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"

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Pulling the Local AI Models

Start your Docker containers by…