How to Build a RAG Pipeline with Spring Boot, Embeddings, and Vector Search

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Developer Kindson Munonye has published a step-by-step tutorial on building a Retrieval-Augmented Generation (RAG) pipeline using Spring Boot. The guide, part of an ongoing AI Developer Tutorials series, walks through ingesting documents, chunking text, generating embeddings, and storing them in a pgvector-backed VectorStore. When a user submits a question, the system embeds the query, retrieves the top-K most similar document chunks, and passes them as context to an LLM to generate a grounded answer. The implementation relies on Spring AI's OpenAI and pgvector starters, with full source code available on GitHub. The tutorial is positioned as a continuation of earlier Spring AI REST basics covered in a prior module of the series.

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