Spring AI 2 - Complete tutorial

Spring AI 2: Build Production-Ready AI Applications with Java & Spring Boot

Artificial Intelligence is transforming software development, and Spring AI 2 brings enterprise-grade AI capabilities directly into the Spring ecosystem. This course is designed for Java developers, Spring Boot developers, and software architects who want to build modern AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, Model Context Protocol (MCP), multimodal AI, and production-ready architectures.

Starting with the fundamentals, you'll learn how to integrate leading AI models such as OpenAI, Gemini, Anthropic Claude, Ollama, and Azure OpenAI into Spring Boot applications using Spring AI 2. You'll then progress to advanced enterprise topics including vector databases, document ingestion pipelines, metadata filtering, observability, security, knowledge graphs, and multi-agent orchestration.

Unlike theory-only courses, every lesson is backed by hands-on projects, real-world examples, and production best practices that you can immediately apply in your own applications.

By the end of this course, you'll have the skills to design, build, deploy, and monitor intelligent Spring Boot applications that are scalable, secure, and enterprise-ready.

What You'll Learn

• Build AI-powered applications using Spring AI 2

• Integrate OpenAI, Gemini, Claude, Ollama, and Azure OpenAI

• Create conversational AI with Chat API and Streaming

• Implement Tool Calling and Function Calling

• Build AI applications with persistent Chat Memory

• Develop Retrieval-Augmented Generation (RAG) applications

• Perform Metadata Filtering for accurate document retrieval

• Build ETL pipelines for document ingestion

• Process PDFs, Word documents, HTML, Markdown, and websites

• Build Vision and Multimodal AI applications

• Convert Speech-to-Text (STT) and Text-to-Speech (TTS)

• Design Multi-Agent AI systems

• Protect applications from Prompt Injection attacks

• Monitor AI applications using Observability and Tracing

• Build Knowledge Graph RAG solutions

• Integrate external tools using Model Context Protocol (MCP)

• Develop reusable AI Agent Skills

• Deploy AI applications to production
  

  Course Curriculum

  Path 1 – Beginner

  Build a strong foundation with Spring AI.

  • Lesson 01: Core Chat API

  • Lesson 02: Streaming Responses

  • Lesson 03: Tool Calling

  • Lesson 04: Chat Memory

  • Lesson 05: Retrieval-Augmented Generation (RAG)

  Path 2 – Intermediate

  Learn enterprise AI application development.

  • Lesson 06: Metadata Filtering

  • Lesson 07: ETL & Document Ingestion

  • Lesson 08: Vision & Multimodal AI

  • Lesson 09: Audio (Speech-to-Text & Text-to-Speech)

  • Lesson 10: Multi-Agent Orchestration

  Path 3 – Advanced

  Master production-ready AI architecture.

  • Lesson 11: Security & Prompt Injection Defense

  • Lesson 12: Observability & Monitoring

  • Lesson 13: Knowledge Graph RAG

  • Lesson 14: MCP (Model Context Protocol) Integration

  • Lesson 15: Agent Skills & Intelligent Workflows