Redis 8.0 introduces native vector search with Vector Set data type, HNSW indexing, quantization, and the Iris platform for agent memory and context retrieval, enabling semantic caching that cuts LLM costs by 70% and integrates with LangChain4j and Spring AI.
This guide categorizes six major Java AI frameworks into infrastructure and orchestration layers, detailing Spring AI 2.0's Advisor chain, LangChain4j's model breadth, Solon AI's Java 8 compatibility, Spring AI Alibaba's workflow engine, AgentScope's production-grade harness, and Embabel's deterministic GOAP planning to match frameworks to project constraints and scenarios.
The article analyzes supermemory, an open-source memory engine that gives AI agents persistent, stateful memory across sessions, achieving 95% recall on LongMemEval while adding only ~720 tokens, and provides Spring AI integration code and self-hosted deployment guidance.
This guide walks through configuring a Redis vector database with Spring AI, adding the vector store advisor for retrieval-augmented generation, ingesting documents via ETL pipelines, and testing the RAG flow with a sample conversation.
The article explains why Spring AI Alibaba's maintenance pause signals mission completion, not abandonment, as Spring AI now natively supports Chinese models via OpenAI-compatible APIs. It argues Java backends remain essential by shifting from writing APIs to organizing capabilities for AI agents through MCP, emphasizing deep business knowledge as the irreplaceable asset.
This guide categorizes Java AI frameworks into infrastructure and orchestration layers, comparing Spring AI, LangChain4j, Solon AI, Spring AI Alibaba, AgentScope, and Embabel with code examples, version requirements, and decision criteria for matching frameworks to project constraints and use cases.
Spring AI Alibaba halted maintenance because Spring AI's rapid maturation and OpenAI protocol standardization made its wrapper redundant, offering three lessons for Java AI developers: avoid secondary wrappers, embrace standard interfaces, and decouple business logic from experimental agent frameworks.
This article details a production-ready customer service knowledge base built with Spring AI 2.0.1 and Milvus, covering immutable index versioning, tenant-isolated retrieval with parameterized filters, deterministic chunk IDs, idempotent ingestion pipelines, dual-index blue-green deployments, and comprehensive observability with automated rollback triggers.
This article details the evolution of a product semantic recall system using Spring AI Embedding, covering architecture design, reliable vector indexing with Outbox pattern, hybrid search fusion, pgvector HNSW tuning, and safe migration to Milvus, with production readiness criteria and observability practices.
This article details the development of an AI-powered contract review system using RAG and Spring AI, covering the tech stack (Java 21, Spring Boot 3.x, MySQL, Redis, MinIO), document parsing with Apache Tika and PaddleOCR, user roles, and features like clause-level risk detection, version control, and knowledge base management.
The author migrates a Spring AI Agent with 30+ business tools to Spring AI 2.0's Tool Search, which uses Lucene indexing to let the model search for relevant tools before calling them, reducing context overload and improving selection accuracy, while sharing best practices for tool descriptions and safe write-operation handling.
The article details how an LLM‑driven Troubleshooter system automates data collection, root‑cause analysis, and recommendation generation for alerts, slashing median investigation time from about 20 minutes to 4.4 minutes across 11 services and over ten alert types, and presents architecture, tool design, observability, a real‑world case, performance metrics, and future roadmap.
This comprehensive guide walks through Harness Engineering concepts, the seven‑layer architecture, environment setup, full Spring Boot codebase, agent configuration, best‑practice recommendations, testing procedures, common issues, and advanced directions for building controllable AI agents with Spring AI Alibaba.
This article details Redis's evolution into a comprehensive AI data infrastructure, covering its four core capabilities—vector search with hybrid queries, native Vector Sets data type, LangCache semantic caching for 70% LLM cost reduction, and Redis Iris context engine for AI agent memory—with technical implementations, performance benchmarks, and use-case recommendations.
Spring AI 2.0.1 patches seven critical CVEs, adds tool call limits and OpenAI audio streaming, updates Google GenAI with image generation, and includes breaking changes like Redis module rename and OpenAI strict mode default off across 80+ issues and PRs.
