BestHub
Sign in
Tagged articles

LangChain4j

62 articles · Page 1 of 1
Su San Talks Tech
Su San Talks Tech
Jun 30, 2026 · Artificial Intelligence

LangChain4j vs LangGraph4j: Which Java AI Framework Fits Your Needs?

This article compares LangChain4j and LangGraph4j, explaining that the former is an AI capability integration layer for Java while the latter is a state‑graph workflow engine, and guides developers on when to use each based on features such as model access, tool calling, multi‑agent orchestration, conditional routing, checkpointing, and version maturity.

AI agentsJavaLangChain4j
0 likes · 19 min read
LangChain4j vs LangGraph4j: Which Java AI Framework Fits Your Needs?
Coder Trainee
Coder Trainee
Jun 22, 2026 · Artificial Intelligence

Building Java AI Agents with LangChain4j: A Hands‑On Guide

This article explains why LangChain4j is needed for advanced Java AI agents, compares its capabilities with Spring AI, walks through project setup, configuration, defining tools and memory, assembling the agent, and demonstrates a complete smart‑customer service example with testing commands.

AI agentsChatMemoryJava
0 likes · 10 min read
Building Java AI Agents with LangChain4j: A Hands‑On Guide
Coder Trainee
Coder Trainee
Jun 20, 2026 · Artificial Intelligence

Java RAG Tutorial: Vector Search and Knowledge‑Base Integration

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.

JavaLangChain4jRAG
0 likes · 12 min read
Java RAG Tutorial: Vector Search and Knowledge‑Base Integration
Architect's Tech Stack
Architect's Tech Stack
Jun 18, 2026 · Backend Development

Why Ditch Spring AI? 3 Java AI Frameworks That Supercharge Spring Boot

The article compares three Java‑centric AI frameworks—LangChain4j, Spring AI Alibaba, and LangGraph4j—showing how each integrates with Spring Boot to turn ordinary services into stateful, workflow‑driven AI agents, and offers guidance on picking the right tool for different project needs.

Agent frameworkJava AILangChain4j
0 likes · 11 min read
Why Ditch Spring AI? 3 Java AI Frameworks That Supercharge Spring Boot
IoT Full-Stack Technology
IoT Full-Stack Technology
Jun 8, 2026 · Artificial Intelligence

Spring AI 2.0 vs LangChain4j: Which Should You Choose?

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.

AI frameworksJavaLLM integration
0 likes · 12 min read
Spring AI 2.0 vs LangChain4j: Which Should You Choose?
Java Backend Technology
Java Backend Technology
May 19, 2026 · Artificial Intelligence

Top 5 Java AI Frameworks You Should Know

This article reviews the five major Java AI frameworks—Spring AI, LangChain4j, Spring AI Alibaba, AgentScope‑Java, and Semantic Kernel—detailing their architectures, core features, pros and cons, and provides guidance on selecting the right one for different enterprise scenarios.

AIAgentScopeJava
0 likes · 22 min read
Top 5 Java AI Frameworks You Should Know
Architect's Guide
Architect's Guide
May 7, 2026 · Artificial Intelligence

Spring AI 2.0 vs LangChain4j: Which Should You Choose?

The article provides a side‑by‑side analysis of Spring AI 2.0 and LangChain4j, comparing their goals, version alignment, programming models, RAG and agent capabilities, ecosystem integration, learning curve, and operational considerations to help Java teams decide which library best fits their project constraints.

AI agentsJavaLLM integration
0 likes · 11 min read
Spring AI 2.0 vs LangChain4j: Which Should You Choose?
java1234
java1234
May 5, 2026 · Artificial Intelligence

Spring AI 2.0: New Video Tutorial Series Empowers Java Developers with AI

The author announces a refreshed Spring AI 2.0 video tutorial series and provides a detailed overview of the framework’s design goals, provider‑agnostic API, full‑type model support, Spring integration, enterprise value, typical use cases, and a comparison with competing Java AI libraries.

