OpenAI’s new gpt-image-2 model improves dense Chinese text rendering, follows detailed prompts more reliably, and offers precise edit capabilities, making it suitable for real‑world business graphics such as posters, banners, and dashboards, and the article shows how to integrate it with Spring AI in Java.
While many Java teams chase flashy AI demos and agents, the real 2026 focus has shifted to engineering concerns—ensuring model outputs reliably map to Java objects, integrating Retrieval‑Augmented Generation into robust data pipelines, and adding observability so AI services can be monitored and debugged like traditional back‑end components.
The article explains that in 2026 Java AI development shifts from simple model SDKs and prompt engineering to engineered, production‑ready solutions, highlighting Spring AI’s new stable releases with dynamic structured output and LangChain4j’s mature integration options, and compares their suitability for Spring‑centric versus framework‑agnostic projects.
This step‑by‑step guide shows Java developers how to set up Spring AI, configure various model providers, build basic and streaming chat APIs, enable multi‑turn memory, implement RAG with vector stores, add tool‑calling and multimodal capabilities, integrate MCP, and create sophisticated agents, while comparing ChatModel and ChatClient and outlining strengths, weaknesses, and ideal use cases.
The article argues that AI will automate low‑context, repetitive Java coding, but the true value of Java developers lies in complex business judgment, system stability, and delivery responsibility, which AI cannot replace.
The article explains how recent releases like Java 26 and Spring AI 2.0 have turned Java‑AI from a hobbyist demo into a mature, production‑ready engineering stack, outlining the practical steps Java teams should follow to integrate AI into existing systems.
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.
Spring AI 1.1 GA transforms Java AI development from a patchwork of disparate SDKs and tools into a cohesive, engineering‑grade framework, offering unified model access, MCP support, workflow reasoning and better maintainability for enterprise applications.
This tutorial shows Java engineers how to set up a Spring Boot 4 project, configure Spring AI for the MiniMax large‑language model, call it via simple and streaming endpoints, use prompt templates with dynamic parameters, add metadata and advisors, and switch between different LLM providers with minimal code changes.
This article explores six practical AI Agent design patterns—ReAct, Tool Use, Reflection, Planning, Multi‑Agent, and Human‑in‑the‑Loop—detailing their principles, Java Spring AI implementations, advantages, drawbacks, and suitable scenarios, and provides guidance on selecting and combining them for robust AI applications.
With Spring AI 1.1, LangChain4j, and MCP Java SDK now offering mature, framework‑level AI capabilities, Java backend teams can move beyond ad‑hoc model calls to fully engineered AI integration—RAG, tool calling, and agents—making Java a viable, production‑ready AI stack for enterprises by 2026.
The article explains how Oracle, Spring, and JetBrains are collectively pushing Java toward an agent‑centric ecosystem, shifting the developer’s role from writing code to orchestrating AI agents, and outlines the specific capabilities, engineering practices, and risks Java engineers must adopt to stay competitive in the coming years.
This article compares Spring AI Alibaba and AgentScope-Java, explaining their distinct design philosophies, workflow versus agentic paradigms, core capabilities, architecture layers, code examples, and practical selection guidance, while also discussing emerging fusion trends that combine the strengths of both frameworks.
The article argues that the real turning point for Java programmers is not the language's relevance but the shift from merely writing code to mastering AI agent orchestration, highlighting emerging Java 26 AI support, Spring AI advances, IDE integration, and the need for new engineering skills.
The article argues that the key transformation in the Java ecosystem by 2026 is not AI replacing developers, but the rise of AI agents that rewrite how Java engineers design prompts, manage tools, and orchestrate enterprise workflows, demanding new skills beyond traditional coding.
This article explains how to turn a simple Text‑to‑SQL demo into a production‑grade service by covering the underlying principles, layered architecture, risk‑control mechanisms, multi‑tenant security, high‑concurrency strategies, caching, observability, and deployment practices using Spring AI Alibaba.
After a month of using OpenClaw, the author found its lack of persistent memory frustrating and discovered Hermes Agent, which automatically captures skills, stores context in SQLite with full‑text search, and integrates effortlessly with OpenAI‑compatible APIs, Spring AI, and LangChain4j.
