本课程教授如何利用 Spring AI 2 框架在 Spring Boot 中构建生产级别的 AI 应用，涵盖了从多主流大模型接入、多模态处理、文档 ETL 与知识图谱 RAG 检索，到最新的 MCP 智能体编排与提示词注入防御等企业级全套方案。课程专为 Java 后端开发者设计，聚焦手写项目与工业级运维，旨在帮助学员快速具备设计、部署和监控高安全、可扩展企业级 AI 架构的能力。
Published 7/2026
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 6h 58m | Size: 4.66 GB
Complete tutorial for developing AI enabled java application
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.
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
Artificial Intelligence is transforming software development, andSpring 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.
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