Spring AI: 构建Java AI应用、聊天机器人及RAG系统（2026）

《Spring AI: 构建Java AI应用、聊天机器人及RAG系统（2026）》是一门专为Java开发者设计的实践课程，旨在无需深厚数据科学背景的情况下，将Spring Boot开发技能转化为AI工程能力。课程从LLM基础（如令牌、提示和上下文窗口）入手，逐步指导学员使用Spring AI构建具备文本、图像、音频及多模态功能的AI应用，并集成OpenAI、Mistral等模型。学员将学习通过Ollama和Docker本地运行模型以降低成本，利用Spring WebFlux实现实时交互，并通过工具调用开发能执行业务逻辑的AI代理。课程重点涵盖RAG系统搭建，包括嵌入向量、语义搜索及PgVector集成，最终通过一个全栈HR助手聊天机器人项目综合应用所有技能，涵盖知识管理、对话记忆及React前端。学员无需AI/ML基础，仅需基础Java和Spring Boot知识即可学习如何开发可投入生产环境的企业级AI应用。

制作人：Rishabh Kumar NIgam
MP4格式 | 视频：h264，1280×720 | 音频：AAC，44.1 kHz，2声道
级别：初级 | 类型：在线学习 | 语言：英语 | 时长：59节课（6小时58分钟） | 文件大小：4.25 GB

Spring AI: Build Java AI Apps, Chatbots & RAG Systems (2026)。Transform your Java skills into real AI engineering with Spring AI—build local, cost-free AI apps, intelligent agents, The AI revolution is here, and enterprise systems are still powered by Java. Java developers need a modern, practical way to integrate LLMs without deep data science knowledge. This course is the direct answer, transforming you from a Spring Boot developer into a high-demand AI engineer.We cut through the noise and show you exactly how to build robust, scalable AI features using the familiar patterns of the Spring ecosystem.We move quickly from foundational concepts to hands-on, production-ready features.Foundations (Module 1): Master the core mechanics of LLMs—tokens, prompts, and context windows—which are the building blocks of every AI application.Core Integration (Module 2-3): Build your first Spring AI application from scratch. Go beyond text generation to integrate image generation , Text-to-Speech (TTS) , Speech-to-Text (STT) , and multimodal (vision/audio) capabilities. You’ll implement moderation pipelines using both OpenAI and the free Mistral model.The Power of Local AI (Module 4): Free yourself from cloud costs and latency. Learn how to install and use Ollama to run fast, local models like Gemma directly on your machine. We implement real-time streaming using Spring WebFlux and even integrate local Whisper Api via Docker.Intelligent Agents (Module 5): Build AI agents that take actions. Master Tool Calling (Function Calling) to let the LLM securely trigger your Spring Boot business logic, fetch real-time data (like weather) , and orchestrate complex workflows.RAG Mastery (Module 6-7): The most critical enterprise skill. We start by building a custom RAG pipeline from scratch using embeddings and cosine similarity. Then, we integrate fully with PgVector—the gold standard for RAG—to implement scalable semantic search, document ingestion (PDF chunking via Tika), and lifecycle management.The Capstone Project (Module 8): Bring it all together by building a Full-Stack HR Assistant Chatbot. This project features:Admin APIs for knowledge base management.Spring AI Chat Memory for personalized conversations.A full conversation management API.A complete, AI-generated React Frontend.By the end of this course, you will have the confidence and portfolio to build real, feature-rich, AI-powered applications that solve genuine business problems.

What you’ll learn
Master LLM fundamentals—tokens, prompts, context windows—to build a strong foundation for all Spring AI features.
Build production-ready AI applications using Spring AI, Java, and modern LLMs across text, image, audio, and multimodal capabilities.
Integrate multiple AI providers—OpenAI, Mistral, Stability AI, Ollama—without vendor lock-in or complex SDK implementations.
Run AI models locally with Ollama and Docker for free, offline, secure, and customizable AI development workflows.
Build real-time streaming chat experiences using Spring WebFlux and locally running LLMs for faster and more interactive responses.
Create intelligent AI agents with Spring AI Tool Calling to execute business logic, fetch external data, and automate workflows securely.
Implement complete RAG pipelines using embeddings, semantic search, and cosine similarity to generate accurate, context-rich answers.
Design scalable knowledge bases with document chunking, metadata enrichment, embedding generation, and PgVector vector storage.
Develop an end-to-end HR Assistant chatbot with admin APIs, chat memory, tool-calling agents, and a fully integrated React UI.
Apply enterprise-grade engineering patterns to architect, test, and deploy real AI features inside production Java applications.

Requirements
Basic Java knowledge (classes, methods, OOP fundamentals).
Light familiarity with Spring Boot — just enough to understand basic project structure; other things will be taught inside the course.
No AI/ML background required — all AI