This article compares Alibaba's Spring AI Alibaba and the AgentScope-Java framework, examining their design philosophies, workflow versus agentic paradigms, core capabilities, architecture, code styles, and suitable use cases, and offers guidance on selecting the right solution or combining both for optimal Java AI development.
This article explains how to design and implement a scalable multi‑agent architecture for AI‑driven story creation using Spring AI Alibaba, covering core design principles, engineering optimizations, orchestration, high‑concurrency handling, observability, and deployment best practices.
This article introduces Spring AI, explains its purpose as a standardized Java framework for AI integration, highlights the new Model Context Protocol and expanded model support in version 1.1 GA, and provides a step‑by‑step tutorial—including dependencies, configuration, and code examples—to help developers quickly build AI‑powered applications.
The article explains how the ReAct reasoning‑acting loop transforms a generic AI troubleshooting agent into a precise fault‑diagnosis tool, boosting accuracy from about 30% to over 90% and even out‑performing human engineers, while providing a step‑by‑step Spring AI implementation.
This article explains why enterprise‑grade AI development in Java goes far beyond calling a model, introduces the five major Java AI frameworks—Spring AI, LangChain4j, Spring AI Alibaba, AgentScope‑Java, and Semantic Kernel—compares their core features, provides concrete code samples, offers a selection matrix for different scenarios, and outlines future trends in AI system orchestration.
This comprehensive guide explains how to design, implement, and operate a production‑ready Retrieval‑Augmented Generation (RAG) platform using Spring AI and PostgreSQL PGVector, covering architecture, indexing, hybrid retrieval, prompt engineering, scaling, security, observability, deployment, and common pitfalls for enterprise knowledge‑base applications.
This article provides a deep, production‑grade comparison of LangChain4j and Spring AI, examining their architectural philosophies, engineering governance, high‑concurrency design, code examples, and real‑world scenarios to help Java teams decide which framework best fits their AI system boundaries, team capabilities, and long‑term evolution goals.
This article analyzes the emerging Java AI ecosystem, comparing Spring AI, LangChain4j, Spring AI Alibaba, AgentScope‑Java, and Microsoft Semantic Kernel, and provides guidance on selecting the right framework based on features such as RAG, agent support, observability, security sandbox, and cloud integration.
This article provides an in‑depth analysis of Spring AI Alibaba, comparing it with Spring AI, detailing its four‑layer architecture, GraphCore workflow engine, AgentFramework, enterprise‑grade MCP integration, code examples, pros and cons, suitable scenarios, and future roadmap for Java developers building AI applications.
This article analyzes the evolution of enterprise AI from single‑model chat to scalable multi‑agent workflows, explains seven core multi‑agent patterns—including Pipeline, Routing, Skills, Subagents, Supervisor, Handoffs, and Custom Workflow—provides detailed implementation guidance with Java code, and shows how Spring AI Alibaba now natively supports AgentScope orchestration for robust, observable AI applications.
The article explains how Spring AI's AskUserQuestionTool brings an interview‑style questioning model to Java, letting AI clarify ambiguous requirements before generating code, and provides step‑by‑step implementation details, code samples, and a walkthrough of the underlying tool architecture.
This article analyzes the evolution of AI agents, compares multi‑agent and single‑agent frameworks, introduces the concept of an AI Tool Platform, and presents a five‑layer enterprise AI architecture with a step‑by‑step Java implementation roadmap for real‑world automation.
Spring AI eliminates SDK fragmentation, side‑car Python services, and operational complexity by providing a unified AI abstraction layer for Java, enabling seamless model switching, RAG, tool calling, and offering concrete performance data and best‑practice guidance for production use.
This guide walks you through setting up Spring AI, configuring OpenAI credentials, using ChatClient for basic and advanced interactions, enabling function calling, managing conversation memory, and building real‑world projects such as a smart customer‑service bot, document summarizer, and code assistant, while also comparing Spring AI with LangChain4j and addressing common setup issues.
The article compares LangChain4j and Spring AI across design philosophy, core features, ecosystem integration, community maturity, and learning curve, providing concrete code examples, a feature‑richness matrix, and practical selection guidelines to help Java developers choose the most suitable AI framework for their needs.
This article explains Spring AI's architecture, including strategy, template, advisor, function‑calling, and RAG patterns, and provides a step‑by‑step tutorial for building a zero‑cost, privacy‑preserving AI assistant with the local Ollama model in a Spring Boot project.
