This article explains how to replace Spring AI's default in‑memory ChatMemory with a JDBC‑backed implementation that stores conversation history in a MySQL table, covering component changes, schema design, auto‑configuration, and best‑practice configuration details.
This guide explains how Spring AI abstracts LLM conversation memory using a three‑layer architecture, demonstrates configuring MessageWindowChatMemory with a sliding‑window strategy, shows two ways to register the memory advisor, and provides complete Maven, YAML, and Java code examples with test screenshots.
This guide demonstrates how to integrate LangChain4j into a SpringBoot application to enable multi‑turn chat memory and per‑user session isolation using @MemoryId, configuring ChatMemoryProvider, updating the AI service interface, and testing with curl requests.
LangChain4j fills the long‑standing gap for Java developers by offering a Java‑native, enterprise‑grade LLM framework that abstracts model calls, prompts, memory, tools, RAG, streaming and structured output, enabling quick setup, clean AI Service definitions, and seamless integration into Spring Boot or Quarkus applications.
The article demonstrates how to give a Spring Boot AI chatbot persistent context using Spring AI's ChatMemory, showing the problem of stateless requests, explaining the ChatMemory architecture, providing a five‑line code example, comparing storage options (InMemory, JDBC, Redis), and sharing practical tips and pitfalls.