AI FrameworkJavaLangChain4j
0 likes · 7 min read
Spring AI 2.0: New Video Tutorial Series Empowers Java Developers with AI
MeowKitty Programming
MeowKitty Programming
Apr 29, 2026 · Artificial Intelligence

10 Must‑Try Open‑Source AI Projects for Java Developers: RAG, Agents, Knowledge Bases, and Text‑to‑SQL

This article curates ten open‑source AI projects on Gitee that Java developers can use to learn RAG pipelines, AI agents, knowledge‑base construction, Text‑to‑SQL, workflow orchestration, and multi‑model integration, offering concrete use cases, learning goals, and guidance on selecting a learning path.

AIJavaLangChain4j
0 likes · 13 min read
10 Must‑Try Open‑Source AI Projects for Java Developers: RAG, Agents, Knowledge Bases, and Text‑to‑SQL
The Dominant Programmer
The Dominant Programmer
Apr 28, 2026 · Backend Development

Spring Boot, LangChain4j & Ollama: Chain for Intent Recognition and Task Dispatch

The article demonstrates how to construct a Spring Boot application that orchestrates multiple AI services using LangChain4j and Ollama, defining intent‑classification and tool‑based assistants, registering them as beans, and routing user requests through a controller to achieve multi‑step intent recognition and task dispatch in a simulated intelligent customer‑service workflow.

AI orchestrationLangChain4jOllama
0 likes · 13 min read
Spring Boot, LangChain4j & Ollama: Chain for Intent Recognition and Task Dispatch
The Dominant Programmer
The Dominant Programmer
Apr 27, 2026 · Artificial Intelligence

Build and Integrate a Local LLM with Spring Boot, LangChain4j, and Ollama

This guide walks through installing Ollama on Windows, downloading a Qwen2.5‑7B model, configuring Spring Boot with LangChain4j dependencies, setting up application.yml, defining AI service interfaces, adding conversation memory, creating REST and streaming controllers, and testing the end‑to‑end local LLM workflow.

AIChatbotLLM
0 likes · 12 min read
Build and Integrate a Local LLM with Spring Boot, LangChain4j, and Ollama
The Dominant Programmer
The Dominant Programmer
Apr 27, 2026 · Artificial Intelligence

Building a Private Document Vector Search with SpringBoot, LangChain4j, and Ollama RAG

This guide walks through why Retrieval‑Augmented Generation (RAG) is needed for large language models, explains the three‑step indexing and query workflow, details LangChain4j’s core components, and provides a complete SpringBoot example—including Maven setup, configuration, service code, and troubleshooting—to create a private document‑vector search system powered by Ollama.

EmbeddingLangChain4jOllama
0 likes · 13 min read
Building a Private Document Vector Search with SpringBoot, LangChain4j, and Ollama RAG
The Dominant Programmer
The Dominant Programmer
Apr 25, 2026 · Backend Development

Integrating LangChain4j with Spring Boot for Fast AI Conversations on Alibaba Baichuan

This guide walks through using the SpringAIAlibaba framework to integrate Alibaba Baichuan with Spring Boot via LangChain4j, explains core concepts, compares LangChain4j to Spring AI and OpenAI, and provides step‑by‑step dependency setup, environment configuration, code examples, and a simple browser test.

AI chatAgentAlibaba Baichuan
0 likes · 11 min read
Integrating LangChain4j with Spring Boot for Fast AI Conversations on Alibaba Baichuan
Ray's Galactic Tech
Ray's Galactic Tech
Apr 24, 2026 · Backend Development

From Bottlenecks to a High‑Concurrency Medical Assistant with LangChain4j

This guide details how to design and implement a production‑grade, high‑concurrency medical AI assistant using LangChain4j, Spring Boot, Redis, and Kubernetes, covering architecture, RAG‑enhanced retrieval, controlled tool invocation, guardrails, idempotent transactions, scaling strategies and observability to ensure reliable, compliant patient interactions.

High concurrencyLangChain4jRAG
0 likes · 33 min read
From Bottlenecks to a High‑Concurrency Medical Assistant with LangChain4j
java1234
java1234
Apr 24, 2026 · Artificial Intelligence

Choosing Between Spring AI 2.0 and LangChain4j for Java AI Development

This article compares Spring AI 2.0 and LangChain4j, examining their positioning, version alignment, architecture, programming model, RAG support, observability, learning curve, and ecosystem integration to help Java teams decide which library best fits their AI project constraints.

