Spring AI 2.0 lets Java developers integrate large‑language‑model capabilities with minimal code by adding a starter, configuring model parameters, injecting a ChatClient bean, and optionally enabling Redis‑backed chat memory for multi‑turn conversations, all demonstrated with runnable examples and screenshots.
This article walks through integrating Spring AI 2.0 into a Spring Boot project, configuring model access, implementing synchronous and streaming chat endpoints, and adding Redis‑backed conversation memory to enable true multi‑turn interactions with large language models.
This article explains how to integrate Claude Code’s auto‑memory mechanism into Spring AI by using AutoMemoryTools and AutoMemoryToolsAdvisor, compares three integration options, shows the request workflow, memory file formats, and provides concrete code snippets and consolidation strategies for persistent, typed long‑term memory.
This article provides a step‑by‑step guide to quickly set up LangChain4j, configure API keys, call LLM models, use structured outputs, function calling, and chat memory, and demonstrates three hands‑on projects—smart assistant, document summarizer, and code generator—plus FAQs and resources.
This article demonstrates how to enable conversation memory and history in Spring Boot 3 applications using Spring AI's Advisor API, covering environment setup, Maven dependencies, configuration, code examples for advisors, unique chat IDs, history retrieval, and optional database persistence.
This tutorial walks through creating an intelligent airline‑ticket customer‑service agent using Spring AI Alibaba, covering requirements, architecture, RAG integration, function calling, chat memory, core capabilities, code implementation with ChatClient, and a sample running result.
This article introduces the open‑source Spring AI Alibaba framework, explains its background, core features such as chat model abstraction, prompt templates, structured output, function calling, RAG and chat memory, and walks through a complete smart‑ticket‑assistant example with code snippets and deployment guidance.
