# Build Your First Spring AI Application with OpenAI Using Spring Boot > Source: > Published: 2026-08-17 19:44:38+00:00 If you have been following this Spring AI series, you already understand the two most important abstractions: `ChatClient` and `ChatModel` . In the previous article, we learned that: ``` Your Java Code | v ChatClient | v ChatModel | v AI Provider ``` `ChatClient` gives us a developer-friendly API, while `ChatModel` handles the underlying model integration. Now it is time to build something real. In this article, we will build our first **Spring AI application using OpenAI**. We will start from project setup and configuration, connect Spring Boot to OpenAI, create a REST API, send prompts to an AI model, and understand what happens behind the scenes. By the end, you will have a working AI-powered Spring Boot API. Our application will expose a simple REST endpoint: ``` GET /api/chat?message=Explain dependency injection ``` The flow will look like this: ``` Client | v Spring Boot REST API | v ChatClient | v ChatModel | v OpenAI | v AI Response ``` The goal is intentionally simple. We want to understand the complete flow before adding more advanced concepts such as RAG, tools, memory, structured output, and AI agents. Before starting, you should have: You should also be comfortable with dependency injection and creating REST controllers. If you have followed the previous articles in this series, most of this should already be familiar. The easiest way to create the project is through Spring Initializr. Choose: ``` Project: Maven Language: Java Spring Boot: Your compatible Spring Boot version Packaging: Jar Java: 17 or later ``` For dependencies, we need Spring Web and the Spring AI OpenAI starter. Your project will eventually look something like: ``` spring-ai-openai-demo │ ├── src │ ├── main │ │ ├── java │ │ │ └── com.example.demo │ │ │ └── DemoApplication.java │ │ │ │ │ └── resources │ │ └── application.properties │ └── pom.xml ``` Add the Spring AI OpenAI model starter to your Maven configuration. For example: ``` org.springframework.ai spring-ai-starter-model-openai ``` You will also need Spring Web: ``` org.springframework.boot spring-boot-starter-web ``` Your project now has the components required to build a simple AI REST API. Conceptually: ``` Spring Boot | +-- Spring Web | +-- Spring AI | +-- OpenAI integration ``` Spring AI needs credentials to communicate with OpenAI. You can configure the API key using an environment variable. For example: ``` export OPENAI_API_KEY=your-api-key ``` On Windows PowerShell: ``` $env:OPENAI_API_KEY="your-api-key" ``` Then reference it from your Spring configuration: ``` spring.ai.openai.api-key=${OPENAI_API_KEY} ``` The important part is that your API key should **not be hard-coded inside your Java source code**. Avoid doing this: ``` String apiKey = "sk-xxxxxxxx"; ``` Instead, keep secrets outside your source code. A better approach is: ``` Environment Variable | v Spring Configuration | v Spring AI | v OpenAI ``` This becomes even more important when deploying your application to production. Spring AI needs to know which OpenAI chat model your application should use. You can configure the model through your application properties. For example: ``` spring.ai.openai.chat.options.model=your-model ``` The exact model you choose depends on the models available to your OpenAI account and the requirements of your application. The important concept is that your application does not need to manually construct OpenAI HTTP requests. Spring AI handles that integration. Now let's create our first AI-powered controller. ``` @RestController @RequestMapping("/api") public class ChatController { private final ChatClient chatClient; public ChatController(ChatClient.Builder chatClientBuilder) { this.chatClient = chatClientBuilder.build(); } @GetMapping("/chat") public String chat(@RequestParam("message") String message) { return chatClient .prompt(message) .call() .content(); } } ``` Let's understand this carefully. The constructor receives: ``` ChatClient.Builder chatClientBuilder ``` Spring AI provides this builder through Spring Boot auto-configuration when the appropriate model integration is configured. We then create our `ChatClient` : ``` this.chatClient = chatClientBuilder.build(); ``` Now the controller has a ready-to-use AI client. The architecture looks like: ``` Spring Boot | v ChatClient.Builder | | build() v ChatClient ``` This is the same concept we explored in Part 2 of this series. Now let's call the API. Start your Spring Boot application. Then send: ``` GET /api/chat?message=What is dependency injection in Spring? ``` The controller receives: ``` What is dependency injection in Spring? ``` and passes it to: ``` chatClient .prompt(message) .call() .content(); ``` The flow is: ``` HTTP Request | v ChatController | v ChatClient | v ChatModel | v OpenAI | v AI Response ``` The generated response is returned to the client. That's it. You have now built a Spring Boot application that can communicate with an AI model. Let's look at this code again: ``` chatClient .prompt(message) .call() .content(); ``` There are three important operations here. ``` .prompt(message) ``` This defines the prompt that you want to send to the model. For example: ``` .prompt("Explain Java interfaces") ``` or: ``` .prompt("Write a SQL query to find duplicate users") ``` or: ``` .prompt(message) ``` where `message` comes from an HTTP request. ``` .call() ``` This executes the model interaction. You can think about it as: ``` Build Prompt | v Call Model .content() ``` This extracts the generated text from the response. So the entire chain: ``` chatClient .prompt(message) .call() .content(); ``` can be mentally understood as: ``` Create Prompt | v Call AI Model | v Extract Text ``` This fluent style is one of the reasons `ChatClient` is convenient for application developers. Although putting the AI call directly inside a controller works for a small demonstration, it is not how I would structure a production application. Instead, let's introduce a service. Our architecture becomes: ``` Client | v Controller | v Service | v ChatClient | v OpenAI ``` Create: ``` @Service public class ChatService { private final ChatClient chatClient; public ChatService(ChatClient.Builder builder) { this.chatClient = builder.build(); } public String generateResponse(String message) { return chatClient .prompt(message) .call() .content(); } } ``` Then our controller becomes: ``` @RestController @RequestMapping("/api") public class ChatController { private final ChatService chatService; public ChatController(ChatService chatService) { this.chatService = chatService; } @GetMapping("/chat") public String chat(@RequestParam String message) { return chatService.generateResponse(message); } } ``` This separation is much cleaner. The controller handles HTTP. The service handles AI interaction. Imagine that six months from now your application has: ``` ChatController EmailController SupportController DocumentController ``` If every controller directly interacts with the AI model, your code can quickly become difficult to maintain. Instead: ``` Controllers | v AI Services | v ChatClient | v ChatModel ``` This keeps your application organized. It also makes it easier to add features later. So far, we have only sent a user prompt. But real AI applications usually need more control. For example, imagine we are building a customer support assistant. We don't want the model to behave like a generic chatbot. We want to tell it: ``` You are a customer support assistant. Answer clearly. Keep responses concise. Do not invent information. ``` We can do that with a system message. For example: ``` return chatClient .prompt() .system(""" You are a helpful customer support assistant. Answer clearly and concisely. Do not invent information. """) .user(message) .call() .content(); ``` Now we have two different types of instructions: ``` System Message + User Message | v Model ``` This distinction will become extremely important later in the series. Think about the two messages like this. Defines the behavior of the assistant. ``` You are a Java programming assistant. Always provide production-quality examples. ``` Contains the actual request. ``` Explain dependency injection. ``` Together: ``` System | | "You are a Java assistant" | v User | | "Explain dependency injection" | v AI Model ``` The PDF structure for this series introduces system and user messages as dedicated upcoming topics, so we will explore them in much more detail later. Let's make our API slightly more realistic. Instead of simply passing the user's message directly to the model, we can create a dedicated service method: ``` @Service public class ChatService { private final ChatClient chatClient; public ChatService(ChatClient.Builder builder) { this.chatClient = builder.build(); } public String ask(String question) { return chatClient .prompt() .system(""" You are a helpful Java and Spring Boot assistant. Explain technical concepts clearly. Use examples when appropriate. """) .user(question) .call() .content(); } } ``` Then: ``` @RestController @RequestMapping("/api/chat") public class ChatController { private final ChatService chatService; public ChatController(ChatService chatService) { this.chatService = chatService; } @GetMapping public String chat(@RequestParam String message) { return chatService.ask(message); } } ``` Now our API becomes: ``` GET /api/chat?message=What is Spring Boot? ``` and the service controls how the AI behaves. This simple line: ``` chatClient .prompt() .user(message) .call() .content(); ``` hides several operations. Conceptually: ``` Spring Boot | v ChatClient | v Build Chat Request | v ChatModel | v OpenAI Integration | v OpenAI API | v AI Response | v ChatClient | v String ``` This abstraction is one of the major benefits of Spring AI. You focus on your application. Spring AI handles the model integration. This is another reason abstractions are useful. Without Spring AI, you might build: ``` Spring Boot | v Custom HTTP Client | v OpenAI API ``` Your application would now contain provider-specific request and response handling. With Spring AI: ``` Spring Boot | v ChatClient | v ChatModel | v Provider ``` Your application code stays focused on the AI interaction rather than provider-specific implementation details. This is the abstraction we discussed in the previous article. If you're a Spring Boot developer, think about Spring AI in layers. ``` Application Layer | v ChatClient | v ChatModel | v AI Provider | v AI Model ``` Each layer has a responsibility. Business logic. Developer-friendly AI interaction. Model/provider abstraction. OpenAI, Ollama, AWS Bedrock, and other supported providers. The actual language model generating the response. One of the biggest mistakes beginners make