Lets start with Spring AI

Spring AI : Your First Step into Generative AI with Java

Often Java based enterprise systems find it difficult to work with Python libs and related tool chains. Introducing Spring AI, an open-source framework designed to simplify the development of applications that incorporate Artificial Intelligence capabilities, specifically Large Language Models (LLMs), using the familiar patterns of the Spring ecosystem.

If you are a Java developer looking to integrate powerful features like ChatGPT or Google Gemini into your enterprise applications without wrestling with provider-specific SDKs, Spring AI is the perfect tool.

What is Spring AI?

At its core, Spring AI acts as a common abstraction layer for AI models.

Think of it like Spring Data JPA for databases: just as Spring Data abstracts away SQL and database specifics, Spring AI abstracts away the differences between various AI providers (OpenAI, Google, Azure, Anthropic, etc.).

This approach offers two huge benefits:

  1. Portability: You can switch between different AI models and providers with minimal code changes, allowing you to choose the most cost-effective or highest-performing model for your use case.
  2. Familiarity: It uses standard Spring concepts like dependency injection, auto-configuration, and fluent APIs (like WebClient or JdbcClient), making the learning curve shallow for millions of existing Spring developers.

Why Use Spring AI Over LangChain?

While LangChain is a powerful, provider-agnostic framework that popularized the “chaining” of LLM calls, it is primarily built for the Python ecosystem. Spring AI, on the other hand, is built from the ground up to be idiomatic Java and integrate seamlessly into Spring Boot applications.

Here is why a Java enterprise developer should strongly consider using Spring AI:

The “Idiomatic Java” Advantage

For a Java team, choosing Spring AI means:

  • No Polyglot Complexity: You avoid introducing Python dependencies, virtual environments, and inter-process communication headaches into your production Java environment.
  • Performance: Spring AI runs natively within the Java Virtual Machine (JVM), leveraging its excellent garbage collection and performance optimizations.
  • Tooling: You benefit from static type checking, robust debugging, and the full ecosystem of Java testing frameworks (JUnit, Mockito).

In short, if your application is written in Java and uses Spring Boot, Spring AI is the natural, lowest-friction choice for integrating generative AI.

Key Concepts in Spring AI

To build a basic AI application, you need to understand three core components:

Building a Simple Chat Service

Let’s create a minimal Spring Boot application that uses the ChatClient to generate responses based on a user’s message. For this example, we will use the OpenAI model.

1. Project Setup (Maven)

Add the following to your pom.xml file:

<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>
</dependencies>
<dependencyManagement>
<dependencies>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-bom</artifactId>
<version>1.0.0</version> <type>pom</type>
<scope>import</scope>
</dependency>
</dependencies>
</dependencyManagement>

2. Configuration (application.properties)

You need to provide your AI provider’s API key. Place this in your src/main/resources/application.properties file.

Properties

# Replace with your actual OpenAI API Key

spring.ai.openai.api-key=<YOUR_OPENAI_API_KEY>

3. The Controller (AiController.java)

This class defines a REST endpoint that accepts a message and uses the injected ChatClient to get a response.

Java
package com.example.aidemo;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
@RestController
public class AiController {
private final ChatClient chatClient;
/**
* Spring Boot automatically configures and injects the ChatClient based
* on the dependency and properties.
*/
public AiController(ChatClient.Builder chatClientBuilder) {
// Build the ChatClient instance using the injected builder
this.chatClient = chatClientBuilder.build();
}
@GetMapping("/generate")
public String generate(@RequestParam(value = "message", defaultValue = "Tell me a short, friendly joke.") String message) {
// Use the fluent API to define the prompt and call the model
return chatClient.prompt()
.user(message) // Set the user's input message
.call() // Execute the call to the AI model
.content(); // Extract the plain text content from the response
}
}

4. Run and Test

  1. Run your Spring Boot application.
  2. Test the endpoint: http://localhost:8080/generate?message=Explain%20Spring%20AI%20in%20one%20sentence

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