This guide explains the extension mechanism of Spring AI Alibaba 1.1.2.0, compares built‑in Hooks and Interceptors, provides full code examples for creating custom components, shows project structure, testing procedures, and best‑practice tips for fine‑grained control of AI agents.
This article explains how Spring AI Alibaba’s Agent framework uses Hooks and Interceptors to dynamically control model, tool, and lifecycle contexts, demonstrating the concepts through a complete e‑commerce smart‑customer‑service demo with role‑based tool selection, dynamic prompts, and context compression.
This guide walks through configuring Spring AI Alibaba 1.1.2.0 to persist an agent's short‑term memory in Redis, covering environment setup, core concepts like Checkpointer and StateSerializer, step‑by‑step Maven project creation, code snippets, common pitfalls, and verification of multi‑turn conversation state across sessions.
This article presents a complete, runnable Spring AI Alibaba Tools example (v1.1.2.0) that explains core concepts, three creation methods, registration techniques, advanced features such as JSON schema generation and custom result conversion, environment setup, testing commands, and common troubleshooting tips.
The article presents a layered roadmap of the 2026 Java AI ecosystem, compares major AI frameworks, LLMs, embedding models, vector databases, and agent toolchains, and offers three concrete stack configurations with cost estimates and practical configuration snippets for architects and technical leaders.
This guide shows how to replace external vector databases like Milvus or Qdrant with the JVM’s incubating Vector API and the integrallis/vectors library, providing built‑in distance kernels, indexing (FLAT, HNSW, IVF), and persistence, and demonstrates integration with Spring AI and LangChain4j through concise code examples and required JVM flags.
This article walks Java developers through installing Trae IDE, configuring the required JDK, Maven and Spring AI dependencies, creating a Spring Boot project, adding AI model settings, implementing a chat controller with synchronous and streaming endpoints, and troubleshooting common issues.
This guide analyses why selecting an AI‑Agent framework is more complex than picking a model, defines key evaluation dimensions, compares the major Python and Java ecosystems (LangChain, LangGraph, LangChain4j, Spring AI, LlamaIndex, Haystack, CrewAI), highlights common pitfalls, and provides a step‑by‑step selection process and architectural recommendations for enterprise deployments.
Rod Johnson, the creator of Spring, introduces Embabel—a Kotlin‑based AI agent framework that uses GOAP planning to make agents deterministic, explainable, and Java‑friendly, aiming to turn uncontrolled AI demos into production‑ready systems.
This article walks through the Spring AI Alibaba Agent Framework (v1.1.2.0), explaining the ReAct reasoning‑acting loop, core APIs, configuration, code examples, testing commands, and common troubleshooting steps so developers can quickly create LLM‑driven agents with tool‑calling and memory support.
The article explains why many AI projects fail not because of model performance but due to architectural boundaries, illustrates a real‑world incident caused by an ever‑growing "super Prompt", and shows how Spring AI Alibaba Skills can split responsibilities, enforce governance, and make AI services production‑ready.
The article analyzes why AI applications that require branching, parallelism, retries, human‑in‑the‑loop, or long‑running tasks should adopt Spring AI Alibaba Workflow, explains its core concepts, shows when it fits or not, provides a detailed example and ten real‑world scenarios, and offers practical guidance on design, state management, error handling, persistence, and operational costs.
The article details how a Spring Boot‑based intelligent customer service evolves from a simple chat endpoint to a robust Function Calling architecture, addressing challenges such as real‑time data access, permission checks, idempotency, error handling, token management, and scaling to tens of millions of daily conversations.
This article provides a thorough walkthrough of Spring AI Alibaba Graph, covering its core concepts, API details, environment setup, common pitfalls, and advanced features such as human-in-the-loop support, parallel execution, and checkpointing, enabling Java developers to build sophisticated AI workflows.