This article explains why the previous custom WebMvcStreamableServerTransportProvider approach is fragile, introduces Spring AI's standard TransportContextExtractor, and provides step‑by‑step code and screenshots showing how to configure the extractor, capture ServerRequest, and read HTTP headers, URL, query parameters, and other request data in an MCP Server.
The article reviews ten essential AI tools for Java developers in 2026, explaining how they automate boilerplate code, improve code quality, detect security issues, and integrate with Spring Boot, while emphasizing that AI acts as an tireless junior engineer that boosts productivity without replacing core design work.
This guide explains what Agent Skills are, shows their markdown‑based structure, and provides a step‑by‑step Spring Boot 3 example—including Maven dependencies, configuration, skill definition, controller registration, and troubleshooting—to enable modular AI capabilities in backend applications.
This guide walks you through upgrading a Spring Boot project from 3.x to 4.0, covering Gradle version bump, dependency version catalog updates, starter module changes, Jackson 3 migration, Redisson API adjustments, and verification steps, while highlighting common pitfalls and official upgrade paths.
This article provides a detailed, source‑code‑level analysis of Spring AI’s Model Context Protocol (MCP) implementation, covering its layered architecture, core client‑server interaction steps, key components, runtime debugging with SSE, and practical code examples for building and testing MCP services in Java.
This article walks through creating a Retrieval‑Augmented Generation (RAG) powered Q&A service in Java using Spring AI, covering the rationale for choosing Spring AI over LangChain, required environment, Maven setup, configuration, document ingestion, Advisor‑based query handling, testing, and practical limitations of RAG implementations.
This article provides a comprehensive analysis of Spring AI’s Model Context Protocol (MCP), covering its layered client‑server architecture, core interaction sequences, key source‑code components, and step‑by‑step debugging of the SSE‑based initialization flow, enabling developers to integrate AI capabilities into Java applications with confidence.
This article walks through creating a Spring AI 1.1 MCP Server using the new annotation‑based programming model, covering project setup, Maven dependencies, YAML configuration, replacing @Tool with @McpTool, exploring the new context parameters, and demonstrating dynamic schema support with concrete code examples and test results.
This article answers the most frequent questions about AgentScope Java, comparing its design and capabilities with Spring AI Alibaba, outlining future integration plans, core features, model support, and providing practical resources for developers.
This article explains the background, principles, and step‑by‑step implementation of Retrieval‑Augmented Generation (RAG) using Spring AI, covering embedding models, vector databases, chunking strategies, indexing algorithms, similarity metrics, re‑ranking, prompt templates, and a complete Java code example.
Spring AI Alibaba Admin addresses three major engineering obstacles—inefficient prompt debugging, unreliable AI quality assessment, and opaque production operations—by providing a full AI agent lifecycle platform with versioned prompt management, dataset versioning, flexible evaluator configuration, experiment automation, and end‑to‑end observability.
Spring Boot 4.0 launches alongside Spring Framework 7, Spring Data 2025.1 and Spring AI 1.1, delivering a sweeping upgrade that moves the stack to Jakarta EE 11, JDK 25, JSpecify null‑safety, build‑time optimizations, native OpenTelemetry support, and a dual‑track AI strategy.
A third‑year student recounts four distinct campus interviews—from a state‑owned big‑data firm to an AI startup—detailing test formats, technical questions, and personal growth, and concludes that most graduates ultimately join the countless small‑mid‑size companies that keep society running.
Spring Boot 4 arrives alongside Spring Framework 7, upgrading to Jakarta EE 11, JDK 25, JSpecify null‑safety, build‑time optimizations, a declarative HTTP client, full Jackson 3 support, native API versioning, built‑in resilience, OpenTelemetry integration, and a dual‑track Spring AI strategy.
Spring AI 1.1.0 arrives with a major overhaul, adding out‑of‑the‑box Model Context Protocol support, five‑mode prompt caching that can cut LLM costs by up to 90%, reasoning APIs, recursive advisors, a broadened model ecosystem, enhanced vector‑store and chat‑memory options, and richer observability integrations.
This article explains how Spring AI handles prompts, covering the Prompt and Message APIs, role-based message types, PromptTemplate creation, custom template renderers, tokenization basics, and practical code examples to help developers craft effective AI prompts.
