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 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.