Java agents often work in demos but crash in production due to stack mismatches, governance gaps, and runtime issues such as memory drift, large tool outputs, and lack of observability; the Spring‑Ai‑Trip harness adds progressive compression, spill protection, skill injection, hot‑plug tools, and concurrent execution to bridge the gap.
The article explains why Spring AI Ollama requests time out after 10 seconds despite server‑side timeout settings, analyzes the independent OkHttp read timeout and other ineffective configurations, and provides a step‑by‑step solution that creates a custom OllamaApi bean with a longer OkHttp read timeout to permanently fix the issue.
This guide explains vectors, text embedding, embedding models, vector databases, cosine similarity, dimensional trade‑offs, and the full RAG workflow, providing concrete code samples, performance calculations, and real‑world use cases such as semantic search, recommendation, and intelligent customer service.
This guide walks through integrating Spring AI 2.0 into a Spring Boot application, covering Maven dependency setup, model configuration, ChatClient usage for synchronous and streaming calls, Redis‑backed chat memory for multi‑turn conversations, and testing with Postman and a UI component.
This article walks Java developers through turning Spring AI into an enterprise‑grade AI agent that can query internal databases, access a vector‑based knowledge base, enforce role‑based permissions, persist chat sessions in Redis, add full observability, and be container‑deployed with Docker and Kubernetes.
This guide walks through adding embedding support to a Spring AI application by configuring Ollama, creating an EmbeddingService for vector generation and similarity calculations, exposing REST endpoints via EmbeddingController, and providing a simple HTML front‑end for interactive testing, with step‑by‑step instructions and code samples.
An interviewer's question about whether to use LangChain4j or Spring AI reveals that the true decision for enterprise Java AI projects hinges on team expertise, project requirements, and long‑term maintenance rather than just feature counts or ecosystem size.
This tutorial walks through creating a Spring Boot application that integrates the Spring AI Alibaba framework with a local Ollama model to enable function calling, covering environment setup, Maven dependencies, configuration, core code, testing, best practices, and common troubleshooting steps.
This article explains how to equip a Spring AI Agent with real‑world capabilities by annotating Java methods with @Tool, registers those tools for the agent, demonstrates single‑ and multi‑tool orchestration, and shows how the Advisor mechanism brings AOP‑style processing such as RAG and memory management into AI workflows.
Spring AI 2.0 lets Java developers integrate large‑language‑model capabilities with minimal code by adding a starter, configuring model parameters, injecting a ChatClient bean, and optionally enabling Redis‑backed chat memory for multi‑turn conversations, all demonstrated with runnable examples and screenshots.
This article compares Spring AI 2.0 and Spring AI Alibaba, detailing their design philosophies, core architectures, recent upgrades, code examples, strengths, weaknesses, and ideal use‑cases, and explains how the two frameworks can be combined for enterprise AI solutions.
The article analyzes how AI is shifting from answering questions to delivering concrete results—documents, videos, code, and other assets—and argues that Java back‑ends must evolve to manage, audit, version, and publish these AI‑generated artifacts securely and reliably.
This tutorial walks through installing Ollama, configuring JDK 17, adding Spring AI dependencies, setting up application.yml, implementing a chat controller, creating launch scripts, testing the endpoints, and comparing local Ollama with Alibaba Cloud Bailei, highlighting cost‑free, private, offline AI chat in a Spring Boot project.
This article compares Spring AI 2.0 and Spring AI Alibaba, detailing their architectures, core capabilities, pros and cons, and ideal scenarios, and explains how the two can be combined to leverage both atomic AI integration and enterprise‑grade multi‑agent orchestration.
Recent AI releases—OpenAI’s GPT‑Live for full‑duplex voice, ByteDance’s Seedance for multi‑shot video generation, and xAI’s Grok 4.5 for coding and knowledge work—force Java back‑end teams to evolve from handling orders and queues to managing diverse AI capabilities, routing, task orchestration, cost, audit and security.
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.
