Unlocking the Power of ONNX Transformers Model with Spring AI: A Step-by-Step Guide

Prerequisites

Before you begin, make sure you have the following installed on your system:

• Java 8 or higher
• Spring Boot 2.3 or higher
• ONNX Runtime 1.8 or higher
• Maven or Gradle

Step 1: Setting Up the Project

Let’s create a new Spring Boot project using Maven:

<dependency>
  <groupId>org.springframework.boot</groupId>
  <artifactId>spring-boot-starter-web</artifactId>
</dependency>

<dependency>
  <groupId>com.microsoft.onnxruntime</groupId>
  <artifactId>onnxruntime</artifactId>
  <version>1.8.0</version>
</dependency>

Create a new directory for your project and navigate to it in your terminal or command prompt. Run the following command to generate a new Spring Boot project:

mvn archetype:generate -DgroupId=com.example -DartifactId=onnx-transformers-spring-ai -DarchetypeArtifactId=maven-archetype-quickstart

Step 2: Loading the ONNX Transformers Model

Next, we’ll load the pre-trained ONNX transformers model using the ONNX Runtime:

import com.microsoft.onnxruntime.OnnxRuntime;
import com.microsoft.onnxruntime.OrtEnvironment;

@Service
public class TransformersService {

  private OrtEnvironment ortEnvironment;
  private OnnxRuntime onnxRuntime;

  @PostConstruct
  public void init() {
    ortEnvironment = OrtEnvironment.getEnvironment();
    onnxRuntime = ortEnvironment.createInferenceSession("transformers_model.onnx");
  }
}

In the above code, we’re creating an instance of the `OrtEnvironment` class, which provides a way to interact with the ONNX Runtime. We then create an instance of the `OnnxRuntime` class, passing the path to our pre-trained ONNX model as an argument.

Step 3: Creating an Inference API

Now that we have the model loaded, let’s create an API to perform inference:

@RestController
@RequestMapping("/api/transformers")
public class TransformersController {

  @Autowired
  private TransformersService transformersService;

  @PostMapping("/infer")
  public String infer(@RequestBody String inputText) {
    // Pre-processing
    String processedInput = preprocessInput(inputText);

    // Create input tensor
    Tensor inputTensor = transformersService.getInputTensor(processedInput);

    // Run inference
    Tensor outputTensor = transformersService.runInference(inputTensor);

    // Post-processing
    String outputText = postProcessOutput(outputTensor);

    return outputText;
  }
}

In this example, we’re creating a REST API with a single endpoint `/infer` that accepts a string input. We’re then pre-processing the input, creating an input tensor, running the inference using the `onnxRuntime`, and post-processing the output.

Step 4: Integrating with Spring AI

To integrate our ONNX transformers model with Spring AI, we’ll create a new configuration class:

@Configuration
public class TransformersConfig {

  @Bean
  public TransformersService transformersService() {
    return new TransformersService();
  }
}

This configuration class creates a bean for our `TransformersService` class, making it available for injection in our application.

Putting it all Together

Now that we’ve completed the integration process, let’s test our application:

@SpringBootApplication
public class OnnxTransformersSpringAiApplication {

  public static void main(String[] args) {
    SpringApplication.run(OnnxTransformersSpringAiApplication.class, args);
  }
}

Run the application using the following command:

mvn spring-boot:run

Use a tool like Postman or cURL to send a POST request to the `/api/transformers/infer` endpoint with a sample input string. You should receive the output of the transformers model in response.

Conclusion

In this comprehensive guide, we’ve demonstrated how to use the ONNX transformers model with Spring AI. By following these steps, you can unlock the full potential of transformers models in your Spring-based applications. Remember to experiment with different models, fine-tune hyperparameters, and explore the vast possibilities of AI with Spring AI.

We hope this article has been informative and helpful. Happy coding!

Frequently Asked Question

Get started with ONNX Transformers model and Spring AI with these frequently asked questions!