Integrating Large Language Models in Enterprise Java Applications with Spring Boot

“

“To build AI, thou must switch to Python.” — Ancient Developer Scrolls (probably)

If you’re a Java developer who’s encountered Python-centric AI tutorials and questioned whether to abandon Java, reconsider that approach. Through Spring AI and Ollama, you can now interact with large language models (LLMs) using exclusively Java + Spring Boot—without Python environments or Jupyter Notebooks.

This guide demonstrates how to build an enterprise-ready AI application entirely within the Java ecosystem.

Core Application Functionality

We’ll implement a REST API that:

1. Accepts user prompts via the /chat endpoint
2. Routes requests to a local LLM through Ollama
3. Returns AI-generated responses
4. Operates entirely within the Java/Spring Boot stack

Why This Approach Suits Java Developers

• ✅ Run LLMs locally (GPU optional)
• ✅ Maintain Spring Boot development patterns
• ✅ Integrate with existing Java microservices
• ✅ Avoid cross-language maintenance complexity

Technology Components Explained

Ollama: Local LLM Execution Engine

Ollama delivers out-of-the-box LLM management with one-command deployments:

# Install model (example: llama2)
ollama pull llama2
# Launch model service
ollama run llama2

The model service runs at http://localhost:11434, effectively creating a local AI service instance.

Implementation Walkthrough

Step 1: Initialize Spring Boot Project

Generate your project via Spring Initializr with:

• Spring Web
• Spring Boot DevTools

Add Ollama dependency to pom.xml:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
    <version>0.7.0</version>
</dependency>

Step 2: Configure Model Parameters

In application.yml:

spring:
  ai:
    ollama:
      chat:
        model: llama2  # Must match locally running model
        base-url: http://localhost:11434

Step 3: Implement API Controller

Create the REST controller:

@RestController
public class OllamaChatController {

    private final OllamaChatModel chatModel;

    // Constructor injection
    public OllamaChatController(OllamaChatModel chatModel) {
        this.chatModel = chatModel;
    }

    @PostMapping("/chat")
    public ResponseEntity<String> chatBot(@RequestBody String userQuery) {
        // Invoke model and return response
        String response = chatModel.call(userQuery);
        return ResponseEntity.ok(response);
    }
}

Step 4: Test AI Service

Launch the application:

./mvnw spring-boot:run

Validate with curl:

curl -X POST http://localhost:8080/chat \
-H "Content-Type: text/plain" \
-d "Explain Java's garbage collection mechanism"

Enterprise Feature Extensions

Spring AI supports advanced capabilities:

Solution Advantages

1. Data Security

• All processing occurs locally
• Eliminates third-party API data exposure

2. Performance Efficiency

• Sub-1ms local network latency
• No remote API response delays

3. Cost Management

• Zero cloud service fees
• No token-based pricing

4. Technical Consistency

• Leverage existing Java expertise
• Seamless Spring ecosystem integration

Frequently Asked Questions (FAQ)

Q1: Is Python required?

No. The entire solution uses Java and Spring Boot, with Ollama as a standalone local runtime.

Q2: Can different LLMs be used?

Yes. Ollama supports dozens of open-source models. Switch via configuration:

spring.ai.ollama.chat.model: mistral  # Switch to Mistral

Q3: How to handle multi-turn conversations?

Spring AI’s ChatMemory interface maintains context:

@Bean
public ChatMemory chatMemory() {
    return new SimpleChatMemory();
}

Q4: Is streaming supported?

Yes. Modify the controller for streaming:

@PostMapping("/stream")
public Flux<String> streamChat(@RequestBody String prompt) {
    return chatModel.stream(prompt);
}

Q5: What hardware is required?

Most 7B-parameter models run on CPUs. GPUs accelerate performance (optional).

Technical Validation

Complete Code Access

Open-source project available:
👉 github.com/Aman20aug/SpringAI

Includes:

1. Runnable Spring Boot project
2. Configuration templates
3. Test cases
4. Multi-model demonstration

Conclusion: Java’s Position in AI

Enterprise applications can integrate AI without stack overhaul. Through Spring AI + Ollama:

1. Maintain technical consistency – Preserve Java development standards
2. Reduce migration costs – Eliminate Python training needs
3. Control infrastructure – Full on-premises deployment

When questioned whether Java supports AI, you can now respond:
“With Spring Boot and Ollama, my Java application runs LLMs locally.”