AI librariesJavaLLM integration
0 likes · 13 min read
Choosing Between Spring AI 2.0 and LangChain4j for Java AI Development
java1234
java1234
Apr 22, 2026 · Artificial Intelligence

Getting Started with LangChain4j: Building Java AI Agents with a Mature LLM Framework

LangChain4j fills the long‑standing gap for Java developers by offering a Java‑native, enterprise‑grade LLM framework that abstracts model calls, prompts, memory, tools, RAG, streaming and structured output, enabling quick setup, clean AI Service definitions, and seamless integration into Spring Boot or Quarkus applications.

AI servicesChatMemoryJava
0 likes · 24 min read
Getting Started with LangChain4j: Building Java AI Agents with a Mature LLM Framework
Ray's Galactic Tech
Ray's Galactic Tech
Apr 21, 2026 · Artificial Intelligence

From Demo to Production: Building a Scalable AI Agent Web App with LangChain4j

Learn how to transform a simple LangChain4j demo into a production‑ready AI agent web application by designing a robust architecture, implementing multi‑agent orchestration, RAG, tool integration, session management, observability, security, and scalable deployment with Spring Boot, PostgreSQL, Redis, Kafka, Docker and Kubernetes.

AILangChain4jMicroservices
0 likes · 43 min read
From Demo to Production: Building a Scalable AI Agent Web App with LangChain4j
MeowKitty Programming
MeowKitty Programming
Apr 21, 2026 · Backend Development

2026 AI Priorities for Java Developers: Structured Output, RAG, and Observability

While many Java teams chase flashy AI demos and agents, the real 2026 focus has shifted to engineering concerns—ensuring model outputs reliably map to Java objects, integrating Retrieval‑Augmented Generation into robust data pipelines, and adding observability so AI services can be monitored and debugged like traditional back‑end components.

AILangChain4jObservability
0 likes · 7 min read
2026 AI Priorities for Java Developers: Structured Output, RAG, and Observability
MeowKitty Programming
MeowKitty Programming
Apr 20, 2026 · Backend Development

Why Java AI Is Moving Beyond Agents: Spring AI vs. LangChain4j Redefine Backend Development

The article explains that in 2026 Java AI development shifts from simple model SDKs and prompt engineering to engineered, production‑ready solutions, highlighting Spring AI’s new stable releases with dynamic structured output and LangChain4j’s mature integration options, and compares their suitability for Spring‑centric versus framework‑agnostic projects.

Backend EngineeringJava AILangChain4j
0 likes · 7 min read
Why Java AI Is Moving Beyond Agents: Spring AI vs. LangChain4j Redefine Backend Development
Programmer XiaoFu
Programmer XiaoFu
Apr 20, 2026 · Artificial Intelligence

How Java + LangChain4j Can Eliminate Messy Chunking for High‑Quality RAG Document Splitting

The article explains why fixed‑size chunking harms RAG recall, demonstrates three semantic‑chunking strategies—including recursive punctuation splitting, overlapping windows, and parent‑child document mapping—and provides complete Java/LangChain4j code that integrates tokenizers, Redis, and Qdrant to boost retrieval performance.

EmbeddingJavaLangChain4j
0 likes · 10 min read
How Java + LangChain4j Can Eliminate Messy Chunking for High‑Quality RAG Document Splitting
MeowKitty Programming
MeowKitty Programming
Apr 14, 2026 · Backend Development

Why Java + AI Will Become the Backend Breakthrough by 2026

With Spring AI 1.1, LangChain4j, and MCP Java SDK now offering mature, framework‑level AI capabilities, Java backend teams can move beyond ad‑hoc model calls to fully engineered AI integration—RAG, tool calling, and agents—making Java a viable, production‑ready AI stack for enterprises by 2026.

AIBackend DevelopmentJava
0 likes · 7 min read
Why Java + AI Will Become the Backend Breakthrough by 2026
LuTiao Programming
LuTiao Programming
Apr 3, 2026 · Artificial Intelligence

Beyond Simple API Calls: The 2026 Complete Guide to Java AI Frameworks

This article explains why enterprise‑grade AI development in Java goes far beyond calling a model, introduces the five major Java AI frameworks—Spring AI, LangChain4j, Spring AI Alibaba, AgentScope‑Java, and Semantic Kernel—compares their core features, provides concrete code samples, offers a selection matrix for different scenarios, and outlines future trends in AI system orchestration.

