This is a summary of the full tutorial published on howtostartprogramming.in.
TL;DR Plug a local LLM into a Spring Boot app in seconds with LangChain4j . The library abstracts model loading, prompt chaining, and response handling, letting you keep data on‑premise while writing idiomatic Spring code. Table of Contents TL;DR Introduction Prerequisites Core Concepts LangChain4j Abstractions Step‑by‑Step Integration 1. Add the required dependencies 2. Configure the LLM bean Full Example Project 1. Project Structure 2. pom.xml Common Mistakes & How to Avoid Them Production‑Ready Tips 1. Scale with Asynchronous Calls 2. Cache Expensive LLM Responses FAQ – Using a Local LLM with Spring Boot & LangChain4j (2026) 1. What license do I need for the LLM and LangChain4j? 2. Should I run inference on GPU or CPU? Key Takeaways Why go local with LangChain4j & Spring Boot? Best‑Prac
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🔗 How to use local LLM with Spring Boot and LangChain4j 2026 — Full Guide with Code Examples
The full article includes:
- ✅ Step-by-step code examples (copy-paste ready)
- ✅ Complete working project (Spring Boot / Java)
- ✅ Common mistakes + fixes
- ✅ Production tips and benchmarks
- ✅ FAQ section
Published on How to Start Programming — practical AI and Java tutorials for developers.
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