Spring AI Training Overview
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.
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- 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.
Outline
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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
Training Materials
All Generative AI training students receive comprehensive courseware.
Software Requirements
All attendees must have a modern web browser and an Internet connection.