AI frameworksAgentScopeJava
0 likes · 7 min read
Beyond Simple API Calls: The 2026 Complete Guide to Java AI Frameworks
Ray's Galactic Tech
Ray's Galactic Tech
Mar 27, 2026 · Artificial Intelligence

Choosing Between LangChain4j and Spring AI: Which Java AI Framework Wins in Production?

This article provides a deep, production‑grade comparison of LangChain4j and Spring AI, examining their architectural philosophies, engineering governance, high‑concurrency design, code examples, and real‑world scenarios to help Java teams decide which framework best fits their AI system boundaries, team capabilities, and long‑term evolution goals.

Enterprise IntegrationJava AILangChain4j
0 likes · 29 min read
Choosing Between LangChain4j and Spring AI: Which Java AI Framework Wins in Production?
Programmer XiaoFu
Programmer XiaoFu
Mar 27, 2026 · Artificial Intelligence

Top 5 Java AI Frameworks for Enterprise Applications

This article analyzes the emerging Java AI ecosystem, comparing Spring AI, LangChain4j, Spring AI Alibaba, AgentScope‑Java, and Microsoft Semantic Kernel, and provides guidance on selecting the right framework based on features such as RAG, agent support, observability, security sandbox, and cloud integration.

AI frameworksAgentScopeJava
0 likes · 19 min read
Top 5 Java AI Frameworks for Enterprise Applications
SpringMeng
SpringMeng
Mar 7, 2026 · Artificial Intelligence

LangChain4j vs Spring AI: Which Java AI Framework Is Right for Your Project?

The article compares LangChain4j and Spring AI across design philosophy, core features, ecosystem integration, community maturity, and learning curve, providing concrete code examples, a feature‑richness matrix, and practical selection guidelines to help Java developers choose the most suitable AI framework for their needs.

AI frameworksAgentComparison
0 likes · 15 min read
LangChain4j vs Spring AI: Which Java AI Framework Is Right for Your Project?
Java Architecture Diary
Java Architecture Diary
Feb 10, 2026 · Artificial Intelligence

Boost RAG Accuracy with LangChain4j 1.11.0 Hybrid Search on PgVector

This guide explains why pure vector retrieval often fails for version‑specific queries, introduces hybrid search that combines semantic and keyword matching, and provides step‑by‑step code and SQL examples for enabling PgVector hybrid search in LangChain4j 1.11.0.

Full-Text SearchHybrid SearchLangChain4j
0 likes · 11 min read
Boost RAG Accuracy with LangChain4j 1.11.0 Hybrid Search on PgVector
JakartaEE China Community
JakartaEE China Community
Jan 20, 2026 · Backend Development

How to Build AI‑Powered Java Apps with Helidon and LangChain4j

This article explains how Helidon 4.2 integrates the LangChain4j framework to simplify adding large‑language‑model capabilities, covering core features, Maven setup, configuration, component creation, dependency injection, annotations, custom tools, and sample applications such as a coffee‑shop assistant.

AI integrationDependency InjectionHelidon
0 likes · 14 min read
How to Build AI‑Powered Java Apps with Helidon and LangChain4j
JakartaEE China Community
JakartaEE China Community
Dec 16, 2025 · Artificial Intelligence

Build a Retrieval‑Augmented Generation (RAG) System with Langchain4j and Ollama 3

This guide walks through the importance of Retrieval‑Augmented Generation, outlines the core Langchain4j and Ollama 3 components, and provides a complete Java example—including Maven setup, document ingestion, embedding creation, similarity search, prompt construction, and response generation—to demonstrate a functional RAG pipeline.

EmbeddingJavaLLM
0 likes · 9 min read
Build a Retrieval‑Augmented Generation (RAG) System with Langchain4j and Ollama 3
JakartaEE China Community
JakartaEE China Community
Nov 18, 2025 · Artificial Intelligence

How to Build a Retrieval‑Augmented Generation (RAG) System with Langchain4j and Ollama 3

This article explains why Retrieval‑Augmented Generation improves LLM accuracy, outlines the key Langchain4j and Ollama3 components, and provides a step‑by‑step Java example—including Maven setup, document ingestion, embedding, similarity search, prompt creation, and response generation—to demonstrate a functional RAG pipeline.

