AI Debug Assistant

AI Debug Assistant is a Spring Boot library designed to enhance the debugging process for developers by providing detailed error explanations and possible solutions using AI, specifically leveraging the GroqCloud API.

Features

• Error Explanation: Automatically explains exceptions thrown in Spring Boot applications using AI.
• Multi-Language Support: Provides error explanations in multiple languages, ensuring that developers around the world can understand and resolve issues faster.
• Easy Integration: Designed as a drop-in library for existing Spring Boot applications.

Getting Started

Prerequisites

• Java 21 or higher
• Maven
• Spring Boot 3.3.0 or higher

Installation

1. Add the dependency to your Spring Boot project:

For Maven, add the following to your pom.xml:

<dependency>
    <groupId>com.kalayciburak</groupId>
    <artifactId>ai-debug-assistant</artifactId>
    <version>0.0.1-SNAPSHOT</version>
</dependency>

2. Configure your application.yml to include your API key and other necessary settings:

api:
  model: llama3-groq-70b-8192-tool-use-preview
  base-url: https://api.groq.com/openai
  key: your-api-key-here
  response:
    language: en

Usage

After installing and configuring the library, it will automatically handle exceptions thrown within your Spring Boot controllers and provide AI-generated explanations and solutions.

Here's an example of a basic Spring Boot application setup:

@SpringBootApplication(scanBasePackages = "com.kalayciburak")
public class InventoryServiceApplication {

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

}

Example Output

When an exception is thrown in your Spring Boot application, the AI Debug Assistant will intercept the exception and provide a detailed explanation and possible solutions. The output is formatted in a structured JSON format, providing comprehensive details about the exception encountered. This structured output is made possible by a global exception handler integrated within the project, which formats error messages and associated information neatly. Here's an example of the output:

{
  "timestamp": "10-09-2024 23:11:27",
  "type": "ERROR: ENTITY_NOT_FOUND_EXCEPTION",
  "code": "2900",
  "message": "Herhangi bir kayıt bulunamadı.",
  "success": false,
  "status": "NOT_FOUND",
  "detail": {
    "className": "jdk.internal.reflect.DirectConstructorHandleAccessor",
    "methodName": "newInstance",
    "lineNumber": 62,
    "debugMessage": "Original Error Message: 'Herhangi bir kategori bulunamadı.'\n\nExplanation in Turkish: Bu hata, kategori bulunamadığı anlamına gelir.\n\nDetails in Turkish:\n- Hata Mesajı: 'Herhangi bir kategori bulunamadı.'\n- Açıklama: Bu hata, kategori bulunamadığı anlamına gelir.\n\nPossible Causes and Solutions in Turkish:\n- Kategori ismi yanlış yazılmış olabilir. Çözüm: Kategori ismini doğru yazmaya dikkat etmek önemlidir.\n- Kategori veritabanında kaydedilmemiş olabilir. Çözüm: Kategorinin veritabanına kaydedildiğini kontrol etmek önemlidir.\n- Kategori silinmiş olabilir. Çözüm: Kategorinin silindiğini kontrol etmek önemlidir ve eğer öyleyse, onu tekrar oluşturmak gerekir."
  }
}

How It Works

The AI Debug Assistant intercepts exceptions thrown from your controllers, sends a request to the OpenAI API with the error message, and receives a detailed explanation and suggested solutions, which are then logged or can be used further based on your exception handling strategy.

Contribution

Contributions are welcome! For major changes, please open an issue first to discuss what you would like to change.