Spring AI Alibaba Admin, Alibaba’s open‑source extension of Spring AI, addresses three major enterprise hurdles—inefficient prompt debugging, unreliable AI quality, and opaque production operations—by providing versioned prompt management, dataset lifecycle control, flexible evaluator configuration, automated experiment execution, and end‑to‑end observability.
This article walks through adding a zero‑dependency human‑approval node to a Spring AI YAML‑DSL workflow engine, detailing the problem of critical business decisions, the JDK‑based pause‑and‑resume architecture, step‑by‑step code changes, best‑practice recommendations, and real‑world use cases such as large‑payment and content‑review approvals.
The article analyzes how AI‑assisted coding tools are collapsing junior Java positions, driving salaries down, while boosting demand and pay for Java engineers who can integrate AI into enterprise systems, reshaping hiring criteria and interview formats in 2026.
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.
In just 20 minutes, the author uses Cursor AI to generate a full‑stack WeChat mini‑program for personal health management, featuring an AI‑driven health consultant, a Spring Boot 4 backend with JWT security, MySQL storage, and a Vue 3 admin console, and explains the architecture, routing, and deployment details.
This article explains how to integrate Claude Code’s auto‑memory mechanism into Spring AI by using AutoMemoryTools and AutoMemoryToolsAdvisor, compares three integration options, shows the request workflow, memory file formats, and provides concrete code snippets and consolidation strategies for persistent, typed long‑term memory.
Spring AI’s new Session model replaces the fragile sliding‑window ChatMemory, introducing immutable Session metadata, event‑based Turn grouping, configurable compaction triggers and strategies, multi‑agent Branch isolation, and a JDBC‑backed repository to reliably handle long‑running tool‑calling agents.
This article walks through configuring Spring AI on Spring Boot 3.5.0 to convert tool‑calling results among JSON, TOON, XML, CSV, and YAML, presenting two approaches—per‑tool converters and a global delegator—complete with code samples, limitations, and sample output for each format.
Spring AI 2.0 adds self‑healing structured output with schema validation and provider‑side constraints, letting Java applications receive reliable JSON objects from large language models, eliminating brittle string‑cleaning code while still requiring business‑level validation.
This article explains how Function Calling lets large language models invoke Java methods via Spring AI, walks through the four‑step workflow, shows declarative and programmatic tool definitions, and demonstrates a complete customer‑service chatbot with code examples and best‑practice guidelines.
This article explains how to equip a Java application with Retrieval‑Augmented Generation (RAG) so large language models can access private PDFs, Word files, and internal documents, covering the core architecture, two implementation paths using LangChain4j and Spring AI, vector‑store options, and practical tuning techniques.
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.
The article explains how Spring AI’s recursive advisors enable dynamic tool discovery, replacing the traditional all‑tools‑in‑prompt approach, thereby reducing token consumption by 34%‑64% while preserving access to hundreds of tools, and provides benchmark data, code examples, and configurable search strategies.
This tutorial walks Java backend developers through the mature Java LLM ecosystem, comparing frameworks like Spring AI and LangChain4j, and demonstrates step‑by‑step how to create a Spring Boot application with a chat endpoint, streaming responses, and dynamic model switching among OpenAI, Tongyi Qwen, and Ollama.
Spring AI 2.0 reaches GA, offering a production‑grade, Java‑first AI development path tightly integrated with Spring Boot 4.x, Spring Framework 7.0, and the Model Context Protocol, while introducing upgraded agent tooling, Jackson 3, JSpecify annotations, and streamlined provider SDKs.
The article explains that fixed-size chunking in Retrieval‑Augmented Generation ignores semantic boundaries, causing broken sentences, scattered topics, redundant or missing information, and noisy retrieval, and it evaluates overlap as a partial fix while presenting better alternatives such as recursive, semantic, structural, and agentic chunking along with practical production tips and future trends.
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 how the Model Context Protocol (MCP) standardizes AI model access to external tools like file systems, databases, and browsers, showing how to integrate ready‑made MCP servers with Spring AI, expose your own tools as MCP servers, and choose between stdio and SSE transports.
