a complete production endpoint. The user’s input reaches the model, and the generated text returns as a string. Apply input validation and an appropriate response policy before exposing the method through a controller. Avoid assuming model output is trustworthy merely because it is syntactically valid.
For a first experiment, call the method with a non-sensitive question and inspect the returned text. A real application should also handle provider errors and avoid logging secrets or unnecessary user data; model calls can transmit prompts to an external service, so review the provider’s data-handling terms for the deployment you choose.
Use an AI Service when it improves the service boundary
Calling the model directly is useful for understanding the basic interaction. As the feature grows, LangChain4j AI Services can move prompt-to-response handling behind a Java interface. The library can handle input formatting and output parsing, and can support chat memory, tools, and RAG. That can keep application code focused on the feature rather than low-level request plumbing. Learn the interface pattern in the AI Services tutorial.
Adopt the abstraction when it makes the code clearer, not as a requirement for every application. Keep explicit control where you need custom prompt construction, provider-specific settings, special error handling, or a response format that needs validation. Parsing a response into a Java type does not by itself prove that the contents are correct.
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Add only the capability your feature needs
| Need | Capability | What it adds |
|---|---|---|
| Follow-up questions should account for earlier turns | Chat memory | Retains selected conversation context across calls; define what is stored and for how long. |
| The model must perform a bounded application action | Tools | Lets the model request an application-provided operation; your code should validate and authorize that operation. |
| Answers should use a defined document collection | Retrieval-augmented generation (RAG) | Finds relevant content and includes it in the model interaction; retrieval does not guarantee a correct answer. |
These are distinct design choices, not a checklist of features every Java AI integration needs. Begin with the smallest one that addresses a visible limitation.
Use chat memory for continuity
A stateless request cannot reliably know what “that issue” refers to in a later turn unless relevant context is supplied again. Chat memory can preserve selected turns or other context for follow-up requests. Decide how memory is scoped—such as per conversation or user—and apply retention, privacy, and access-control rules. The AI Services tutorial documents memory support: LangChain4j AI Services.
Use tools for a bounded action
A tool is appropriate when the model needs to ask your application to perform a defined operation, such as looking up an order status. The application, not the model, should enforce identity, authorization, allowed inputs, and business rules. Treat a tool request as untrusted input and do not expose unrestricted operations merely because they are convenient to call.
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Use RAG for answers grounded in a corpus
RAG is useful when the answer should draw on a defined collection of documents rather than rely only on the model’s general knowledge. Its basic path is to prepare and index the corpus, retrieve relevant content for a user’s question, and supply that content to the model as context. The documents, chunking, embedding model, embedding store, and retrieval behavior all affect what context reaches the model; the model can still misread or misrepresent it.
LangChain4j provides a tutorial titled “How to do Easy RAG with LangChain4j?” Use its RAG tutorial to follow the documented implementation for the release you choose. Be explicit about the source of the documents and refresh process in your own application. RAG is not a guarantee of accuracy, and it should not be described as though the model has been trained on your corpus.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose the integration around your application
- Framework fit: If the application already uses Spring Boot, Quarkus, or Helidon, start with the corresponding documented integration rather than building a parallel application layer.
- Provider and store fit: Check that the provider and any embedding store you need are supported by the specific LangChain4j release. A unified API can reduce direct dependence on provider APIs, but provider-specific behavior remains relevant.
- Interaction complexity: Use a direct model call for a simple request; consider AI Services for a cleaner interface boundary; add memory, tools, or RAG only to meet a concrete need.
- Compatibility: Verify the selected Java, framework, starter, and model-integration versions together. Old version-specific requirements should not be carried forward as universal rules.
- Deployment choice: Hosted providers and other deployment models involve workload-specific trade-offs. The cited documentation does not establish a universal cost or performance winner.
Operational checks before shipping
- Errors and retries: Handle authentication failures, rate limits, timeouts, and provider outages. Retry only where appropriate and avoid turning transient failures into duplicate side effects.
- Privacy: Decide which prompt data may leave your system, whether it may be logged, and how conversation history or retrieved documents are retained.
- Latency and cost: Measure these in your workload and deployment. They depend on provider, model, prompt size, traffic, and other conditions; no general figure is established here.
- Testing: Test your application’s validation, error paths, tool authorization, and retrieval behavior separately from the model. Model-generated answers can vary, so avoid brittle tests that require one exact phrasing.
- Provider differences: Check the chosen integration’s supported settings and behavior rather than assuming every provider implements the same API features in the same way.
If you want to explore a more agent-focused Java example after the basic interaction is working, Google Developers provides a Codelab using LangChain4j and Google GenAI: Build Java applications with LangChain4j and Google GenAI. An agent is an optional next step, not a prerequisite for adding a useful AI feature.
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