Build a RAG Pipeline in Java (Text Vector LLM, No Paid APIs)

Ever asked an LLM a question about your own data and received an incorrect or generic answer? That’s because Large Language Models (LLMs) don’t know your private data. In this article, we’ll build a complete Retrieval-Augmented Generation (RAG) pipeline using: Java PostgreSQL (with vector support) Ollama (local LLM + embeddings) 👉 No OpenAI / No paid APIs 👉 Fully local 👉 Practical and production-relevant 🧠 What is RAG? Retrieval-Augmented Generation (RAG) is an architecture that improves LLM responses by: Retrieving relevant data from a knowledge source Passing that data to the LLM Generating an answer grounded in that context In simple terms: Instead of guessing, the model first looks up relevant information and then answers. 🔍 Why Do We Need RAG? LLMs are powerful, but they have limitations: ❌ They don’t know your private/company data ❌ Their knowledge is static ❌ They can hallucinate RAG solves this by combining: Your data (database) Smart retrieval (vector search) LLM reasonin