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

Fri, 26 Jun 2026 00:00:00 +0000

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.

Tutorial on JavaDev AI - Java + AI in Practice

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

What You’ll Build

Step 1: Add Dependencies

Step 2: Configure LLM Connection