Spring AI 2 - Complete tutorial

Name: Spring AI 2 - Complete tutorial
Rating: 3.2 (2 reviews)

Complete tutorial for developing AI enabled java application

Created byNithesh S

Last updated 7/2026

English

What you'll learn

Build AI-powered Java applications using Spring AI 2 by integrating Large Language Models (LLMs) into Spring Boot projects.
Integrate popular AI providers such as OpenAI, Anthropic, Google Gemini, Ollama, and Azure OpenAI using Spring AI's unified API.
Implement Retrieval-Augmented Generation (RAG) by connecting vector databases, embedding models, and document retrieval to create context-aware AI applications.
Develop AI chat applications with prompt engineering, conversation memory, streaming responses, and structured outputs.
Use Spring AI Advisors and Tools to enable function calling, tool execution, and intelligent workflows within AI applications.
Work with embeddings and vector stores to perform semantic search, document indexing, and similarity-based retrieval.
Build multimodal AI applications that process text, images, and other supported input types using Spring AI.
Apply production-ready best practices for AI application development, including configuration, testing, observability, error handling, and security.

Course content

17 sections • 64 lectures • 6h 58m total length

Introduction1:29
Know your instructor0:56
Learning Path2:28
Environment setup3:06
Update1:06

Introduction2:42
Explore how tool calling lets LLMs invoke external APIs to act as real agents, using a weather example, and compare static versus dynamic tool declarations in Spring AI.
Static tool calling7:43
This lecture explains static tool calling in spring ai with groq keys and openai, shows a weather tool integration, and compares tools and toolNames approaches with a quick calculation example.
Dynamic tool calling3:46

Tool memory introduction2:14
Explicit chat history9:13
Build an explicit chat history pattern in a Spring Boot app by persisting chat-memory entries in an in-memory H2 database with JPA and using history to inform LLM prompts.
Session window summary12:06
Tool context5:04
Discover tool context, a pattern that blends memory and tools to build a personalized assistant, while the LLM never sees sensitive data like home city, language, and unit.
Redis cache12:35
Demonstrates semantic vector memory using a Redis cache to store and retrieve semantically similar queries, reducing LLM calls. Configures Redis, Ollama embeddings, and a Redis vector store to cache responses.

ETL Introduction1:16
Build an ETL pipeline for RAG applications that ingests documents from URLs and file uploads, chunks data for context with TokenTextSplitter, and enriches metadata with LLMs while applying security redaction.
Basic ETL11:47
Build a basic ETL in a Spring app that reads a URL HTML page, extracts and chunks content, enriches with summaries and keywords, and ingests into a Redis vector store.
Batch ETL5:14
Process batch ETL ingestion by parallelizing URL extraction and enrichment with Java completable futures, then load documents into a Redis vector store, with robust error handling.
Custom Formatted9:42
Learn to manage custom metadata and content formatting in Spring AI, using a default content formatter as a firewall to tailor embedding and inference views.
Custom Enricher8:31
Build a custom document transformer that redacts sensitive metadata and content, then applies summary and keyword enrichers before storing in a Redis vector store.

Basic Vision API4:29
Explore vision and multimodal AI that understands images and text, describes images, and extracts text for applications in product photo analysis and document scanning.
Image Compare5:36
Explore in-memory image comparison using an api to pass two images to an llm, identify differences and similarities, with mime parsing and byte array resources.
OCR Structured Output4:31
Build an API that accepts receipt images, extracts structured data with Spring AI, and returns type-safe Java objects like document data with title, date, items, total, and notes.
Generating Image4:31
Explore image generation with spring ai 2 by posting a json prompt to huggingface via a rest client and returning a jpg image.

Text to Speech7:12
Explore speech-to-text and text-to-speech in Spring AI by building an API that performs transcription and synthesis on audio, enabling hands-free input and audible content.
Speech to Text3:10
Learn to build a speech-to-text API using a whisper transcription model, handling a multipart audio file and a temp file to return the transcribed text for an LLM workflow.
Voice Chat3:43
Voice Vision Analysis3:50

Requirements

Familiarity with Java and Spring Boot fundamentals, including dependency injection and REST APIs.
An API key for at least one supported AI provider (such as OpenAI, Google Gemini, Anthropic, or Azure OpenAI) or a local Ollama installation for running open-source models.
No prior experience with Artificial Intelligence, Machine Learning, or Large Language Models is required—these concepts will be introduced throughout the course.

Description

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

Most of the videos are preview enabled, I think more than 85%. You dont have to buy this course to undestand what this teaches!! Just watch preview enabled videos, if you think, it makes sense, and you need source code to run; without writing it or without asking LLM to write it; then, go ahead and buy!!!

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

Who this course is for:

Java developers who want to build AI-powered applications using Spring AI 2.
Backend developers interested in implementing AI features such as chatbots, RAG, embeddings, and function calling.
Developers transitioning into AI application development without requiring a Machine Learning background.
Architects and technical leads exploring production-ready AI solutions for Java and Spring-based systems.

Spring AI 2 - Complete tutorial

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 9min

Core Chat API5 lectures • 22min

Streaming3 lectures • 17min

Tool Calling3 lectures • 14min

Chat Memory5 lectures • 41min

Retrieval-Augmented Generation2 lectures • 24min

Metadata Filtering1 lecture • 15min

ETL & Document Ingestion5 lectures • 37min

Vision API4 lectures • 19min

Audio & Multimodal4 lectures • 18min

Requirements

Description

Who this course is for: