Retrieval-Augmented Generation (RAG) is a powerful design pattern that allows you to ground Large Language Models (LLMs) with your proprietary, real-time context. This prevents hallucinations and eliminates the need for expensive model fine-tuning. This comprehensive guide walks you through building a completely local, production-ready RAG application using the Spring ai. 1. Prerequisites and Local Environment Setup Before touching Java code, you need to set up the infrastructure. Create a compose.yml file to spin up PostgreSQL (with the pgvector extension) and Ollama: docker-compose.yml services : postgres : image : pgvector/pgvector:pg16 container_name : spring-ai-rag-postgres environment : POSTGRES_DB : spring_ai_rag POSTGRES_USER : postgres POSTGRES_PASSWORD : postgres ports : - " 5432:5432" ollama : image : ollama/ollama:latest container_name : spring-ai-rag-ollama ports : - " 11434:11434" Enter fullscreen mode Exit fullscreen mode Pulling the Local AI Models Start your Docker containers by running…