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Developing Generative AI Applications with Spring

This Spring AI training teaches developers how to create intelligent applications using state-of-the-art AI models and the Spring AI framework. Participants learn how to integrate popular AI models...

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Course Code AI-152
Duration 2 days
Available Formats Classroom

Overview

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This Spring AI training teaches developers how to create intelligent applications using state-of-the-art AI models and the Spring AI framework. Participants learn how to integrate popular AI models from Open AI, Amazon, and other leading AI providers, fine-tune model behavior, build chat applications with natural language processing and generation, create stunning visuals with image generation, implement semantic search, and employ advanced techniques like Retrieval-Augmented Generation (RAG) and SQL generation.

Skills Gained

  • Include and configure Spring AI dependencies within a Spring Boot application
  • Configure Spring AI to work seamlessly with various foundational models
  • Utilize the ChatModel and ChatClient classes to interact with different models
  • Implement effective state management for chat-based foundational models directly from your application code
  • Control the output of foundational models, tailoring it to produce Java objects or program code that align with your application's requirements
  • Understand and apply various forms of prompt engineering, including single-shot, few-shot, Chain of Thought, React Flow, and more
  • Leverage embeddings stored in vector stores to perform semantic search, enhancing the relevance and accuracy of AI-generated content
  • Use Advisors to implement RAG, improving the quality and specificity of generated outputs
  • Execute image generation tasks from within a Spring Boot application, expanding the range of AI-driven features
  • Develop applications capable of generating SQL code to perform complex database queries autonomously
  • Implement and manage local functions within your AI-driven Spring Boot applications

Prerequisites

All learners must be familiar with Java and Spring Boot.

Course Details

Training Materials

All Generative AI training students receive comprehensive courseware.

Software Requirements

All attendees must have a modern web browser and an Internet connection.

Outline

  • Introduction to Spring AI
    • Overview of supported models and modalities
    • Key abstractions in Spring AI
    • Starter dependencies and their configurations
    • Essential properties and credential management
    • Basic usage patterns for integrating Spring AI into applications
  • Quick Start with Spring AI's ChatClient and OpenAI
    • In-Depth Coverage of Chat Models
    • Detailed exploration of Chat Models from Amazon Bedrock, OpenAI, Azure, and Ollama
    • Dependency management and model enablement
    • Autoconfiguration and key properties for each provider
  • Working with Chat Models
  • Understanding Chat Properties
    • In-depth explanation of properties like TopK, TopP, Temperature
    • Frequency & Presence Penalties and their impact on outputs
    • Logit Bias, Max Tokens, Stop Sequences, and Response Formats
  • ChatClient Features
    • Configuring retry behavior and overriding default property settings
    • Roles and system messages within ChatClient
    • Advisors, ChatMemoryAdvisor, and managing conversational context
    • Entity recognition and handling streaming responses
  • Image Generation
    • Fundamentals of image generation and its differences from chat models
    • Key properties for controlling image generation output
  • Local Functions
    • The role and benefits of client-side functions
    • Implementation strategies and limitations of local functions within Spring AI
  • Prompt Engineering
    • Techniques including One-shot, Few-shot, and Chain of Thought
    • Concepts of Retrieval-Augmented Generation (RAG) and the ReACT flow
  • Embeddings and Semantic Search
    • Understanding embeddings and their application in semantic search
    • Implementation of semantic search with TransformersEmbeddingModel
  • Embeddings and Vector Stores
    • Types of Vector Stores and their integration with embeddings
    • Workflow of semantic search using vector stores
    • Building and querying a simple vector store
  • Retrieval-Augmented Generation (RAG)
    • Comprehensive understanding of the RAG flow
    • Utilizing QuestionAnswerAdvisor to enhance query responses
  • The Do-It-Yourself RAG Approach
    • Constructing multi-step flows for advanced RAG implementations
    • Implementing RAG workflows without relying on embeddings
  • SQL Generation
    • Building multi-step flows to generate SQL queries dynamically
    • Integrating AI-driven SQL generation into applications
  • AI-based Database Queries
  • Conclusion
    • Recap of key concepts and techniques
    • Q&A and course completion
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