Spring AI: Building Intelligent Applications

Course Duration

3 Days

Audience

Employees of federal, state and local governments; and businesses working with the government.

Prerequisites

Java development experience and familiarity with Spring Boot are required. Basic understanding of REST APIs is helpful. No prior AI or machine learning experience is needed.

Course Description

Unlock the full potential of AI and Spring technologies with our comprehensive course designed for developers eager to master the latest advancements in artificial intelligence and application development. This course offers an in-depth exploration of foundational AI principles, cutting-edge AI models, and practical implementation techniques, all within the robust Spring ecosystem.

Learning Objectives

• Grasp the core principles of artificial intelligence and how AI processes information.
• Learn about vector databases and their significance in AI applications.
• Understand Large Language Models (LLMs) and their applications in generating human-like text.
• Create effective prompts to optimize AI model performance.
• Use prompt templates to streamline the prompt creation process.
• Test and refine prompts to achieve desired AI responses.
• Utilize the Spring AI Model API for integrating various AI models.
• Configure Spring Boot to support AI model integration and vector stores.
• Understand the role of ETL (Extract, Transform, Load) processes in data preparation for AI models.
• Create and configure a ChatClient using Spring’s Chat Client API.
• Implement different response types and streaming responses for real-time interactions.
• Use system text, advisors, and chat memory to enhance the flexibility and personalization of chat responses.
• Explore the capabilities of multimodal LLMs, integrating text, images, audio, and video.
• Implement multimodal models using Spring AI’s tools and APIs.
• Understand the benefits of multimodal approaches over traditional single-modality models.
• Evaluate the content generated by AI models to ensure accuracy and relevance.
• Use the RelevancyEvaluator to assess the relevance of AI-generated content.
• Implement JUnit tests to perform Retrieval Augmented Generation (RAG) and validate responses.
• Set up Docker environments for Spring applications to enhance scalability and deployment.
• Integrate vector databases within Docker containers for efficient data retrieval.
• Build and deploy native images using GraalVM for improved performance and reduced memory footprints.

Course Outline

• Explore the foundational principles of AI.
• Gain an in-depth understanding of how AI thinks and processes information.
• Learn how vector databases enhance AI capabilities by efficiently managing high-dimensional data.
• Understanding Large Language Models (LLMs), discovering how they generate human-like text and their applications.
• Understand the concept of tokens in AI models
• Explore the techniques for effectively interacting with large language models to achieve desired outcomes.
• Introduction to AI Models: The basics of building and fine-tuning AI models that drive smart applications.
• Learn the fundamentals of creating impactful prompts and templates to enhance AI performance.
• Discover the foundational concepts of embeddings and how they transform data into actionable insights.
• Gain a basic understanding of the roles messages play in improving AI system functionality.
• Get introduced to the techniques that boost AI-generated content relevance and efficiency.
• Understand the basics of function calling to create dynamic and responsive AI applications.
• Introduce the foundational concepts of prompt engineering and its importance in AI development.
• Learn about various types of prompts and their applications in different AI scenarios.
• Gain insights into crafting prompts that optimize AI model performance and accuracy.
• Discover how to use prompt templates to streamline the prompt creation process and maintain consistency.
• Learn techniques for testing and refining prompts to achieve the desired AI responses.
• Lab: AI Prompts for Developers
• Introduction to Spring AI: Understand its role in simplifying AI development for Java developers.
• Learn the basics of the AI Model API and how it facilitates interaction with various AI models.
• Discover how Spring Boot's auto-configuration and starters streamline the integration of AI models and vector stores.
• Understand the concept of vector stores and their application within the Spring framework.
• Explore the Function Calling API and its use in enabling dynamic and responsive AI applications.
• Get introduced to the fundamentals of ETL (Extract, Transform, Load) data engineering and its importance in preparing data for AI models.
• Learn how to create and configure a ChatClient to build interactive chat applications.
• Use an autoconfigured ChatClient.Builder to streamline the setup and customization of your ChatClient
• Explore the different types of responses supported by ChatClient
• Work with streaming responses to enable real-time, dynamic interactions in your chat applications.
• Utilize default system text with parameters to enhance the flexibility and personalization of chat responses
• Learn about advisors and how they can be used to manage and influence the behavior of your ChatClient.