EmbeddingJavaLLM
0 likes · 8 min read
How to Build a Retrieval‑Augmented Generation (RAG) System with Langchain4j and Ollama 3
macrozheng
macrozheng
Jul 4, 2025 · Artificial Intelligence

Build Java LLM Applications with LangChain4j: A Hands‑On Guide

This tutorial walks through the fundamentals of large language models, prompt engineering, word embeddings, and shows how to use the LangChain framework (including its Java implementation LangChain4j) to build, memory‑manage, retrieve, and chain AI‑driven applications with practical code examples.

AIEmbeddingJava
0 likes · 17 min read
Build Java LLM Applications with LangChain4j: A Hands‑On Guide
Big Data Technology & Architecture
Big Data Technology & Architecture
May 26, 2025 · Artificial Intelligence

Spring AI 1.0 Released: Maven Dependency, Comparison with LangChain4j, and Future Trends

The article announces the Spring AI 1.0 release, provides the Maven BOM dependency, compares Spring AI with LangChain4j for Java AI development, and outlines upcoming integration, performance, and observability improvements for both frameworks amid rapid large‑model advancements.

Artificial IntelligenceJavaLangChain4j
0 likes · 4 min read
Spring AI 1.0 Released: Maven Dependency, Comparison with LangChain4j, and Future Trends
JavaEdge
JavaEdge
Apr 24, 2025 · Artificial Intelligence

How to Customize HTTP Clients for LangChain4j LLM Integration in Java

This guide explains how LangChain4j modules let you replace the default HTTP client used to call LLM provider APIs, showing two out‑of‑the‑box implementations (JdkHttpClient and SpringRestClient) and providing step‑by‑step code examples for custom JDK and Spring RestClient configurations.

HTTP ClientJavaLLM
0 likes · 4 min read
How to Customize HTTP Clients for LangChain4j LLM Integration in Java
JavaEdge
JavaEdge
Apr 21, 2025 · Artificial Intelligence

How to Build a LangChain4j MCP Tool Provider with Docker and GitHub Integration

This tutorial explains how to use LangChain4j's Model Context Protocol (MCP) to create a tool provider, configure HTTP or stdio transports, run a GitHub MCP server in Docker, and employ a language model to summarize recent repository commits, complete with code samples and logging customization.

AIDockerGitHub
0 likes · 11 min read
How to Build a LangChain4j MCP Tool Provider with Docker and GitHub Integration
Java Architecture Diary
Java Architecture Diary
Apr 2, 2025 · Artificial Intelligence

Run AI Models Locally with Docker Model Runner and Java Integration

This article explains how Docker Model Runner enables effortless local execution of AI models, details platform support, provides a full command reference, shows how to use the REST endpoint, and demonstrates integration with Java via LangChain4j, including code examples and a feature comparison with Ollama.

AIDockerLangChain4j
0 likes · 9 min read
Run AI Models Locally with Docker Model Runner and Java Integration
Java Architecture Diary
Java Architecture Diary
Mar 26, 2025 · Artificial Intelligence

How DeepSeek V3-0324 Boosts Java AI Apps with Function Calling

The article introduces DeepSeek's new V3-0324 model, highlights its performance gains and new features like function calling and standardized JSON output, demonstrates Chinese and frontend coding tests, provides Java code examples for AI integration, and concludes with a summary of its business impact.

AIChat2BIDeepSeek
0 likes · 6 min read
How DeepSeek V3-0324 Boosts Java AI Apps with Function Calling
Architect
Architect
Mar 20, 2025 · Artificial Intelligence

Building a Gitee AI Repository Assistant with MCP and LangChain4j

This article explains the Model Context Protocol (MCP) introduced by Gitee, shows how Java developers can integrate it using LangChain4j, compares stdio and SSE transport modes, provides full code samples, installation steps, and demonstrates a practical AI‑powered repository assistant.

