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.
The article shares nine hands‑on tips for building Spring AI 2.0 agents—including using ChatClient as the entry point, delegating tool calls to ToolCallingAdvisor, defining tools with @Tool, crafting effective system prompts, leveraging Advisor chains, streaming responses early, managing conversation memory, limiting tool count, and adding observability—each illustrated with concrete code snippets.
This article explores Spring AI's core design patterns, advanced ChatClient usage, dynamic PromptTemplate creation, few‑shot prompting, structured output parsing, and declarative function calling with @Tool annotations, providing code examples, advisor mechanisms, and testing tips for Java developers.
This article presents a panoramic knowledge map of Spring AI 2.0, outlines core components, dependency configurations, key APIs for chat, RAG and tool integration, and provides a concise interview Q&A guide to help developers verify their mastery of the framework.
This article explains Spring AI Advisors as interceptors that can modify requests and responses in the LLM call chain, demonstrates building a custom logging Advisor with code examples, shows how ordering controls execution, and reviews the built‑in Advisors for memory, RAG, logging, and safety.
This article explains how Spring AI’s Media API lets a ChatClient handle both textual prompts and image inputs, shows code examples for attaching images, discusses required visual models, and outlines practical use cases such as OCR, chart analysis, and image moderation.
The article explains how to move beyond static prompts in Spring AI by using System and User message roles, PromptTemplate placeholders with .param(), defaultSystem configuration for reusable role definitions, and independent PromptTemplate usage, providing concrete code examples for each technique.
This article explains how Spring AI's ChatClient serves as a high‑level façade over ChatModel, compares the synchronous call() method with the streaming stream() method, demonstrates retrieving plain text, full responses, or structured entities, and shows how to override model parameters such as temperature on a per‑request basis.
This article introduces Spring AI 2.0, explains why Java developers should adopt it, and walks through setting up a Spring Boot 3.x project with JDK 17, adding the DeepSeek starter, configuring properties, writing a simple ChatController, and running a curl request to see the model’s reply.
This guide walks through integrating Ollama’s local LLM with Spring AI, explaining Prompt and PromptTemplate concepts, configuring application.yml, implementing a PromptService, adding controller endpoints, using external template files, and testing the setup with curl commands.
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.
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.
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.
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 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.
This guide walks through converting a traditional Spring Boot book‑management API into a Model Context Protocol (MCP) service that can be invoked via natural‑language prompts, covering dependency setup, proxy configuration, @Tool annotations, MCP server registration, chat client wiring, data initialization, and end‑to‑end testing.
This article demonstrates how to convert a traditional Spring Boot book‑management service into an AI‑enabled MCP server, covering dependency setup, entity definition, tool annotations, MCP configuration, chat client integration, data initialization, and testing, with complete code examples.
This article provides a concise guide on using SpringAI to connect Java applications with the domestic large‑language model DeepSeek, covering design philosophy, configuration, code examples for chat, streaming, structured output, security hardening, performance tuning, and production best practices.
