Spring AI is a project that aims to streamline the development of applications that incorporate artificial intelligence functionality. It provides abstractions that serve as the foundation for developing AI applications. These abstractions have multiple implementations, enabling easy component swapping with minimal code changes. Spring AI supports all major Model providers such as OpenAI, Microsoft, Amazon, Google, and Huggingface.
What is Spring AI?
Spring AI is a framework that simplifies the development of AI applications on the Java platform. It provides a set of abstractions that can be used to build AI-powered applications without having to become an expert in machine learning or deep learning.
What does Spring AI offer?
Spring AI offers a number of features that can help developers build AI applications more easily. These features include:
- A unified programming model for AI applications
- Support for multiple model providers
- Easy integration with Spring Boot applications
- A wide range of pre-built components for common AI tasks
How does Spring AI work?
Spring AI works by providing a set of abstractions that can be used to build AI applications.
- Models: Models are the core of any AI application. They are the components that learn from data and make predictions. Spring AI supports a variety of model types, including deep learning models, natural language processing models, and computer vision models.
- Data: Data is essential for training AI models. Spring AI provides a number of features for working with data, including data loading, preprocessing, and transformation.
- Inference: Inference is the process of using a trained model to make predictions on new data. Spring AI provides a number of features for performing inference, including model serving and batch processing.
Benefits of Spring AI
There are a number of benefits to using Spring AI for developing AI applications. These benefits include:
- Spring AI’s abstractions can help developers build AI applications with agility.
- Spring AI’s abstractions can help to reduce the complexity of AI application code.
- Spring AI’s abstractions can help to improve the maintainability of AI applications.
- Spring AI’s support for multiple model providers gives developers greater flexibility in choosing the right tools for their needs.
Code Sample
// Import the Spring AI classes
import org.springframework.ai.api.Model;
import org.springframework.ai.api.Prediction;
import org.springframework.ai.api.ModelProvider;
// Load a pre-trained model
Model model = ModelProvider.load("openai-gpt3");
// Prepare the input data
String inputText = "What is the weather like today?";
// Make a prediction
Prediction prediction = model.predict(inputText);
// Get the predicted text
String predictedText = prediction.getString();
// Print the predicted text
System.out.println(predictedText);This code sample above shows how to load a pre-trained model from OpenAI GPT-3, make a prediction on a piece of text, and get the predicted text.
In summary, Spring AI provides a unified programming model, support for multiple model providers, and a wide range of pre-built components for common AI tasks.
If you are interested in learning more, visit the Spring AI documentation: https://docs.spring.io/spring-ai/reference/1.0-SNAPSHOT/index.html
Also stay tuned for another article comparing Spring AI and Langchain4j!!
References
1. https://docs.spring.io/spring-ai/reference/1.0-SNAPSHOT/index.html
