Building a Retrieval-Augmented Generation (RAG) Application with Ollama 3.2 and Spring Boot
Building a RAG Application with Ollama 3.2 and Spring Boot This blog post demonstrates how to build a Retrieval-Augmented Generation (RAG) application using Ollama 3.2 for large language models (LLMs) and Spring Boot for creating REST APIs. RAG combines information retrieval with LLMs to provide more accurate and contextually relevant answers. We'll leverage Docker Desktop for containerization and pgvector for vector storage. Project Setup We'll use Spring Boot version 3.3.7 for this project. Here's a breakdown of the key components and configurations: 1. Dependencies (Gradle): dependencies { implementation 'org.springframework.boot:spring-boot-starter-jdbc' implementation 'org.springframework.boot:spring-boot-starter-web' implementation 'com.fasterxml.jackson.module:jackson-module-kotlin' implementation 'org.springframework.ai:spring-ai-ollama-spring-boot-starter' ...