Spring AI RAG Implementation with pgvector: 2026 Tutorial

This is a summary of the full tutorial published on howtostartprogramming.in.

TL;DR In 2026, Spring AI + pgvector gives you a production‑ready Retrieval‑Augmented Generation (RAG) stack that runs entirely on PostgreSQL, scales with modern vector indexes, and integrates seamlessly with Spring Boot’s ecosystem. Step What you do Why it matters in 2026 1⃣ Add dependencies Include spring-boot-starter-ai , spring-ai-pgvector-store , and the PostgreSQL driver. All‑in‑one starter keeps your build lean and ensures compatibility with the latest Spring AI 2026 release. 2⃣ Configure pgvector Enable the vector extension, create a documents table with a vector(1536) column, and set spring.ai.vectorstore.pgvector properties. pgvector now supports HNSW and IVF indexes out‑of‑the‑box, delivering sub‑millisecond similarity search at billions of rows. 3⃣ Embed & ingest Use EmbeddingMo

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🔗 Spring AI RAG implementation with pgvector tutorial 2026 — Full Guide with Code Examples

The full article includes:

• ✅ Step-by-step code examples (copy-paste ready)
• ✅ Complete working project (Spring Boot / Java)
• ✅ Common mistakes + fixes
• ✅ Production tips and benchmarks
• ✅ FAQ section

Published on How to Start Programming — practical AI and Java tutorials for developers.