Spring AI + RAG：使用您的数据构建生产级AI

《Spring AI + RAG: Build Production-Grade AI with Your Data》是一门专注于后端系统设计的课程，旨在教授如何利用Spring AI框架构建可用于生产环境的检索增强生成（RAG）系统。课程摒弃了演示级的简单实现，转而采用后端工程原则，指导学员设计涵盖数据摄入、分块、检索和提示管理的完整、可维护的管道。通过基于Spring Boot、PostgreSQL和向量数据库的实战项目，学员将学习如何为PDF、Wiki和数据库内容构建可重复的摄入流程，实施影响检索质量的分块策略，创建元数据感知的检索管道，并通过明确的提示编排控制LLM行为，最终实现知识的安全增删改查。本课程面向具备Java和Spring Boot基础的后端工程师，帮助其建立构建正确、可靠且长期可演进AI系统所需的工程思维与实践能力。

制作：Infiproton Tech，Harish BN
MP4格式 | 视频：h264，1920×1080 | 音频：AAC，44.1 kHz，2声道
级别：所有级别 |语言：英语 | 时长：48节课（3小时50分钟） | 文件大小：3 GB

Spring AI + RAG: Build Production-Grade AI with Your Data

Spring AI RAG system design covering ingestion, chunking, retrieval, and prompt reliability.

What you’ll learn
✓ Design end-to-end RAG systems using Spring AI, following backend system design principles rather than demo-style implementations.
✓ Build repeatable ingestion pipelines for PDFs, wiki documents, and database content with clear structure and metadata.
✓ Implement effective chunking and embedding pipelines that directly impact retrieval quality and correctness.
✓ Design metadata-aware retrieval pipelines and integrate them cleanly into backend chat flows.
✓ Control LLM behavior using explicit prompt orchestration, grounding rules, and source-aware answers.
✓ Manage the full knowledge lifecycle by safely adding, updating, and deleting data without corrupting retrieval results.

Requirements
● Basic experience with Java and Spring Boot (REST APIs, configuration, project structure).
● Comfortable working with databases and general backend application concepts.
● Familiarity with IDE-based development and running applications locally.
● No prior AI, RAG, or Spring AI experience required — all AI concepts are covered from scratch.

Description
Most RAG courses stop at loading a few documents and asking questions.

This course goes further.

Spring AI + RAG: Build Production-Grade AI with Your Data teaches you how to design, build, and operate a real Retrieval-Augmented Generation (RAG) system the way backend engineers build serious systems — with clear boundaries, explicit pipelines, and production-minded decisions.

This is not a prompt-engineering or chatbot tutorial.
It is a backend-first system design course focused on correctness, reliability, and long-term maintainability.

You will build a complete Internal Knowledge Assistant for a fictional company, using

• Spring Boot

• Spring AI

• PostgreSQL

• Redis / vector stores

The same codebase evolves throughout the course, exactly like a real backend system.

What Makes This Course Different

• RAG is treated as a system, not a prompt trick

• Ingestion, chunking, retrieval, and prompting are separate, testable pipelines

• Metadata is a first-class concern, not an afterthought

• Knowledge can be added, updated, and deleted safely

• Everything is implemented using Spring AI abstractions, not custom hacks

• No Python, no LangChain, no demo-only shortcuts

By the end, you will not just “use Spring AI” — you will understand how to own and evolve an AI system in production.

What You Will Learn

• How to design ingestion pipelines for PDFs, Markdown, and databases

• Why chunking strategies directly affect retrieval quality

• How embeddings and vector stores fit into backend architecture

• How to build metadata-aware retrieval pipelines

• How to control LLM behavior with explicit prompt orchestration

• How to manage knowledge lifecycle: add, update, delete

• How to build RAG systems that remain correct as data changes

Course Modules Overview

This course is organized as a progressive backend system build, where each module introduces exactly one new system concern.

• Module 1 — Setup & Spring AI Baseline
Spring Boot + Spring AI setup and a minimal chat endpoint to establish the foundation.

• Module 2 — RAG Readiness
Use-case framing, data sources, and infrastructure setup (PostgreSQL, Redis).

• Module 3 — Ingestion Pipelines
Designing repeatable ingestion for PDFs, wiki content, and database records.

• Module 4 — Chunking Strategies
Source-specific chunking approaches and a unified chunking pipeline.

• Module 5 — Embeddings & Vector Storage
Generating embeddings and persisting them with metadata in a vector store.

• Module 6 — Retrieval Pipelines
Metadata-aware similarity search and clean retrieval integration into chat.

• Module 7 — Prompt Orchestration & Reliability
Grounded prompts, explicit behavior control, and citation-based, source-attributed answers.

• Module 8 — Knowledge Lifecycle
Safe add, update, and delete workflows to keep the system correct over time.

Who This Course Is For

• Java and Spring Boot developers

• Backend engineers integrating AI into real systems

• Developers who already understand REST APIs, databases, and Spring fundamentals

• Engineers who want to move beyond demo-level RAG implementations

Who This Course Is NOT For

• Absolute beginners to Java or Spring

• No-code or prompt-only AI learners

• Frontend-focused developers looking for chatbot-only examples

• Learners expecting quick “load a PDF and chat” style examples

Outcome

After completing this course, you will be able to

• Design RAG systems confidently

• Build production-grade AI pipelines using Spring AI

• Reason about correctness, reliability, and system boundaries

• Apply the same architecture to other real-world use-cases

This course gives you the mental model and engineering discipline needed to build AI systems that last.