Spring AI Alibaba Admin is an Alibaba‑built platform that extends Spring AI to address three major engineering hurdles—inefficient prompt debugging, uncertain AI quality, and opaque production operations—by offering comprehensive prompt, dataset, evaluator, experiment, observability, and model‑configuration capabilities for enterprise AI agents.
This article reviews Spring AI's evolution, architecture, and key features—including unified APIs, prompt engineering, streaming, RAG, multi‑modal support, and agent workflows—showing how Java back‑ends can efficiently expose AI capabilities without handling low‑level model logic.
Spring AI Agents brings a unified Java SDK that lets developers integrate, orchestrate, and run multiple AI coding agents—such as Claude, Gemini, and OpenAI Codex—directly within GitHub workflows, transforming development from single‑assistant Copilot models to collaborative AI team‑based architectures.
The article introduces Spring AI Alibaba DeepResearch, a Java‑based system that automates end‑to‑end research by integrating multi‑agent workflows, configurable RAG pipelines, data ingestion, observability, and dynamic report generation, and provides detailed deployment and extension guidance.
This article explores how Spring AI Alibaba enables the development of autonomous AI agents that run on schedules, interact with humans when needed, and handle tasks such as periodic business automation, batch processing, emergency response, and long‑cycle data analysis, illustrated with Java code examples.
Spring AI Alibaba extends Spring AI with multi‑agent and enterprise features, but faces three engineering hurdles—inefficient prompt debugging, unguaranteed AI quality, and opaque operations—so Alibaba released Spring AI Alibaba Admin, offering prompt templating, dataset versioning, evaluator configuration, experiment management, and deep observability to streamline AI agent development and deployment.
This article outlines the industry shift toward large‑scale AI Agent deployment, identifies key engineering challenges such as prompt management, quality assessment, and observability, and presents Spring AI Alibaba Admin—a cloud‑native platform that offers prompt, dataset, evaluator, and tracing capabilities, complete with setup instructions and future roadmap.
This article explains the challenges of moving AI agents from demo to production, compares native framework and non‑intrusive observability solutions, and provides a step‑by‑step guide to integrate Spring AI Alibaba with LoongSuite's Java agent for OpenTelemetry‑based monitoring.
This tutorial demonstrates how to integrate multiple large language models from different providers into a Spring Boot application using Spring AI, configure primary, secondary, and tertiary models, and implement a fallback mechanism with Spring Retry to ensure high availability of the chatbot.
This article demonstrates how to resolve AI hallucination in train‑ticket queries by cleaning data, building a Spring AI‑based Model Context Protocol (MCP) server with HTTP SSE in Java, exposing it as a tool, and seamlessly integrating the server into a FastGPT workflow to enable natural‑language train‑ticket assistance.
This article defines the Model Context Protocol (MCP), explains why it is needed to unify LLM and external tool interactions, details its client‑host‑server architecture, compares its communication modes, outlines Java/Spring AI support, and discusses current adoption trends and open challenges.
The article examines how rapid advances in generative AI have transformed enterprise AI Agent development, comparing evolving frameworks like LangChain, Semantic Kernel, and Spring AI with emerging low‑code platforms such as Dify and Copilot Studio, and outlines architectural challenges, integration strategies, and best‑practice design principles for Java‑centric organizations.
This article introduces Spring AI, explains its relationship to Spring Boot, outlines support for major AI model providers and capabilities, and provides step‑by‑step code examples for a chatbot, conversational memory, and prompt engineering, while highlighting version pitfalls and future extensions.
This article explains the Model Context Protocol (MCP) introduced by Anthropic, compares it with Function Call, details its architecture and communication flow, and provides a step‑by‑step Spring AI guide—including server and client development, configuration, code examples, and testing—so developers can integrate AI models with back‑end services efficiently.
This article walks through Spring AI’s three core classes—Message, Prompt, and ChatModel—explaining their roles, showing concrete code examples for constructing messages, building prompts, and invoking a large language model via a REST controller, and provides a complete demo repository.
This article explores how programming paradigms have shifted from traditional languages to AI‑driven development, detailing AI Agent concepts, workflow versus agentic modes, single versus multi‑agent strategies, prompt versus context engineering, the reference architecture with Spring AI Alibaba, Nacos, Higress and RocketMQ, and the observability solutions built on OpenTelemetry and LoongSuite.
