JavaDev AI - Java + AI in Practice

Llm

ยท 5 min read

Spring Boot AI Integration: Complete Guide to Adding LLM Capabilities to Your Application

Adding AI capabilities to your Spring Boot application doesn’t require rebuilding from scratch. In this comprehensive guide, you’ll learn how to enhance an existing Spring Boot application with LLM-powered features using LangChain4j.

What You’ll Build

A customer support assistant that can:

  • Answer questions about your product using documentation (RAG)
  • Process natural language commands via tool calling
  • Maintain conversation context across requests

Step 1: Add Dependencies

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<dependencies>
    <!-- LangChain4j with OpenAI -->
    <dependency>
        <groupId>dev.langchain4j</groupId>
        <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
        <version>0.36.2</version>
    </dependency>
    
    <!-- For RAG with PgVector -->
    <dependency>
        <groupId>dev.langchain4j</groupId>
        <artifactId>langchain4j-pgvector</artifactId>
        <version>0.36.2</version>
    </dependency>
</dependencies>

Step 2: Configure LLM Connection

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# application.yml
langchain4j:
  open-ai:
    chat-model:
      model-name: gpt-4o-mini
      temperature: 0.3
      max-tokens: 2000
      timeout: 30s
      log-requests: true
      log-responses: true

For cost optimization, use gpt-4o-mini for most tasks and gpt-4o only for complex reasoning.