Spring AI: The Secret Weapon for Smarter, More Engaging Spring Boot Applications

A Deep Dive into Spring AI for Spring Boot Developers


The world of AI is booming, and Spring Boot developers are uniquely positioned to leverage its potential. But jumping into AI can feel daunting, with unfamiliar languages and frameworks. Here's where Spring AI comes in, acting as a bridge between the familiar Spring Framework and the exciting world of Artificial Intelligence.

Spring AI is a powerful API that opens up a world of AI capabilities for Spring Boot developers. With its portable API across AI providers like OpenAI, Microsoft, Amazon, Google, Huggingface, and more, developers can easily integrate Chat, Text to Image, and Embedding models into their applications. This API supports both synchronous and stream API options, allowing developers to access model-specific features effortlessly.

In addition to AI capabilities, Spring AI also provides support for Vector Stores, offering a unique SQL-like metadata filter API that is portable across various databases. With support for 8 vector databases, managing and querying vector data is simplified, making it easier to incorporate AI-driven insights into your applications.

Features of Spring AI

Portable API for AI Providers

Chat, Text to Image, and Embedding Models: Spring AI offers a portable API across AI providers, supporting models from OpenAI, Microsoft, Amazon, Google, Huggingface, and more. Both synchronous and stream API options are available, with support for accessing model-specific features.

Portable API for Vector Store Providers

Vector Store Support: Spring AI provides a portable API across Vector Store providers, including a novel SQL-like metadata filter API. This feature supports 8 vector databases, making it easier to manage and query vector data.

Function Calling

Invoke POJO java.util.Function Object: Spring AI simplifies function calling, allowing AI models to easily invoke POJO java.util.Function objects.

Spring Boot Auto Configuration and Starters

Seamless Integration: Spring Boot Auto Configuration and Starters are available for AI Models and Vector Stores, making integration into Spring Boot applications straightforward.

ETL Framework for Data Engineering

Loading Data into Vector Databases: Spring AI includes an ETL framework for Data Engineering, facilitating the loading of data into vector databases. This framework helps implement the Retrieval Augmented Generation pattern, enabling data incorporation into AI model responses.

Integrating Spring AI with Spring Boot

Chat Completion Using OpenAI

The Chat Completion API offers developers the ability to integrate AI-powered chat completion capabilities into their applications. It leverages pre-trained language models, such as GPT (Generative Pre-trained Transformer), to generate human-like responses to user inputs in natural language.

  1. You will need to create an API with OpenAI to access ChatGPT models. Create an account at OpenAI signup page and generate the token on the API Keys page.

  2. Add dependency to your project

    1. Maven pom.xml

    2. Gradle build.gradle

       dependencies {
           implementation 'org.springframework.ai:spring-ai-openai-spring-boot-starter'

Rest Controller Example

public class ChatController {

    private final OpenAiChatClient  chatClient;

    public ChatController(OpenAiChatClient chatClient) {
        this.chatClient = chatClient;

    public String generate(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        return chatClient.call(message);


Spring AI Use Cases

1. AI-powered Chatbots:

Imagine chatbots that can hold natural conversations with your users, answer their questions effectively, and even personalize interactions. Spring AI makes this a reality by enabling you to integrate Chat models like OpenAI's ChatGPT. This can revolutionize customer service, provide 24/7 support, and enhance user engagement within your application.

2. Intelligent Automation:

Repetitive tasks can be a drag for both users and developers. Spring AI empowers you to automate these tasks using AI models for data processing, classification, or even code generation. This frees up valuable development resources and streamlines workflows within your application.

3. Predictive Analytics:

Unlock the power of data with Spring AI. Integrate AI models for tasks like sentiment analysis, anomaly detection, or even future trend prediction. This allows you to gain deeper insights from your data and make data-driven decisions for your application.

4. Smart Content Creation and Personalization:

Spring AI can be a game-changer for content creation. Use Text-to-Image models to generate creative visuals based on text descriptions. Personalize user experiences by tailoring content recommendations or product suggestions using AI models that understand user preferences.


Spring AI empowers Spring Boot developers to easily integrate AI capabilities into their applications. With a portable API, support for multiple AI and Vector Store providers, and features like function calling and ETL framework, Spring AI simplifies AI development. It opens up new possibilities for building intelligent, innovative applications with Spring Boot.


  1. Official Spring Doc

  2. Image Generation using Stability AI Demo

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