is committing API keys to Git. Never do this: ``` spring.ai.openai.api-key=sk-your-secret-key ``` inside a repository that will be shared publicly. Instead: ``` spring.ai.openai.api-key=${OPENAI_API_KEY} ``` and configure the environment variable separately. For local development: ``` OPENAI_API_KEY ``` For production, use your deployment platform's secret management mechanism. The principle is simple: ``` Source Code X | | No secrets | Environment / Secret Store | v Spring Boot ``` Never commit secrets. If your key is exposed, rotate it immediately. Avoid this structure: ``` php Controller 1 -> AI Controller 2 -> AI Controller 3 -> AI Controller 4 -> AI ``` Prefer: ``` Controllers | v Services | v ChatClient ``` Remember: ``` ChatClient != AI Model ``` `ChatClient` is the application-facing abstraction. The underlying model integration is handled through `ChatModel` . A production AI application usually needs some control over model behavior. Instead of: ``` .prompt(message) ``` you will often evolve toward: ``` .prompt() .system("...") .user(message) ``` Later, we will see how prompt templates, advisors, memory, RAG, and tools make this even more powerful. You don't need this on day one: ``` RAG + Vector Database + Tools + MCP + Memory + Agents + Multiple Models ``` Start with: ``` Spring Boot | v ChatClient | v OpenAI ``` Then add complexity when your application actually needs it. Our application currently looks simple: ``` Client | v REST API | v ChatClient | v OpenAI ``` But this architecture can grow. For example: ``` Spring Boot | v ChatClient | +--------------+--------------+ | | | v v v Memory RAG Tools | | | v v v History Vector Store Backend APIs ``` This is where Spring AI becomes much more interesting. A simple chatbot can eventually become a complete AI backend. Here is a simple production-style starting point. ``` @Service public class ChatService { private final ChatClient chatClient; public ChatService(ChatClient.Builder builder) { this.chatClient = builder.build(); } public String ask(String question) { return chatClient .prompt() .system(""" You are a helpful Java and Spring Boot assistant. Explain concepts clearly and provide examples when useful. """) .user(question) .call() .content(); } } @RestController @RequestMapping("/api/chat") public class ChatController { private final ChatService chatService; public ChatController(ChatService chatService) { this.chatService = chatService; } @GetMapping public String chat(@RequestParam String message) { return chatService.ask(message); } } spring.ai.openai.api-key=${OPENAI_API_KEY} spring.ai.openai.chat.options.model=your-model ``` The result is a clean architecture: ``` HTTP Client | v ChatController | v ChatService | v ChatClient | v ChatModel | v OpenAI ``` In this article, we built our first Spring AI application using OpenAI. We learned how to: `ChatClient.Builder` `ChatClient` Most importantly, you now understand the complete request flow: ``` Client | v Spring Boot | v Controller | v Service | v ChatClient | v ChatModel | v OpenAI ``` This is the foundation for everything we will build later. A hosted AI model is useful, but what if you want to run an LLM locally? Maybe you don't want to send your data to an external provider. Maybe you want to experiment without paying for API usage. Maybe you're building an application where local inference is important. That's where **Ollama** comes in. In the next article, we will explore: **Part 4: Run Local LLMs with Ollama and Spring AI** We will install Ollama, run a local model, connect it to Spring AI, and see how the same `ChatClient` application can work with a local LLM. That is one of the most interesting parts of Spring AI: ``` php Same Application | +------> OpenAI | +------> Ollama | +------> Other Providers ``` The application stays focused on the AI interaction while Spring AI handles the underlying model integration. Spring AI is an abstraction layer that makes it easier for Spring applications to integrate with AI models and AI-related capabilities. Yes. Spring AI provides an OpenAI integration that allows Spring Boot applications to communicate with OpenAI models. Do not hard-code it in your source code. Use environment variables or a proper secret-management solution. For most application-level use cases, you can work primarily with `ChatClient` . `ChatModel` remains important because it represents the underlying model integration. One of the goals of Spring AI's abstraction model is to reduce application coupling to provider-specific implementation details. The next step in this series is running a local LLM with Ollama and Spring AI. **Part 1:** Spring AI Tutorial: How Java Developers Can Build Generative AI Applications with Spring Boot **Part 2:** ChatModel vs ChatClient in Spring AI **Part 3:** Build Your First Spring AI Application with OpenAI **Part 4:** Run Local LLMs with Ollama and Spring AI **Part 5:** Run AI Models Locally with Docker Model Runner **Part 6:** Using AWS Bedrock with Spring AI **Part 7:** Working with Multiple Chat Models in Spring AI **Part 8:** Understanding Message Roles in LLMs **Part 9:** System Messages and User Messages in Spring AI **Part 10:** Configuring Default Behavior in ChatClient The series will continue into prompt templates, advisors, structured output, tokens, embeddings, chat memory, RAG, vector stores, tool calling, MCP, evaluation, observability, and AI agents.