Spring AI 1.0.1 has been officially released, introducing over 150 changes that improve stability, add new AI model provider capabilities, advanced features like vector store enhancements and MCP streaming, and outline upcoming roadmap priorities for developers and enterprises.
The article explains how the Java MCP SDK 0.11 adds Streamable HTTP support, compares it with the traditional HTTP+SSE approach, and walks through adding Maven dependencies, configuring Spring AI, creating custom auto‑configuration classes, implementing a Weibo trending tool, and deploying a fully functional Streamable HTTP MCP server.
This guide walks through creating a Spring Boot 3 application that loads web pages into a vector database using Spring AI, configures AI models and embeddings, and exposes REST endpoints for storing documents and answering queries with retrieval‑augmented generation.
This article explains how the open‑source JManus framework enables Java developers to create, configure, and deploy multi‑agent AI applications on Alibaba Cloud's Serverless SAE and Function Compute platforms, highlighting its OpenManus compatibility, MCP protocol support, PLAN‑ACT mode, high‑availability architecture, performance advantages over low‑code solutions, and step‑by‑step deployment instructions.
This article walks through the Spring AI MCP Client source code, showing how to set up a Java project, configure the MCP client, implement a demo controller, and dissect the auto‑configuration classes that assemble transport, client, and tool‑callback components during application startup.
This article explains the MCP framework that standardizes AI tool invocation using JSON‑RPC, outlines its client‑server architecture, details communication methods such as STDIO, SSE and streamable HTTP, and provides a Spring AI demo showing tool registration, discovery, and execution.
This article walks you through what Spring AI Alibaba 1.0 offers, highlights its major updates such as the Graph multi‑agent framework and ecosystem integrations, and provides a step‑by‑step upgrade path with Maven dependency changes, code fixes, and configuration adjustments for Java developers.
This article explains the MCP framework’s architecture, execution flow, JSON‑RPC communication, and lifecycle, showing how it standardizes AI function calling and tool integration using Spring AI, with code examples and comparisons of communication methods.
This article walks through the fundamentals of large language models, their stateless and structured-output nature, explains how Spring‑AI provides a Java‑friendly API for model integration, covers RAG architecture, the MCP protocol, and demonstrates end‑to‑end code examples for building intelligent agents.
This tutorial walks through creating a natural‑language‑to‑SQL chatbot using Spring AI, configuring a MySQL school database with Flyway, defining system prompts for a DeepSeek LLM, implementing service beans and a REST API, and interacting with the bot via curl commands.
Learn how to leverage Spring AI's prompt template feature to create flexible, variable-driven queries, and implement backend services using DeepSeek and QWEN models for dynamic travel recommendations, complete with code examples for interfaces, service implementations, and controller routing.
This tutorial walks through creating a Sequential Thinking MCP Server in Java using Spring AI 1.0, covering configuration, core classes, tool registration, HTTP SSE setup, performance trade‑offs, and links to the official guide and GitHub repository.
This step‑by‑step tutorial shows how to install Ollama, pull the DeepSeek‑R1 model, create a Spring Boot project with the Spring AI Ollama starter, code a ChatController, and test a local AI "Hello World" integration, illustrating AI‑enhanced backend development.
This article introduces essential AI concepts—including models, prompts and prompt templates, embeddings, tokens, structured output, and Retrieval‑Augmented Generation—explaining their meanings and relevance for effectively using Spring AI in real‑world applications.
This article walks through how Java developers can connect to Python‑based AI services using the Model Context Protocol (MCP), compares STDIO and SSE transports, explains why Spring AI’s MCP support is limited, and shows a complete implementation with the raw MCP Java SDK and OpenAI client to invoke tools like Blender from Java code.
The article announces the official release of Spring AI 1.0, explains the meaning of GA, provides the Maven dependency configuration, describes how Spring AI simplifies AI integration for Java applications, and outlines its key features and supported model types.
Learn how to implement Spring AI’s Model Context Protocol (MCP) in Spring Boot 3.4.2, enabling dynamic runtime tool updates, configuring MCP server and client, building sample tools with code snippets, and demonstrating add/remove operations and testing via Cline and REST endpoints.
