This is a summary of the full tutorial published on howtostartprogramming.in.
Introduction Retrieval‑Augmented Generation (RAG) has become the de‑facto pattern for building knowledge‑aware AI applications. In 2026 the ecosystem around Ollama —a lightweight, locally‑hosted LLM server—has matured, and LangChain4j now offers first‑class Java bindings for prompt orchestration, document retrieval, and LLM invocation. Pairing these tools with Spring Boot lets you ship a production‑ready RAG service in minutes, while keeping the codebase idiomatic to the Java ecosystem. This post walks you through the core building blocks of a RAG pipeline using Spring Boot, LangChain4j, and Ollama 2026. We’ll cover: Why Spring Boot is a natural fit for RAG micro‑services. How LangChain4j abstracts retrieval, chunking, and LLM calls. Connecting LangChain4j to an Ollama endpoint (e.g., llam
📖 Read the Full Tutorial
🔗 Building RAG pipeline with Spring Boot LangChain4j and Ollama 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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