Spring AI 2 - Intermediate

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)

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.

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
  

  Prerequisites

  • Basic Java programming

  • Spring Boot fundamentals

  • REST APIs

  • Maven or Gradle

  • No prior AI experience required

What You'll Learn, with all 3 parts of this course

• 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