Build AI Apps with Spring AI, OpenAI, Ollama & SpringBoot

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

14 hours 48 minutes
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

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Overview

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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will explore how to harness the power of Spring AI, OpenAI, and Ollama to create intelligent AI-driven applications using Java and Spring Boot. From understanding the fundamentals of large language models (LLMs) to integrating OpenAI’s API, you'll learn the best practices for building and optimizing AI apps for real-world scenarios. By diving deep into prompt engineering, error handling, and advanced AI concepts like tool calling and Retrieval-Augmented Generation (RAG), this course prepares you to develop robust AI-powered systems. As you progress, you will build key skills around integrating LLMs with various tools, streamlining the messaging process, handling structured outputs, and creating custom advisors. You’ll also explore the exciting realm of multimodal AI by creating and processing images, audio, and vision data through OpenAI’s models. Hands-on projects will ensure that you gain practical experience in developing applications that are both creative and functional, with real-time integration of live data such as weather, currency, and system time. This course is perfect for Java developers, AI enthusiasts, and anyone looking to leverage OpenAI and Spring AI for powerful applications. It is ideal for individuals who have a basic understanding of programming and are looking to develop expertise in integrating AI into Java applications. The course is suitable for intermediate learners, with foundational programming knowledge being essential for the practical exercises and tools covered. By the end of the course, you will be able to integrate AI models with Spring Boot, design and deploy AI-powered systems, build custom message handlers, and process multimodal data like images, text, and audio. You'll also gain hands-on experience in creating real-world applications using Spring AI and OpenAI.

Syllabus

Getting Started With the Course

In this module, we will introduce you to the course structure, detailing the learning objectives and what you will gain. You'll also identify the essential prerequisites to ensure you're ready for the journey ahead. Finally, we will help you set up the tools and resources required for a seamless learning experience.

Introduction to Large Language Models (LLMs), OpenAI & ChatGPT [Theory]

In this module, we will explore the evolution of large language models, from their early stages to the present cutting-edge technologies. We will delve into the revolutionary impact of ChatGPT and similar models on modern AI, along with the benefits and challenges they present in various fields.

Getting Started with Spring AI and OpenAI API

In this module, we will guide you through setting up Spring AI and integrating it with OpenAI to build AI-powered applications. You will get hands-on experience with tools like OpenAI’s Playground and develop a basic AI chat application with Spring’s ChatClient to interact with OpenAI's platform.

Global ErrorHandler to Deal with Exception/Errors using Github Copilot

In this module, we will explore how GitHub Copilot can assist in error handling within your Spring Boot application. You’ll learn how to set up Copilot and implement custom error handling strategies to enhance code reliability and streamline development.

Streamlining Message Passing to LLMs using StringTemplates & PromptTemplates

In this module, we will demonstrate how to streamline AI interactions using StringTemplates and PromptTemplates. You will learn how to construct flexible, dynamic prompts and customize message exchanges to optimize the efficiency of your AI-driven applications.

Spring AI Advisors: Enhancing AI Interactions

In this module, we will introduce Spring AI Advisors and show you how they can be used to enhance and modify AI interactions. You’ll learn to build a custom advisor, starting with a logging advisor, to improve your AI app’s debugging and monitoring capabilities.

Prompt Engineering

In this module, we will dive deep into prompt engineering, teaching you how to craft clear and accurate prompts that lead to meaningful outputs. You will also explore real-world examples and learn techniques to mitigate potential risks such as prompt injection.

Generating Structured Data with OpenAI & Spring AI

In this module, we will teach you how to generate structured outputs from LLMs using prompt engineering and Spring AI tools. You’ll gain hands-on experience in parsing structured data, essential for AI-powered applications that need consistent and organized information.

Augmenting LLMs using Tool Calling with Spring AI

In this module, we will explore how tool calling can augment LLM functionality by integrating external systems. You will learn how to call live data like currency exchange rates and weather information, expanding the scope and accuracy of your AI-powered applications.

RAG in Action: Empowering LLMs with External Knowledge using Spring AI

In this module, we will introduce Retrieval-Augmented Generation (RAG), a technique for providing LLMs with external knowledge. You’ll learn how RAG improves AI performance by integrating relevant, up-to-date information and explore its advantages over traditional methods like prompt stuffing.

Build a Q&A RAG Application with Spring AI

In this module, we will walk you through building a full Q&A application powered by RAG. You’ll explore the process of indexing documents, retrieving relevant knowledge, and generating accurate answers, culminating in the development of an end-to-end AI solution.

Document Ingestion Strategies with Spring AI

In this module, we will teach you best practices for document ingestion within Spring AI. You’ll learn how to process various document types, including text and Word files, and use chunking techniques to structure content for better AI knowledge retrieval.

Exploring Multimodality: Unlocking Visual Capabilities with OpenAI Image Models

In this module, we will delve into the concept of multimodality, where AI can work across different data types. You will explore how OpenAI’s image models can generate visual content from text and learn to customize images based on input variations for better results.

Exploring Multimodality: Unlocking Vision Capabilities with OpenAI

In this module, we will show you how to analyze images using OpenAI’s vision API. You will learn to pass image files from your local system or as multipart data for processing, with real-world applications such as extracting data from invoices.

Mastering Multimodality: Creating and Processing Audio with OpenAI Audio Model

In this module, we will teach you how to work with OpenAI’s audio models, converting text to realistic speech and vice versa. You’ll explore the customization options for both text-to-speech and speech-to-text, providing you with the tools to build voice-enabled AI applications.

Building Local AI Applications with Spring AI and Ollama

In this module, we will explore how to build local AI applications using Ollama and Spring AI. You’ll learn to set up your environment for running LLMs on your local machine and integrate Spring AI seamlessly, enabling you to develop AI-powered applications without relying on cloud services.