5 hours agoIT & SoftwareBuild layered memory systems with Java, Spring AI, PostgreSQL, pgvector, and scalable backend architecture
Course Description
Most AI applications do not truly remember users.
They simply replay chat history.
In this course, you will learn how to design and implement real memory systems for AI agents using Java, Spring AI, PostgreSQL, and pgvector.
Using a practical AI Travel Planner project, you will build a layered memory architecture that enables AI assistants to remember users correctly across conversations.
This is a backend engineering focused course designed for developers who want to move beyond basic chat applications and build production-style AI systems.
What You’ll Build
Working memory using conversation history
Persona memory for persistent user facts
Episodic memory using conversation summaries
Semantic memory using learned preferences
Vector similarity search with pgvector
Async memory processing pipelines
Centralized prompt assembly using Spring AI Advisors
What You’ll Learn
Why chat history is not real AI memory
How modern AI memory systems are structured
How to design layered memory architectures
How embeddings and vector search work in practice
How to retrieve relevant memory dynamically
How to build scalable AI backend pipelines
How to personalize AI behavior across conversations
Technologies Used
Java
Spring Boot
Spring AI
PostgreSQL
pgvector
By the end of this course, you will have a complete understanding of how real AI memory systems are designed and implemented in modern backend applications.
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