RAG · Spring DevPro

Spring AI + Milvus: Self-Hosted Vector Database Setup

20 December 2025·1687 words·8 mins

Spring AI Vector Databases DevOps Spring AI Milvus RAG Docker Java 21 Self-Hosted Vector Search LLM

Stop paying for cloud vector storage. Learn how to deploy a high-performance Milvus instance via Docker and integrate it seamlessly with Spring AI 1.0.0+ to build cost-effective, private Retrieval-Augmented Generation (RAG) applications.

Building RAG with Spring AI and Pinecone (Cloud Solution)

18 December 2025·1859 words·9 mins

Spring AI Cloud Architecture Spring Boot RAG Pinecone Vector Database OpenAI Java Generative AI

We explore how to bridge the gap between your private enterprise data and Large Language Models (LLMs) using Spring AI. This tutorial covers the end-to-end implementation of a RAG architecture, utilizing Pinecone as a managed cloud vector store for high-performance context retrieval.

Spring AI + PostgreSQL pgvector: Low-Cost RAG Solution

16 December 2025·1820 words·9 mins

Spring AI Architecture Spring AI PostgreSQL Pgvector RAG GenAI Java Vector Database Backend Development

Why buy a specialized Vector DB when you already have PostgreSQL? This deep dive explores how to implement a cost-effective, production-grade RAG architecture using Spring AI’s PgvectorVectorStore, covering setup, HNSW indexing strategies, metadata filtering, and full implementation details.

Vector Database Comparison: Pinecone vs Milvus vs pgvector

13 December 2025·1779 words·9 mins

Spring AI Architecture Vector Database RAG Pinecone Milvus PostgreSQL Java Spring Boot GenAI

Selecting the right vector store is the critical architectural decision in building RAG applications. This guide compares the three market leaders—Pinecone (SaaS), Milvus (Cloud-native), and pgvector (PostgreSQL extension)—specifically through the lens of Spring AI integration, configuration, and long-term operational costs.

What is RAG? A Complete Guide for Spring Developers

11 December 2025·2042 words·10 mins

Spring AI Tutorials Spring Ai Rag RAG LLM Java Vector Database GenAI Spring Boot

Master the architecture of RAG in the Java ecosystem. This guide covers the theory, the Spring AI abstractions, vector database integration, and a step-by-step implementation of a document-chat system.

Spring AI Testing Guide: Unit Tests, Integration Tests & Mocking

9 December 2025·1614 words·8 mins

Spring AI Testing DevOps Spring AI Testing JUnit 5 Testcontainers Ollama Mockito RAG LLM Ops

Building AI apps is easy; testing them is hard. This guide covers the full spectrum of Spring AI testing, from mocking ChatModels with Mockito to running local inference with Testcontainers and Ollama, and solving the non-deterministic assertion problem.

OpenAI Integration with Spring AI: GPT-4, Embeddings & Streaming

18 October 2025·1781 words·9 mins

Spring AI Spring Boot Spring Ai Openai GPT-4 LLM Java RAG Vector Database Reactive Programming GenAI

Master the Spring AI abstraction layer to build production-ready GenAI applications. This guide covers the full lifecycle of OpenAI integration: from basic chat completions and prompt templates to reactive streaming with Flux, generating vector embeddings, and mapping AI responses to Java Records.

Spring AI vs LangChain4j: Which Java AI Framework to Choose?

13 September 2025·1631 words·8 mins

Spring AI Architecture Java AI LLM RAG Generative AI Spring Boot 3 LangChain OpenAI

The Java AI landscape has evolved into a two-horse race. This guide dissects the trade-offs between the official Spring AI project and the community-driven LangChain4j, focusing on abstraction layers, DX, and production readiness.

Getting Started with Spring AI: A Complete Step-by-Step Guide

1 September 2025·1556 words·8 mins

Spring AI Spring AI RAG LLM Spring Boot

Overview # Spring AI is a powerful library that integrates large language models (LLMs) like OpenAI, Hugging Face, and Azure OpenAI into Spring Boot applications. It simplifies LLM calls to be as intuitive as operating a database (similar to JdbcTemplate), enabling developers to quickly build intelligent features like chatbots, question-answering systems, and content generators.