Welcome to our deep dive into Spring AI 1.0.0 M2, the latest release designed to simplify AI integration within the Spring framework. This update brings a host of enhancements that streamline the process of incorporating AI functionalities into your applications. In this post, we'll explore these new features and walk through a practical example of using Spring AI 1.0.0 M2 with a TensorFlow model.
What's New in Spring AI 1.0.0 M2?
Spring AI 1.0.0 M2 introduces several exciting updates:
- Simplified Configuration: Less boilerplate code and easier setup for AI components.
- Expanded AI Framework Support: Enhanced compatibility with TensorFlow, PyTorch, and other popular AI frameworks.
- Performance Enhancements: Improved efficiency and scalability for AI applications.
- Improved Documentation: Updated guides and examples for quicker implementation.
Getting Started with Spring AI 1.0.0 M2
To showcase the capabilities of Spring AI 1.0.0 M2, we'll create a simple Spring Boot application that uses TensorFlow for text classification. Follow these steps to get started:
1. Create a New Spring Boot Project
Generate a new Spring Boot project using Spring Initializr. Add the following dependencies:
- Spring Web
- Spring Boot DevTools
- Spring Data JPA (optional)
2. Add TensorFlow Dependency
Update your pom.xml file with the TensorFlow dependency:
<dependencies>
<!-- Spring Boot Starter Web -->
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<!-- TensorFlow Java Library -->
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow</artifactId>
<version>2.11.0</version>
</dependency>
</dependencies>
3. Build the AI Component
Create a service class to handle interactions with the TensorFlow model:
package com.example.springai.service;
import org.springframework.stereotype.Service;
import org.tensorflow.SavedModelBundle;
import org.tensorflow.Tensor;
@Service
public class TextClassificationService {
private final SavedModelBundle model;
public TextClassificationService() {
// Load the pre-trained TensorFlow model
this.model = SavedModelBundle.load("path/to/saved_model", "serve");
}
public String classifyText(String text) {
try (Tensor<String> inputTensor = Tensor.create(text.getBytes("UTF-8"), String.class)) {
// Run inference
Tensor<?> result = model.session().runner()
.feed("input_tensor_name", inputTensor)
.fetch("output_tensor_name")
.run()
.get(0);
// Process result
float[][] output = result.copyTo(new float[1][1]);
return output[0][0] > 0.5 ? "Positive" : "Negative";
} catch (Exception e) {
throw new RuntimeException("Error during text classification", e);
}
}
}
Note: Replace "path/to/saved_model", "input_tensor_name", and "output_tensor_name" with the actual paths and names used in your TensorFlow model.
4. Create a Controller
Add a REST controller to expose an API endpoint for text classification:
package com.example.springai.controller;
import com.example.springai.service.TextClassificationService;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
@RestController
public class TextClassificationController {
private final TextClassificationService classificationService;
public TextClassificationController(TextClassificationService classificationService) {
this.classificationService = classificationService;
}
@GetMapping("/classify")
public String classify(@RequestParam String text) {
return classificationService.classifyText(text);
}
}
Running the Application
Build and Run Your Application
Use Maven to build and run your Spring Boot application:
mvn clean install mvn spring-boot:runTest the API
Open your browser or use a tool like
curlor Postman to test the endpoint:curl "http://localhost:8080/classify?text=I%20love%20Spring%20AI"You should receive a response indicating whether the text is classified as "Positive" or "Negative."