Spring AI 2.0 RC1 is now on Maven Central, bringing a rebuilt architecture on Spring Boot 4, Java 21 baseline, tool‑calling overhaul, native MCP support, structured‑output enhancements, migration steps, pros and cons, and a detailed comparison with LangChain4j.
This article walks Java developers through using Spring AI to build AI agents, comparing it with Python's LangChain, detailing architecture, environment setup, prompt templates, tool integration, RAG implementation, production‑grade features, and a side‑by‑side feature comparison.
This article explains how Spring AI's Tool Calling lets large language models overcome their inability to access real‑time or private data by invoking annotated Java methods, showing step‑by‑step code examples, parameter handling, and the execution flow that bridges model requests with actual method results.
The article examines the limitations of a naïve QuestionAnswerAdvisor RAG implementation, introduces Spring AI's RetrievalAugmentationAdvisor for modular RAG, and demonstrates how query rewriting, multi‑turn compression, and an empty‑context augmenter improve recall and prevent model refusals.
This tutorial explains how Retrieval‑Augmented Generation (RAG) eliminates hallucinations and stale knowledge by retrieving relevant document fragments from a vector store, augmenting prompts, and generating factual answers, with step‑by‑step Spring AI code examples and parameter tuning guidance.
This article shows how to use Spring AI’s ETL pipeline to automatically read PDFs, Word files, and plain‑text documents, split them into token‑based chunks, and load the resulting embeddings into a vector store without manual copying.
This article explains why traditional keyword search fails on synonyms, introduces Spring AI's local ONNX‑based EmbeddingModel to convert text into high‑dimensional vectors, shows how to store and query those vectors with SimpleVectorStore, and compares production‑grade vector store options.
This article compares Spring AI 2.0 and LangChain4j for integrating large language models into Java enterprise applications, examining their positioning, version alignment, programming models, RAG capabilities, tooling, observability, learning curves, and suitability for different team stacks to help you make an informed selection.
This article surveys the ecosystem of Spring Boot‑focused AI Agent Skills, detailing curated repositories, installation steps, core value propositions, integration with Spring AI, and step‑by‑step instructions for creating and sharing custom Skills to boost developer productivity.
The article details how the IC product team upgraded their single‑turn, low‑efficiency answer‑bot to a multi‑turn, Skills‑and‑MCP powered intelligent Q&A agent using Spring AI Alibaba, improving knowledge retrieval, reducing response time, and achieving over 80% automated resolution with extensive evaluation metrics.
This article explains how Spring AI implements the ETL pipeline for Retrieval‑Augmented Generation, detailing the three core components—DocumentReader, DocumentTransformer, and DocumentWriter—along with concrete code examples, configuration parameters, and processing steps for text, PDF, and Tika document sources.
This article explains why large language models are powerful yet limited, defines AI agents as autonomous systems that combine a model, memory, tools, and actions, details the ReAct reasoning‑and‑acting loop, provides a 30‑line Python LangChain example and a Java Spring AI implementation, and outlines five practical use‑case scenarios and the roadmap for the series.
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 Spring AI’s .entity() method lets developers obtain structured Java objects such as POJOs, lists, enums, and response entities directly from LLM outputs, eliminating the need for manual JSON parsing and handling generic‑type issues with ParameterizedTypeReference.
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.
The article explains that embedding converts text into a 1536‑dimensional floating‑point vector that serves as a semantic fingerprint, describes how the vector is generated, why 1536 dimensions are chosen, how similarity is measured, and provides Java Spring AI code examples along with model‑selection guidance and common interview pitfalls.
This guide presents a curated overview of Spring Boot AI Agent Skills, detailing top repositories, installation steps, core functionalities, integration with Spring AI, and step‑by‑step instructions for creating custom Skills to boost development productivity and code quality.
This article walks through constructing a lightweight YAML‑based DSL workflow engine on Spring AI 1.1.2 and Ollama, showing how to define state graphs, register tools, parse and execute nodes—including conditional edges, while loops, and parallel branches—without external orchestration tools.
