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.
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 Alibaba OpenManus, a Java‑based multi‑agent framework that showcases complex task execution through browser automation, travel itinerary generation, and document translation, while detailing its architecture, current limitations, and future development roadmap.
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.
This article explains the Retrieval‑Augmented Generation (RAG) paradigm, walks through its four core steps, and provides a detailed Spring AI implementation—including configuration, vector storage, REST controller, multi‑query expansion, query rewriting, document joining, and error handling—plus best‑practice recommendations for production deployments.
This article demonstrates how to use Spring AI with a large language model to automatically generate and execute SELECT SQL statements in a Spring Boot 3 application, covering dependency setup, configuration files, prompt templates, controller implementation, and testing with example scripts.
AI Services, inspired by Spring Data JPA and Retrofit, offers a declarative Java API that abstracts LLM interactions, supporting input formatting, output parsing, chat memory, function calling, and RAG, with detailed examples using LangChain4j, custom Spring AI annotations, AOP aspects, and controller integration.
The QwQ-32B model, released by Alibaba Cloud, delivers DeepSeek‑R1‑level results with only 32 billion parameters, offers integrated agent capabilities, is open‑source under Apache 2.0, and can be quickly deployed locally via Ollama or integrated into Java applications using Spring AI.
This article examines three leading Java AI frameworks—LangChain4j, Spring AI, and Agent-Flex—by comparing their architectures, core capabilities, and ideal use‑cases, helping developers choose the most suitable solution for enterprise, domestic, or rapid‑prototype projects.
This tutorial walks you through integrating DeepSeek via Spring AI into a Spring Boot project, covering Spring AI basics, obtaining an API key, adding dependencies and configuration, implementing controller endpoints, testing with Postman, and accessing the full source code.
This tutorial explains what Spring AI is, walks through creating a Spring Boot project with Maven, adding the necessary dependencies, writing a simple controller that forwards user messages to a local DeepSeek model, configuring the application, and testing the AI‑powered endpoint.
This tutorial walks you through creating a Java‑based AI chat agent using Spring AI Alibaba, integrating Alibaba Cloud's large language model, adding function‑calling for weather queries, and enabling full observability with ARMS in a cloud‑native deployment.
This guide explains how to locally deploy the DeepSeek large‑language model using Ollama on Windows, macOS, and Linux, configure model storage and CORS, build personal and enterprise RAG knowledge bases with AnythingLLM and Open WebUI, and integrate the model into a Spring AI application via Docker and Docker‑Compose.
This article explains how to integrate DeepSeek's large language models into a Spring AI application, covering model selection, API key configuration, URL setup, dependency inclusion, and providing complete Java code examples for both synchronous and streaming chat interactions.
This article explains how to obtain a DeepSeek API key, configure Spring AI with the appropriate base URL and model, and provides Java code examples for both synchronous and streaming chat interactions using the DeepSeek large‑language model.
This article walks through installing Ollama, downloading the DeepSeek‑r1:1.5b model, configuring Spring AI in a Spring Boot 3 project, and exposing the model via a REST endpoint with Server‑Sent Events, providing complete code snippets and step‑by‑step screenshots for a functional AI integration.
This article explains how to integrate DeepSeek's large language models—both the chat‑oriented deepseek‑chat and the reasoning‑focused deepseek‑reasoner—into a Spring AI application, covering API key setup, base‑URL configuration, model selection, and providing full code examples for dependency, configuration, and a simple chat controller.
This guide walks Java developers through integrating DeepSeek’s large‑language model into Spring AI, covering environment setup, Maven dependencies, configuration, and sample controller code for both standard and streaming chat responses, including real‑time streaming.
This article explains how Spring AI Alibaba enables AI agents to leverage Retrieval‑Augmented Generation and Tool Calling by providing over twenty ready‑made RAG data source connectors and more than twenty function‑calling interfaces, along with practical code examples for integrating document readers and weather services.
Learn how to deploy the open‑source DeepSeek‑R1 model using Ollama on Linux or macOS, configure various model sizes, and integrate it into a Spring Boot application with Spring AI to build an API‑driven translation service, complete with code examples and testing.
This guide explains how the open‑source Model Context Protocol (MCP) enables natural‑language control of Docker containers, walks through environment setup, Spring AI integration, client project configuration, and demonstrates a full execution log showing automated image pulling, port mapping, volume mounting, and container startup.
This article introduces DeepSeek R1, a breakthrough reasoning‑focused large model that visualizes its chain‑of‑thought process, matches OpenAI O1 performance, offers open‑source advantages, and provides step‑by‑step Spring AI integration guidance, including dependency setup, configuration, and code examples.
This tutorial explains how to integrate Spring AI's Model Context Protocol (MCP) with Chat2DB to enable secure, natural‑language queries against a PostgreSQL database, covering configuration, core Java code, execution flow, and sample SQL queries.
