面向Java开发者的高级RAG课程，基于Spring Boot与Spring AI，深入Hybrid Search、Self-RAG、GraphRAG及企业级安全与监控，构建生产标准AI知识库。
原始标题：Advanced RAG with Spring AI: Enterprise AI Masterclass
本课程是一门面向 Java 开发者的高级企业级 RAG（检索增强生成）实战路线。课程依托 Spring Boot、Spring AI、PostgreSQL (pgvector) 和 Neo4j 核心技术栈，指导学员从零构建具备高并发、高准确率且符合生产标准的 AI 知识库系统。
你将不仅停留在传统的语义搜索层面，而是深度打通混合检索（Hybrid Search）、查询改写、多查询扩展和重排序（Re-ranking）等全套检索优化技术；随后，架构将逐步演进至前沿的自修复 RAG（Self-RAG/Corrective RAG）、自适应路由（Adaptive RAG）以及基于图数据库的 GraphRAG 证明性概念，彻底解决传统向量检索在处理复杂逻辑、跨文档关联及幻觉控制上的痛点。
在工程落地与企业级安全方面，课程覆盖了多模态数据（PDF、多数据库、Wiki等）的高效 ingestion 管道设计，并全面融入多租户隔离、PII 隐私脱敏、审计日志、响应缓存（Cache）等合规安全底座，最终配合 Prometheus、Actuator 与严苛的扎根度（Groundedness）评估指标，帮助你交付一套安全、可控、具备全链路监控的工业级 AI 智能客服/助理系统。
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.24 GB | Duration: 9h 13m
Build Hybrid RAG, Self-RAG, Adaptive RAG, Evaluation, GraphRAG, Retrieval Optimization and Enterprise AI Systems
What you’ll learn
Build production-ready enterprise RAG applications using Spring AI, Java, Spring Boot, PostgreSQL, and pgvector.
Implement Hybrid RAG, Query Rewriting, Multi-Query Retrieval, Re-ranking, and Metadata Filtering to improve retrieval quality.
Evaluate and optimize RAG systems using retrieval benchmarks, groundedness testing, custom metrics, Actuator, and Prometheus.
Build Self-RAG, Corrective RAG, Adaptive RAG, and a practical GraphRAG proof of concept using Neo4j and Spring AI.
Create enterprise ingestion pipelines for PDFs, databases, reports, images, and wiki content to build searchable AI knowledge bases.
Secure enterprise RAG systems with multi-tenant retrieval, audit logging, PII detection, freshness ranking, and response caching.
Choose the right RAG architecture for enterprise AI by comparing Standard RAG, Hybrid RAG, Self-RAG, Adaptive RAG, and GraphRAG.
Build a complete enterprise AI support assistant by integrating retrieval, prompt orchestration, evaluation, and LLM generation.
Requirements
Basic Java programming experience is required.
Familiarity with Spring Boot and REST APIs is recommended.
A basic understanding of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) is helpful.
A computer capable of running Java 17+, Docker, PostgreSQL, Neo4j, and IntelliJ IDEA (Community Edition is sufficient).
No prior experience with advanced RAG architectures such as Hybrid RAG, Self-RAG, Adaptive RAG, or GraphRAG is required—we’ll build them step by step throughout the course.
Description
Retrieval-Augmented Generation (RAG) has become the foundation of modern enterprise AI applications. While basic RAG systems can answer questions using your own data, production-grade enterprise systems require far more than semantic search and prompt engineering.In this course, you’ll move beyond traditional RAG implementations and learn how to build intelligent, production-ready retrieval systems using Spring AI, Java, and Spring Boot.Rather than focusing on isolated concepts, you’ll build a complete enterprise AI platform step by step using a realistic support assistant application. Throughout the course, you’ll implement advanced retrieval techniques, optimize search quality, evaluate RAG performance, and explore modern retrieval architectures used in enterprise AI systems.What you’ll buildBy the end of this course, you’ll have built an advanced enterprise RAG application featuring:Enterprise knowledge ingestion pipelinesMultiple document chunking strategiesVector embeddings and PostgreSQL with pgvectorSemantic search and Hybrid RetrievalRetrieval ranking and re-rankingQuery rewriting and Multi-Query RetrievalPrompt orchestration and grounded response generationRetrieval evaluation and benchmark frameworksSpring Boot Actuator and Prometheus monitoringMetadata filtering and multi-tenant retrievalAudit logging and PII-aware retrievalFreshness-aware ranking and response cachingSelf-RAGCorrective RAGAdaptive RAGA practical introduction to GraphRAG using Neo4jEnterprise-ready RAG architecture and best practicesWhat you’ll learnThroughout the course you’ll learn how to:Build enterprise-grade RAG systems using Spring AIDesign scalable ingestion and indexing pipelinesImprove retrieval quality using Hybrid Search and advanced ranking techniquesOptimize prompts for grounded LLM responsesEvaluate retrieval accuracy and answer qualityMeasure latency, benchmark retrieval performance, and monitor production systemsSecure enterprise AI applications with metadata filtering, tenant isolation, audit logging, and PII protectionImplement modern RAG architectures including Self-RAG, Corrective RAG, Adaptive RAG, and GraphRAGUnderstand when each retrieval strategy should be used in real-world enterprise applicationsWhy take this course?Many RAG tutorials stop after demonstrating vector search and a simple chatbot. Real enterprise AI systems are significantly more sophisticated.This course focuses on the techniques used to improve retrieval quality, increase answer reliability, monitor production systems, and build scalable enterprise AI applications. Every concept is demonstrated through practical coding using Spring AI, Java, and Spring Boot, with a strong emphasis on architecture, clean design, and production-oriented implementation.If you’ve already built a basic RAG application and want to learn what comes next, this course is designed for you.
Java developers who want to build advanced enterprise AI applications using Spring AI and Retrieval-Augmented Generation (RAG).,Spring Boot developers looking to move beyond basic RAG and learn production-ready enterprise retrieval techniques.,Backend engineers who want to implement Hybrid RAG, Self-RAG, Corrective RAG, Adaptive RAG, and GraphRAG in real applications.,Backend engineers who want to implement Hybrid RAG, Self-RAG, Corrective RAG, Adaptive RAG, and GraphRAG in real applications.,AI engineers and solution architects who want to understand modern RAG architectures, retrieval optimization, evaluation, and enterprise AI best practices.,Students who have completed a basic RAG course and are ready to learn advanced retrieval architectures and production techniques.,This course is not intended for absolute beginners to Java or Spring Boot. A basic understanding of Spring Boot development and RAG concepts is recommended.
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