Spring Ai: Beginner To Guru
Published 5/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.77 GB | Duration: 4h 28m
Published 5/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.77 GB | Duration: 4h 28m
Use Java to Unlock the Power of OpenAI's ChatGPT Artificial Intelligence (AI) with Spring AI
What you'll learn
Learn how to use Spring AI to interact with OpenAI
How to write effective Prompts for Large Language Models
Use Retrieval Augmented Generation to Improve Response Capabilities
Generate Speech from Text
Generate Text from Speech
Requirements
Java and Spring Experience is Required
Understanding of working with RESTful APIs
Able to use Docker to run a Docker Compose Script
Description
Traditionally accessing Artificial Intelligence Models, such as OpenAI's ChatGPT, has been the domain of programming languages Python and Javascript. Not any more. Spring AI unlocks the power of Generative AI for Java developers. The Spring AI project aims to streamline the development of applications that incorporate artificial intelligence functionality without unnecessary complexity. Spring AI provides support for all major Artificial Intelligence models, including:OpenAIAzure OpenAIAmazon BedrockHuggingFaceOllama Google VertextAI (PaLM2 and Gemini)Mistral AIAntrhopicWatsonxAISpring AI also supports image generation AI models from OpenAI and Stability. Retrieval Augmented Generation is an important use case for dealing with AI models. Spring AI includes robust support for all major Embedding Models and all popular vector databases. No prior experience with AI is needed for this course. You will start the course with a solid overview of what Artificial Intelligence is. Next you'll have a hands on section to develop a RESTful API to ask OpenAI's ChatGPT questions. In this section, you will learn how we can instruct the model to return data in the format we desire. Since no AI experience is required, the next section of the course builds upon what you learned with a formal look at Prompt Engineering. Prompt Engineering is a collection techniques to improve the quality and accuracy of responses from AI Models.Retrieval Augmented Generation (RAG) is an important technique to provide Large Language Models with additional information required to the user's query to complete specialized tasks. You will see how we can use RAG to develop AI experts to perform high specialized tasks. The AI models are not just limited to working with textual information. In this course you will also see how to use AI to create images, create audio files from text, and how to transcribe audio files to text. Learn all this and more in Spring A:I Beginner to Guru. Enroll today!
Overview
Section 1: Introduction
Lecture 1 Course Introduction
Lecture 2 Instructor Introduction
Lecture 3 Getting the Most Out of Your Course
Lecture 4 Setting Up Your Development Environment
Lecture 5 Is Your IDE Free Like A Puppy?
Lecture 6 Free 120 Day IntelliJ Trial
Lecture 7 Course Slack Room - Chat with Your Instructor and Other Students
Lecture 8 Github Workflow
Section 2: Introduction to AI
Lecture 9 Introduction
Lecture 10 Types of Artificial Intelligence
Lecture 11 Introduction to Large Language Models (LLMs)
Lecture 12 Overview of OpenAI
Section 3: Introduction to Spring AI with OpenAI
Lecture 13 Introduction
Lecture 14 Get Your OpenAI API Key
Lecture 15 Create New Spring Boot Project
Lecture 16 Configure OpenAI API Keys
Lecture 17 Add OpenAI Service
Lecture 18 Exploring LLM Capabilities
Lecture 19 Create Spring MVC Controller
Lecture 20 Test Controller with Postman
Lecture 21 Using Prompt Templates
Lecture 22 Directing OpenAI to format the Response
Lecture 23 Directing OpenAI to Respond with JSON
Lecture 24 Binding OpenAI Responses With JSON Schema
Lecture 25 OpenAI Response Meta Data
Section 4: Prompt Engineering
Lecture 26 Introduction
Lecture 27 Overview on Prompt Engineering
Lecture 28 Giving Clear and Precise Instructions
Lecture 29 Giving the Model Time to Think
Lecture 30 Summarizing
Lecture 31 Inferring
Lecture 32 Zero Shot and Few Shot Prompting
Lecture 33 Chain of Thought
Lecture 34 Using System Messages
Section 5: Retrieval Augmented Generation - RAG
Lecture 35 Introduction
Lecture 36 Overview of Retrevial Augmented Generation
Lecture 37 Configure Vector Store
Lecture 38 Configure Vector Store Data Load
Lecture 39 Returning Documents from Vector Store
Lecture 40 Improving Prompt Performance - Embedding Config
Lecture 41 Improving Prompt Performance - Trimming Data
Lecture 42 Providing Document Meta Data
Section 6: AI Recommendation Expert with RAG
Lecture 43 Introduction
Lecture 44 Overview of Towing
Lecture 45 Spring Boot Project Review
Lecture 46 Configure Milvus Vector Database
Lecture 47 Vector Store Data to Load
Section 7: OpenAI Functions
Lecture 48 Introduction
Lecture 49 Overview of Function Calling with OpenAI
Lecture 50 Spring Boot Project Review
Lecture 51 Get API Ninjas API Key
Lecture 52 Use System Message to Improve Model Response
Section 8: Spring AI Image Generation and Vision
Lecture 53 Introduction
Lecture 54 Overview of Image Generation with OpenAI
Lecture 55 Code Review of Spring Boot Project
Lecture 56 Using DALL-E 3 OpenAI Model
Lecture 57 Implement Spring MVC Endpoint
Section 9: Spring AI Text to Speech
Lecture 58 Overview of Text to Speech with OpenAI
Lecture 59 Spring Boot Project Review
Lecture 60 Implement Spring MVC Endpoint
Section 10: Spring AI Speech to Text
Lecture 61 Overview of Speech to Text
Any Java Developer wishing to get started using OpenAI