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Building an In-House LLM Application with Spring AI and Ollama: A Comprehensive Tutorial

In this tutorial, we’ll guide you through the process of creating an in-house Language Model (LLM) application using Spring AI and Ollama. By the end, you’ll have a robust platform for language processing tailored to your specific needs.


  • Basic knowledge of Java and Spring framework
  • Familiarity with RESTful APIs
  • Access to Spring AI and Ollama services

1. Setting Up Your Development Environment:

Before diving into code, ensure you have Java Development Kit (JDK) installed and a code editor of your choice. Also, sign up for accounts on Spring AI and Ollama platforms to obtain necessary credentials.

2. Creating a Spring Boot Project:

Start by creating a new Spring Boot project using your preferred method. You can use Spring Initializr or your IDE’s project creation wizard.

3. Adding Dependencies:

In your pom.xml or build.gradle, add dependencies for Spring Web, Spring Boot DevTools, and any other dependencies required for your project.

<!-- Example for Maven -->
<!-- Add other dependencies as needed -->

4. Configuring Spring AI Integration:

Integrate Spring AI’s language processing capabilities into your application. This typically involves configuring API endpoints and handling requests/responses.

// Example Spring controller for handling language processing requests
public class LanguageController {

    private SpringAIService springAIService;

    public ResponseEntity<String> processLanguage(@RequestBody String text) {
        // Call Spring AI service to process text
        String processedText = springAIService.process(text);
        return ResponseEntity.ok(processedText);

5. Integrating Ollama for Advanced Language Processing:

Ollama offers advanced features like sentiment analysis, entity recognition, and more. Integrate Ollama into your application to leverage these capabilities.

// Example method using Ollama for sentiment analysis
public String analyzeSentiment(String text) {
    OllamaService ollamaService = new OllamaService("YOUR_OLLAMA_API_KEY");
    SentimentAnalysisResult result = ollamaService.analyzeSentiment(text);
    return result.getSentiment(); // Return sentiment of the text

6. Building Additional Features:

Depending on your requirements, you can extend your application with additional features such as:

  • Customized language models
  • Integration with other services or databases
  • User authentication and authorization

7. Testing and Deployment:

Test your application thoroughly to ensure it meets your requirements. Once satisfied, deploy it to your preferred environment, whether it’s on-premises or on the cloud.


Congratulations! You’ve successfully built an in-house LLM application using Spring AI and Ollama. With this powerful combination, you can now perform advanced language processing tasks tailored to your specific needs. Experiment with different features, scale your application as required, and continue innovating in the realm of language processing. Happy coding!

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