Building Intelligent Applications with Spring Boot & AI

Part 1: Introduction & Environment Setup

Unlock the power of modern AI in your enterprise Java stack using Spring Boot and the latest AI platforms—including OpenAI, Ollama, and Perplexity.

🚀 Why Blend AI with Spring Boot?

Artificial Intelligence is no longer just a research buzzword. With the rise of Large Language Models (LLMs) like GPT-4, Llama, and Perplexity, AI-powered capabilities—including chatbots, summarizers, content markers, and virtual assistants—can be easily integrated right into your existing Spring Boot projects. The new Spring AI library brings these cutting-edge models to Java with minimum boilerplate and maximum flexibility.

🧑‍💻 Who Is This Series For?

  • Spring Boot developers (intermediate/senior)

  • Java architects and backend specialists

  • Engineers looking to design AI-powered microservices

Bring a working knowledge of Spring Boot (REST controllers, configs) and let’s level up your skillset.

1. What We Cover in This Lecture

  • The business case for “AI-first” features in enterprise Java

  • What are LLMs (Large Language Models) and why are they important?

  • Quick intro to the Spring AI project

  • Setting up your development environment step-by-step

2. Understand the Building Blocks

What is an LLM?

Large Language Models are deep learning systems trained on enormous datasets. They interpret text, generate answers, and even reason contextually. Think: OpenAI’s GPT-4, Meta’s Llama, or Perplexity’s web-augmented models.

Why does this matter?
With a simple API, you can automate email replies, generate reports, build intelligent chatbots, and more—directly from your backend.

What is Spring AI?

Spring AI is a new project from the Spring team that abstracts access to multiple AI providers. Instead of learning each platform’s quirks, you code to a unified Java API, plug in your provider (OpenAI, Ollama, Perplexity, others), and harness language models, embeddings, and workflows with ease.

3. Requirements & Environment Setup

Here’s a quick checklist to get your workstation AI-ready:

Prerequisites

  • Java 17+ (or Java 21)

  • Maven or Gradle

  • An IDE (IntelliJ IDEA, Eclipse, VS Code)

  • Accounts/API keys for OpenAI, Perplexity (optional for Ollama, runs locally)

1️⃣ Create a Spring Boot Project

  • Go to Spring Initializr

  • Select Spring Boot 3.x, Java 17+, and Spring Web

  • Download and open in your IDE

2️⃣ Add Spring AI Dependencies

In your pom.xml (Maven example):

<dependency>
  <groupId>org.springframework.ai</groupId>
  <artifactId>spring-ai-starter</artifactId>
</dependency>
<dependency>
  <groupId>org.springframework.ai</groupId>
  <artifactId>spring-ai-starter-model-openai</artifactId>
</dependency>
<dependency>
  <groupId>org.springframework.ai</groupId>
  <artifactId>spring-ai-starter-model-ollama</artifactId>
</dependency>

Add more model integrations as needed (such as Perplexity).

3️⃣ Configure AI Providers

Add these to src/main/resources/application.properties:

# OpenAI configuration
spring.ai.openai.api-key=YOUR_OPENAI_KEY
spring.ai.openai.chat.options.model=gpt-4

# Ollama (local model)
# spring.ai.ollama.base-url=http://localhost:11434

# Perplexity (if using)
spring.ai.openai.base-url=https://api.perplexity.ai
spring.ai.openai.chat.completions-path=/chat/completions
spring.ai.openai.chat.options.model=llama-3.1-sonar-small-128k-online
spring.ai.openai.chat.options.temperature=0.7
spring.ai.openai.embedding.enabled=false

4️⃣ Test Your Setup

Add a basic test controller to verify your environment:

@RestController
public class TestController {
  @GetMapping("/test-ai")
  public String testAI() {
    return "Spring AI environment is ready!";
  }
}

4. What’s Next?

In the upcoming posts and lectures, we’ll:

  • Build prompt-driven endpoints using OpenAI and Ollama

  • Add retrieval-augmented-generation (RAG) and vector database integration

  • Assemble a real-world enterprise knowledge assistant with Spring Boot + AI

💡 Pro Tip

Don’t expose your API keys in public repos! Use environment variables or configuration servers for security.

Ready for hands-on AI with Spring Boot?
Follow along, comment with your questions, and let’s build smarter Java apps—together.

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