Spring Ai For Beginners : Build Genai Llm Apps In Easy Steps

A Step-by-Step Guide to Master Spring AI

What you'll learn

Learn what Spring AI is how it simplifies using LLMs in our applications

Use OpenAI LLMS in a Spring Boot application

Use Open Source LLMS like Mistral,Gemma in a Spring Boot application

Run Open Source LLMs on your local machine using OLLAMA

Use PromptTemplates to reuse and build dynamic prompts

Learn why and how to maintain Chat History

Learn what embeddings are and use the Embeddings Model to find text Similarity

Understand what a Vector Store is and use it to store and retrieve Embeddings

Understand the process of Retrieval Augmented Generation(RAG)

Implement (RAG) to use our own data with LLMs in simple steps

Analyze images using Multi Modal Models

Build multiple LLM APPs using ThymeLeaf and Spring AI

Master Function Calling and Text Moderations

All in simple steps

Requirements

Knowledge of Spring Boot and Java

OpenAI Account to work with OpenAI LLMs

Description

Welcome to Spring AI for Beginners!This course is designed to provide a gentle, step-by-step introduction to Spring AI, guiding youfrom the basics to more advanced concepts. Whether you're a complete novice or have someexperience with AI, this course will help you understand and leverage the power of Spring AI forbuilding AI-powered applications.Course Goals:- Gradual Learning: Learn Spring AI gradually from basic to advanced topics with clear andconcise instructions.- Comprehensive Understanding: Understand why Spring AI is a powerful tool for building AIapplications and how it simplifies the integration of language models into your projects.- Hands-On Experience: Gain practical experience with essential Spring AI features such asprompt templates, chains, agents, document loaders, output parsers, and model classes.What You Will Learn:- Introduction to Spring AI: Get started with the basics of Spring AI and understand its coreconcepts.- Building Blocks of Spring AI: Learn about prompt templates, chains, agents, document loaders,output parsers, and model classes.- Creating AI Applications: See how these features come together to create a smart and flexible- Practical Coding: Write and run code examples to get a hands-on sense of how Spring AIdevelopment looks like.Course Structure:- Concise Chapters: Each chapter focuses on a specific topic in Spring AI programming,ensuring you gain a deep understanding of each concept.- Interactive Learning: Code along with the examples provided to reinforce your learning and buildyour skills.By the end of this course, you will:Learn what Spring AI is how it simplifies  using LLMs in our applicationsUse OpenAI LLMs in a Spring Boot applicationUse Open Source LLMs like Mistral,Gemma in a Spring Boot applicationRun Open Source LLMs on your local machine using OLLAMAUse PromptTemplates to reuse and build dynamic prompts Learn why and how to maintain Chat HistoryLearn what embeddings are and use the Embeddings Model to find text SimilarityUnderstand what a Vector Store is and use it to store and retrieve EmbeddingsUnderstand the process of Retrieval Augmented Generation(RAG) Implement  (RAG) to use our own data with LLMs in simple stepsAnalyze images using Multi Modal ModelsBuild multiple LLM APPs using Thymeleaf and Spring AIAll in simple steps

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Private Course Feedback Link

Lecture 3 Download Completed Projects

Lecture 4 Download Prompts Document

Section 2: The Fundamentals

Lecture 5 What is GenAI

Lecture 6 What is OpenAI

Lecture 7 Other LLMs

Lecture 8 What is Spring AI

Lecture 9 Spring AI Documentation

Section 3: Software Setup

Lecture 10 Setup OpenAI Account

Lecture 11 Setup API Key

Lecture 12 OpenAI Playground in action

Lecture 13 Driving models behaviour with options

Lecture 14 Setup Open Source LLMs

Section 4: Spring AI in Action

Lecture 15 Setup Project

Lecture 16 Generic vs Specific Classes

Lecture 17 Spring AI in action

Lecture 18 Do LLMs have memory?

Lecture 19 Advisors

Lecture 20 Configure Memory for Chat

Lecture 21 Configure ChatOptions

Lecture 22 Use Open Source Models Locally

Section 5: Prompt Templates

Lecture 23 Introduction

Lecture 24 Create a Travel Guide App

Lecture 25 Create a Cuisine Helper

Lecture 26 Improve the prompt

Section 6: Embeddings

Lecture 27 Introduction

Lecture 28 Using the Embeddings Model

Lecture 29 Similarity Finder

Section 7: Vector Stores

Lecture 30 Introduction

Lecture 31 Update Project

Lecture 32 Code Walk Through

Lecture 33 TokenTextSplitter

Lecture 34 Setup ChromaDB

Lecture 35 Load Data in to Vector Store

Lecture 36 Implement Job Search Helper

Lecture 37 More Search Options

Section 8: RAG - Working With Documents

Lecture 38 What is RAG

Lecture 39 UseCase and Code Walkthrough

Lecture 40 Implement RAG Part 1

Lecture 41 Implement RAG Part 2

Lecture 42 Test

Section 9: Image Processing

Lecture 43 Introduction

Lecture 44 Generate a Image

Lecture 45 Image Analysis Introduction

Lecture 46 Create Image Analyzer App Part 1

Lecture 47 Create Image Analyzer App Part 2

Lecture 48 Test

Lecture 49 Few More Usecases

Lecture 50 Create a Diet Helper App

Section 10: Audio

Lecture 51 Introduction

Lecture 52 Speech To Text

Lecture 53 Set more options

Lecture 54 Text To Speech

Section 11: Function Calling

Lecture 55 Introduction

Lecture 56 Create the function

Lecture 57 Configure the bean

Lecture 58 Create Service Method

Lecture 59 Test

Section 12: Moderations

Lecture 60 Introduction

Lecture 61 Moderate Text

Java Developers who want to use Spring AI to build GenAI LLM applications,Any student who has completed my Spring Boot Courses