This article explains fundamental AI concepts—including models, prompts, prompt templates, embeddings, tokens, structured output, data integration, RAG, and tool calling—and shows how Spring AI simplifies their use for Java developers building intelligent applications.
This guide walks Java developers through integrating Spring AI with large‑model services such as DeepSeek, covering setup, API key configuration, code examples for synchronous and streaming calls, reactive implementation, monitoring with Actuator, and compatibility with OpenAI‑style APIs.
The article announces the Spring AI 1.0 release, provides the Maven BOM dependency, compares Spring AI with LangChain4j for Java AI development, and outlines upcoming integration, performance, and observability improvements for both frameworks amid rapid large‑model advancements.
This article explains why observability is essential for Spring AI applications, outlines common cost‑control and performance challenges, and provides a step‑by‑step guide—including Maven setup, client configuration, service implementation, metric exposure, Zipkin tracing, and architecture insights—to create a fully observable, enterprise‑grade AI translation service.
Spring AI 1.0 GA introduces a comprehensive suite of AI capabilities for Java developers, including a ChatClient supporting 20 models, vector‑store integrations, RAG pipelines, advanced chat memory, @Tool function calling, model evaluation, observability, Model Context Protocol, and autonomous agents, with examples for major cloud providers.
Spring AI 1.0, the first production‑grade Java AI framework, introduces ready‑to‑use APIs, seamless model integration, enterprise‑level RAG engine, smart tool calling, and three development modes, empowering developers to rapidly build, customize, and fully control AI applications with major model providers like OpenAI, Anthropic, DeepSeek.
This article explains the challenges of integrating diverse tools without MCP, then demonstrates step‑by‑step how to configure Spring‑AI and the native MCP SDK to call LLMs, register tools, handle SSE and stdio services, and troubleshoot common issues, providing code snippets and best‑practice recommendations.
This article walks you through using Spring AI's MCP client starter in a Spring Boot 3 project to connect to Baidu Map's MCP server, covering dependencies, YAML and JSON configurations, communication modes, custom server setup, and sample code for invoking the service.
This article introduces Spring AI, explains its core features for integrating various AI models, and walks through creating a Spring Boot chat application that connects to the DeepSeek model using both synchronous and streaming endpoints.
JManus is an open‑source, Java‑centric AI agent platform built on Spring AI Alibaba that lets developers quickly create, configure, and run multi‑agent applications with support for major large language models, a web UI, and advanced PLAN‑ACT reasoning capabilities.
This tutorial demonstrates how to create a complete meeting‑management CRUD application using Spring Boot 3, Spring AI Tools, and a custom ChatClient, covering environment setup, entity definitions, repository, service layer, tool integration, and REST endpoints with code examples and screenshots.
The article explains Anthropic’s Model Context Protocol, outlines its architecture, and provides a step‑by‑step guide to creating a Spring AI‑based MCP server in Java—including adding the starter, defining @Tool‑annotated services, packaging the jar, configuring the Cline plugin, and demonstrating advanced tools such as Elasticsearch queries.
This article introduces Spring AI's multimodal capabilities, explains the Message API for handling text, image, audio, and video inputs, and provides step‑by‑step Spring Boot examples for image analysis, ID card extraction, and structured JSON output of car‑color counts.
This tutorial walks developers through setting up a low‑cost, containerized AI service on Windows by installing Docker, deploying Redis and Ollama containers, pulling the DeepSeek‑R1 model, and integrating everything with Spring AI to enable continuous conversation support.
This article introduces Spring AI, a Java‑based AI framework inspired by LangChain, explains its core capabilities such as model management, inference, extension and Spring ecosystem integration, and provides step‑by‑step installation, configuration, sample code, and guidance for creating MCP services.
This article explains the Model Context Protocol (MCP), why it simplifies AI tool integration, and provides a step‑by‑step guide to create a Spring AI 1.0.0‑M7 Maven project, configure the MCP server with HTTP SSE, implement weather and Maoyan movie services, run the server, and test it with the MCP Inspector and Cherry Studio client.
An in‑depth guide shows how to configure Spring AI for various LLM providers, tune model parameters such as temperature and max tokens, and apply a range of prompt‑engineering patterns—including zero‑shot, few‑shot, chain‑of‑thought, self‑consistency, role‑based and automatic prompting—using concise Java code examples.
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.
