Building Semantic Search with Spring Boot, PostgreSQL, and pgvector (RAG Retrieval)

In the previous article, we built the indexing pipeline for our knowledge base: documents are saved content is split into chunks embeddings are generated vectors are stored in PostgreSQL using pgvector But indexing is only half of the system. The real value comes when users can ask questions and receive answers based on the indexed knowledge . In this article we will implement the retrieval side of the architecture using Spring Boot. By the end of this tutorial, our system will support: receiving a user question converting the question into an embedding searching the vector database for similar chunks building a prompt with contextual information sending that prompt to an AI client returning a grounded response This architecture is commonly known as Retrieval-Augmented Generation (RAG) . Understanding the Retrieval Flow Once documents are indexed, the query flow looks like this: User question ↓ Convert question into embedding ↓ Vector similarity search in PostgreSQL ↓ Retrieve most rel