Spring AI now supports the Model Context Protocol (MCP), a universal adapter that lets AI applications seamlessly read databases, files, and APIs, offering a plug‑and‑play alternative to function calling, with simple Maven setup and code examples to get started quickly.
This guide shows how to use Spring AI Alibaba together with Nacos to securely store API keys, dynamically update Prompt templates and model parameters, and encrypt sensitive configurations without restarting Java AI applications.
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.
Learn step‑by‑step how to integrate Spring AI Alibaba into a Spring Boot backend, configure API keys, add necessary dependencies, create a CORS‑enabled chat controller, and then develop a matching React frontend that streams AI responses, enabling a complete Java‑to‑React AI chat application.
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.
Spring AI Alibaba is an open‑source Java framework that integrates Alibaba Cloud's large‑model services with Spring AI, offering high‑level abstractions for chat models, prompts, function calling, RAG, and conversation memory, and includes a complete ticket‑assistant example with code snippets.
Spring AI is a Java‑centric framework that unifies access to chat, text‑to‑image, embedding and retrieval‑augmented generation models—including OpenAI, Anthropic and Alibaba’s Tongyi Qianwen—through synchronous or asynchronous APIs, POJO mapping, function calling, vector‑store integration and fluent tooling for rapid AI agent development.
Spring AI has added full support for OpenAI's structured output feature, enabling developers to define JSON schemas for AI responses, with example code showing how to configure prompts, response formats, and BeanOutputConverter for seamless JSON-to-Java bean conversion, plus Spring Boot starter configuration.
This article explains how to quickly develop an AI‑native application using Spring AI, covering core features such as chat models, prompt templates, function calling, structured output, image generation, embedding, vector stores, and Retrieval‑Augmented Generation (RAG), and provides end‑to‑end Java code examples for building a simple AI‑driven service.
This guide walks you through using Spring AI and Spring Cloud Alibaba AI to quickly create a Java chat application powered by Alibaba's Tongyi large‑model service, covering setup, configuration, core code, and verification steps for a functional generative AI chatbot.
This article introduces Spring Cloud Alibaba AI, explains its relationship to Spring AI, and provides a step‑by‑step tutorial—including Maven setup, dependency configuration, code examples, and sample calls—to integrate Alibaba's Tongyi large‑model services for text QA, image generation, and speech synthesis in a Java Spring Boot application.
This guide explains what an AI‑native application is, compares AI‑native and AI‑based approaches, and walks through Spring AI’s core features—including chat models, prompt templates, function calling, structured output, image generation, embedding, and vector stores—showing step‑by‑step code examples and how to assemble a complete AI‑native app with RAG support.
This tutorial introduces Spring AI and Spring Cloud Alibaba AI, explains their core features, shows how to set up a Maven project with the required dependencies, and provides step‑by‑step code examples for invoking text, image, and audio generation models using Spring Boot.
Spring AI brings artificial‑intelligence capabilities to the Spring Boot ecosystem, offering model support, vector‑database integration, SQL‑like filtering, and easy Maven configuration, enabling Java developers to add generative AI, semantic search, and AI‑driven image generation to their backend applications.
This tutorial walks through setting up Spring AI 0.8.1 with Spring Boot 3.1.1, configuring Alibaba Tongyi model access, and implementing chat, streaming, image, and audio generation endpoints using Java code and vector database integrations.
This guide walks you through setting up Spring AI, configuring model providers and vector stores, initializing a Spring Boot project, adding OpenAI credentials, and running a complete RAG (Retrieval‑Augmented Generation) demo with code snippets and sample API calls.
This guide introduces Spring AI, shows how to add dependencies, configure OpenAI credentials, and demonstrates chat, image, and prompt APIs with code examples, then explores advanced function calling to build business‑level large language model integrations in Java.
This tutorial walks you through adding OpenAI's generative capabilities to a Spring Boot application with Spring AI, covering project setup, API key configuration, controller creation, simple and advanced prompt usage, and how to test the resulting AI-powered endpoints.
This tutorial walks you through creating a Spring Boot project, configuring OpenAI credentials, building a controller, and using Spring AI's Prompt and PromptTemplate classes to integrate generative AI capabilities into your backend application.
This article summarizes a video tutorial that introduces Spring AI, a new project enabling generative AI in Spring Boot applications, demonstrates adding dependencies, configuring OpenAI or Azure OpenAI API keys, using prompts, templates, output parsers, and showcases converting AI responses into domain objects.
Spring Boot 3.2 introduces Java 21 virtual thread support, SSL hot‑reload for embedded servers, GraalVM native image improvements, and a preview of Spring AI, marking a major evolution for Java backend development and performance optimization.