This tutorial demonstrates how to integrate Spring AI with Ollama, define @Tool‑annotated weather and translation utilities, register them for automatic chaining, and let a large language model answer queries like “fetch Beijing weather and reply in English” using a concise end‑to‑end example.
The article analyzes the hidden dangers of writing raw HTTP calls for large language models in Java projects—hard‑coded keys, fragile request bodies, missing retries, no observability—and demonstrates how Spring AI’s unified abstractions, built‑in resilience, streaming, function calling, and seamless Spring integration eliminate these issues while enabling effortless model switching and production‑grade AI services.
This article demonstrates how to replace static Resilience4j circuit‑breaker parameters in a Spring Boot 3.5 application with dynamically generated values using Spring AI, detailing environment setup, code examples, AI prompt construction, and runtime configuration replacement to improve fault tolerance.
This article explains the limitations of large language models, introduces Retrieval‑Augmented Generation (RAG) and its four‑step workflow, details Spring AI's RAG components and vector‑store options, and provides complete, runnable Java code—including Maven, configuration, and service classes—to build a local knowledge‑base Q&A system.
The article analyzes the drawbacks of manually coding HTTP calls to large language models—hard‑coded keys, fragile request construction, missing retries, and poor observability—and demonstrates how Spring AI’s layered abstraction, unified configuration, built‑in resilience, function calling, RAG support, and seamless Spring ecosystem integration solve these problems for production‑grade Java applications.
This article explains how to replace Spring AI's default in‑memory ChatMemory with a JDBC‑backed implementation that stores conversation history in a MySQL table, covering component changes, schema design, auto‑configuration, and best‑practice configuration details.
This tutorial shows how to replace manual MySQL query writing with Spring AI by extracting the database schema into Markdown, prompting a large language model to produce precise SQL, and wiring the result through Spring Boot services, a controller, and execution logic.
Spring AI Alibaba extends Spring AI with a native agent and workflow framework, integrating Alibaba's DashScope models, ReactAgent, multi‑agent orchestration, Graph workflows, tool calling, memory handling, and production‑grade features, enabling Java developers to create sophisticated AI applications with just a few lines of code.
This article walks through the core concepts of Spring AI Alibaba—including token mechanics, prompt structures, embedding, structured output, chat memory, RAG pipelines, function calling, and graph‑based multi‑agent workflows—while providing concrete code samples, configuration tips, performance tricks, and a curated list of common pitfalls.
This guide explains how Spring AI abstracts LLM conversation memory using a three‑layer architecture, demonstrates configuring MessageWindowChatMemory with a sliding‑window strategy, shows two ways to register the memory advisor, and provides complete Maven, YAML, and Java code examples with test screenshots.
The article demonstrates how to turn ordinary Spring Boot methods into AI‑driven tools, enabling a language model to interpret a natural‑language request, orchestrate a multi‑step workflow (stock query, order creation, warehouse notification), and execute the entire business process without any hard‑coded if‑else logic.
The article details how the product team tackled AI‑driven challenges by designing a two‑layer, event‑driven Function‑Centric Agent architecture that unifies workflow orchestration and capability supply, enabling real‑time inference for billions of items, cutting development cycles to one person‑week, and boosting search conversion rates.
The article walks through building a production‑grade AI Agent system using Spring AI, Alibaba's extensions and the Model Context Protocol (MCP), showing how Java's mature ecosystem—security, microservices, data governance, and workflow—makes it the backbone for next‑generation AI‑native applications.
The article introduces SkillsJars, a Maven‑based solution that packages Agent SKILL.md files into JARs, enabling unified dependency management for Java AI agents and demonstrating integration with Claude Code, Spring AI, and LangChain4j while also covering how to publish custom SkillsJars.
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 article walks through setting up Spring AI with Ollama, explains the tool‑calling workflow, shows two ways to define tools, provides full Maven and YAML configurations, presents runnable Java code for services, chat client, and controller, and addresses common compatibility and dependency issues.
