What is Spring AI , and how to use it: A comprehensive guide
In this article, you’ll talk about a new project called Spring AI , which is designed to help Java developers use big language models, such as ChatGPT, more easily. Basically, we’ll create a simple program that can answer questions about the Swiftie Bowl using information from Wikipedia.
Spring AI is an advanced artificial intelligence (AI) platform designed to assist businesses in automating tasks, analyzing data, and optimizing operations. It utilizes machine learning algorithms and natural language processing (NLP) capabilities to interpret and generate insights from large volumes of unstructured data.
The Beginning of Spring AI
Spring AI is a new project within the Spring framework, although it’s not fully available to the public yet. It’s designed to make it easier for developers to add artificial intelligence features into their applications using languages that run on the Java Virtual Machine (JVM). The project was officially introduced by its creator, Mark Pollack, at the SpringOne conference in the fall of 2023.
In simple terms, Spring AI acts as a common platform that simplifies the integration of various AI tools, such as those provided by OpenAI and Azure OpenAI, into applications developed using Spring. However, it’s important to note that the project is still in its experimental phase and is not considered ready for use in production environments. Nonetheless, it’s worth exploring to understand how AI can be applied to solve different types of problems in your projects.
Applications of Spring AI
Spring AI finds applications across various industries and functions, including:
- Customer service automation: Spring AI can automate customer support processes, such as answering frequently asked questions, resolving issues, and handling routine inquiries.
- Content generation and optimization: Spring AI can analyze content performance, identify trends, and generate optimized content recommendations to improve engagement and conversion rates.
- Predictive analytics: By analyzing historical data and identifying patterns, Spring AI can forecast future trends and outcomes, helping businesses anticipate market changes and make proactive decisions.
Best Practices for Using Spring AI
To optimize the use of Spring AI, businesses should adhere to the following best practices:
- Regular updates and maintenance: Stay abreast of updates and new features released by the Spring AI provider, and perform regular maintenance to ensure optimal performance and reliability.
- Monitoring performance and optimizing usage: Continuously monitor Spring AI’s performance metrics and usage patterns, and make adjustments as needed to improve efficiency and effectiveness.
- Ensuring data privacy and security: Implement robust data privacy and security measures to protect sensitive information and comply with relevant regulations and standar
AI Concepts
Before we start building our example app, let’s quickly understand some key terms:
- Models: These are like smart algorithms that imitate how humans think. They use a bunch of data to make predictions, create text, pictures, or do other clever things.
- Prompts: These are instructions given to the AI models in the form of text. They tell the models what to do and what to produce. Crafting good prompts is important for getting the results you want.
- Prompt Templates: These are frameworks for creating prompts. They let you put in specific details to tailor the instructions to your needs.
- Embeddings: These are like translators for text. They turn words into numbers so that the AI models can understand and work with them.
- Tokens: Tokens are the building blocks of language that the AI models work with. Using more tokens can make using AI services more expensive.
We’ll be using these terms as we build our application.
Setting up a Spring AI application from scratch
Spring AI is a fresh addition that hasn’t been integrated into the Spring Initializr yet. Instead of going through the hassle of creating a basic setup on https://start.spring.io and then adding the necessary components manually, I prefer using the Spring CLI. It’s already equipped with Spring AI support.
Once you’ve got the CLI installed, head to the directory where you’d like your application to reside, fire up the Spring Shell, and execute the following command:
This sets up a basic project to say “Hello World” when communicating with ChatGPT.
Next, grab your ChatGPT API key and save it like this:
To use the application, simply run it using the command “mvnw”.
You can tell it what to do by saying something like “Make a haiku”.
Let’s take a closer look at the code. We’ve already learned how to run the application, so now we’ll explore some specific parts of the code to understand how Spring AI’s API works.
In the SimpleAiController file, you’ll notice that AiClient is used as a dependency that’s been injected.
