Spring AI is an application framework that makes it easier to build AI-powered features into your Java applications. If you're already familiar with the Spring ecosystem, you'll find it feels natural to use.

The main goal of Spring AI is to simplify the process of interacting with large language models (LLMs) and other AI tools. Instead of writing complex code to handle API requests and parse responses from different AI providers, you can use Spring AI's straightforward abstractions. This lets you focus on what your application does, not the low-level details of AI integration.

Spring AI comes with several key features designed to make development smoother.

Portable API: The framework provides a consistent API for interacting with different models. Think of it like Java's JDBC for databases. You write your code once against the Spring AI interface, and you can switch the underlying AI model provider (from OpenAI to Gemini, for instance) with only a configuration change. No code rewrite is necessary.

Vector Database Integration: For more advanced use cases, like building chatbots that remember past conversations or can answer questions about your own documents, you need a way to store and retrieve relevant information. Spring AI integrates with vector databases (like Chroma, Pinecone, and Redis) to help you implement Retrieval-Augmented Generation (RAG), a powerful pattern for building smarter AI applications.

To get started, you'll need a Spring Boot project. You can create one easily using the Spring Initializr. The key step is adding the right dependencies.

Here's what you need to include in your pom.xml file for a Maven project. This example uses the OpenAI starter, but there are similar ones for other providers.

You'll also need to add Spring's repository to fetch the AI artifacts, as they might not be in Maven Central yet.

To communicate with an AI model provider like OpenAI, you need an API key. This key authenticates your application and proves you have permission to use the service.

Once you have your key, the simplest way to configure it is in your application.properties file.

Just replace YOUR_API_KEY_HERE with your actual key. It's important to keep this key secure. For production applications, you should use a more secure method like environment variables or a secrets management tool instead of hardcoding it in a properties file.

With these steps complete, your Spring Boot application is ready to start making calls to an AI model.

Let's check your understanding of these initial concepts.

Now you have a basic grasp of what Spring AI is, its core features, and how to set up a project to use it.