AIGiteeJava
0 likes · 9 min read
Building a Gitee AI Repository Assistant with MCP and LangChain4j
JD Tech Talk
JD Tech Talk
Jan 9, 2025 · Artificial Intelligence

Practical Guide to Building Retrieval‑Augmented Generation (RAG) Applications with LangChain4j in Java

This article provides a step‑by‑step tutorial for Java engineers on using the LangChain4j framework to implement Retrieval‑Augmented Generation (RAG) with large language models, covering concepts, environment setup, code integration, document splitting, embedding, vector‑store operations, and prompt engineering.

EmbeddingJavaLangChain4j
0 likes · 35 min read
Practical Guide to Building Retrieval‑Augmented Generation (RAG) Applications with LangChain4j in Java
JD Cloud Developers
JD Cloud Developers
Jan 9, 2025 · Artificial Intelligence

Boost Your Java Apps with LangChain4j: A Hands‑On RAG Guide

This article walks Java developers through the fundamentals of Retrieval‑Augmented Generation (RAG), explains the LangChain4j framework, compares large‑model development with traditional Java coding, and provides step‑by‑step code examples for environment setup, document splitting, embedding, vector‑store operations, and LLM interaction.

EmbeddingJavaLangChain4j
0 likes · 34 min read
Boost Your Java Apps with LangChain4j: A Hands‑On RAG Guide
JD Tech
JD Tech
Oct 13, 2024 · Artificial Intelligence

Building a Simple Local AI Question‑Answer System with Java, LangChain4J, Ollama, and ChromaDB

This article guides readers through the concepts of large language models, embeddings, vector databases, and Retrieval‑Augmented Generation, then demonstrates step‑by‑step how to set up Ollama, install a local Chroma vector store, configure Maven dependencies, and write Java code using LangChain4J to build and test a functional AI Q&A application.

AIJavaLLM
0 likes · 22 min read
Building a Simple Local AI Question‑Answer System with Java, LangChain4J, Ollama, and ChromaDB
JavaEdge
JavaEdge
Sep 24, 2024 · Artificial Intelligence

Mastering RAG with LangChain4j: From Simple Setup to Advanced Retrieval‑Augmented Generation

This article explains how to extend large language models with domain‑specific knowledge using Retrieval‑Augmented Generation (RAG) in LangChain4j, covering the concepts of RAG, its indexing and retrieval stages, simple RAG setup, detailed API usage, and advanced customization options such as query transformers and content injectors.

EmbeddingJavaLLM
0 likes · 24 min read
Mastering RAG with LangChain4j: From Simple Setup to Advanced Retrieval‑Augmented Generation
JavaEdge
JavaEdge
Sep 21, 2024 · Artificial Intelligence

Understanding LLM API Types and Usage in LangChain4j

This article explains the different low‑level LLM API types in LangChain4j, including LanguageModel, ChatLanguageModel, and other model interfaces, and shows how to create and combine ChatMessage objects for multi‑turn conversations.

AI APIChatLanguageModelChatMessage
0 likes · 8 min read
Understanding LLM API Types and Usage in LangChain4j
JavaEdge
JavaEdge
Sep 20, 2024 · Artificial Intelligence

Integrating LangChain4j with Spring Boot: AI Services Made Easy

This guide explains how to use LangChain4j Spring Boot starters to configure OpenAI models, create declarative AI services with @AiService, and run practical examples such as a customer‑support agent and a simple HelloWorld program, covering required dependencies, properties, and code snippets.

ChatLanguageModelDeclarative AI ServiceJava
0 likes · 8 min read
Integrating LangChain4j with Spring Boot: AI Services Made Easy
JavaEdge
JavaEdge
Sep 19, 2024 · Artificial Intelligence

Unlock Java LLM Power: A Deep Dive into LangChain4j Features and Architecture

LangChain4j streamlines the integration of large language models into Java applications by offering a standardized API, extensive support for over a dozen LLM providers and vector stores, a rich toolbox for RAG, chat memory, and tool calling, plus two abstraction layers that cater to both low‑level control and high‑level convenience.

AIJavaLLM
0 likes · 10 min read
Unlock Java LLM Power: A Deep Dive into LangChain4j Features and Architecture