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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.
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 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, 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 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.
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.
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.
This article explores the newly popular Quasar Alpha AI model, detailing its high-performance, multimodal capabilities, speculative origins, and provides a step‑by‑step guide to obtain an OpenRouter API key, configure a Spring Boot environment, and call the model using Java code.
This article explains the evolution from Spring AI FunctionCall to the newer ToolCalling, introduces the Model Context Protocol (MCP), and provides step‑by‑step code examples for building MCP servers and clients in Java, demonstrating how to integrate large language models with backend services.
This article explains how Spring AI's tool‑calling feature extends large language models by integrating custom Java tools for information retrieval and operations, and provides step‑by‑step Spring Boot 3 examples—including date‑time and weather tools—complete with configuration, code snippets, and execution results.
The article introduces Spring AI as an emerging tool for Java developers, explains its background, goals, and core components such as data processing, model training, deployment and monitoring, showcases application scenarios like NLP, image processing, recommendation systems and predictive analytics, and also includes promotional offers for AI resources and community groups.
This article introduces Spring AI, a Spring ecosystem module that simplifies building, training, and deploying AI applications for Java developers, covering its background, goals, core components such as data processing, model training, deployment, practical code examples, use cases, advantages, challenges, and future outlook.
This article introduces Spring AI, explains its background, goals, core components such as data processing, model training, deployment and monitoring, showcases practical use cases like NLP, image processing and recommendation systems, and discusses its advantages, challenges, and future outlook for Java developers.