
This lesson introduces you to the fundamentals of the Java Spring AI framework, highlighting its key features and benefits. You'll explore the differences between Spring Boot and Spring AI, gaining a clear understanding of their roles in modern AI-powered applications. The lesson covers the architecture of Spring AI and provides step-by-step guidance on setting up Java (JDK), configuring Spring Boot with Eclipse IDE, and managing dependencies using Maven or Gradle, ensuring a smooth development environment.
In this practice, you will learn how to install and set up the JDK, Eclipse IDE, Spring Tool Suite 4, Maven, and Postman on a Windows system
In this practice, you'll create a Spring Boot project and set up Spring AI to build an AI-powered application. You'll configure the project with the necessary dependencies, set up the development environment, and ensure seamless integration of Spring AI, laying the foundation for intelligent application development.
Lesson 2 delves into the core components of Spring AI, explaining their roles in building AI-powered applications. You'll explore key Spring AI components and gain an overview of Hugging Face API models. The lesson provides a step-by-step guide to integrating the Hugging Face API with Spring AI, including the process of generating an API key for seamless access to AI models.
In this practice, you will learn how to set up Spring AI with Hugging Face APIs. It includes integrating multiple AI models for chat generation, text summarization, and sentiment analysis. Additionally, you will test these APIs using Postman to validate their responses.
In this practice, you'll build a UI for users to input text, process it using AI models via Hugging Face REST APIs, and display the results. Using Thymeleaf, HTML, JavaScript, and jQuery, you'll handle input, send API requests, and present AI-generated responses in a clear and interactive format.
Lesson 3 introduces REST APIs and their importance in AI applications. You'll explore the Google Gemini API, learn how to generate an API key, and test it using Postman. The lesson also covers developing AI-powered endpoints in Spring Boot, utilizing AI for text generation and NLP tasks, and handling input and output with AI models to build intelligent applications.
In this practice, you'll create an AI-powered chatbot using Spring AI, Google Gemini API, and React.js. The chatbot will allow users to input text, process it using Google Gemini’s AI models, and display AI-generated responses dynamically.
Lesson 4 covers the introduction and setup of the AI-Powered Automated Email Reply Assistant using Spring AI. You'll learn how to integrate the Google Gemini API into a Spring Boot project, create the service and controller layers, and serve the project to verify AI-generated responses. Additionally, the lesson includes best practices for error handling and debugging AI responses to ensure a smooth and efficient application.
In this practice, you will build a smart AI-powered Automated Email Reply Assistant using Spring AI, the Google Gemini API, and React.js. This assistant will generate automated email responses based on received emails and selected tone.
This comprehensive course delves into the world of Java Spring AI, offering a structured approach to building AI-driven applications using the powerful Spring AI Framework. The course begins with a thorough introduction to Spring AI, explaining its architecture, key components, and how it seamlessly integrates AI capabilities into Java-based applications. Students will gain insights into different AI models and APIs supported by Spring AI, with a special focus on using Hugging Face models and the Gemini API. By understanding these AI-powered tools, learners will be able to incorporate natural language processing (NLP), machine learning, and generative AI capabilities into their applications, making them more intelligent and efficient.
As the course progresses, students will learn how to build AI-powered REST APIs using Spring Boot. This module emphasizes hands-on development, guiding learners through the process of creating, configuring, and deploying AI-enhanced APIs. The integration of Hugging Face models and the Gemini API into RESTful services enables applications to generate responses, analyze text, and automate decision-making. Through practical exercises and real-world scenarios, students will understand how to structure their APIs, manage data flow, and optimize AI performance within their applications. This segment ensures that learners are equipped with the necessary skills to develop scalable and production-ready AI-powered backend services.
The final part of the course focuses on applying AI capabilities to real-world use cases by building an AI-powered Automated Email Reply Assistant. This project-based module guides students in integrating AI-driven text processing and response generation into an automated email management system. By leveraging Spring AI, students will develop an intelligent assistant that understands email content, categorizes messages, and generates appropriate replies based on predefined AI models. This hands-on project solidifies the concepts covered in the course, providing students with practical experience in building AI solutions that enhance productivity and automation. By the end of the course, learners will have the knowledge and skills to design, develop, and deploy AI-powered applications using Spring AI, making them well-equipped for modern AI-driven software development.