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This tutorial guides developers through creating a Retrieval-Augmented Generation (RAG) system in Java using Spring Boot, Vertex AI, and BigQuery. The article demonstrates how to build a web application that processes PDF documents, generates embeddings, and enables natural-language question answering. Author Mohammed Fazalullah Qudrath explains how to integrate Spring AI with cloud services for scalable AI solutions. The step-by-step approach covers document ingestion, vector search, and UI integration for enterprise use cases. Readers will gain hands-on experience implementing a production-ready RAG pipeline with real-world deployment options.

Key facts

• The tutorial combines Spring Boot, Vertex AI embeddings, and BigQuery vector search for document-based question answering.
• It includes a web UI built with Thymeleaf for uploading PDFs and interacting with the AI system in real time.
• Spring AI simplifies integration with cloud providers like Google Cloud, OpenAI, and Azure for scalable deployments.
• The BigQuery schema stores document metadata and embeddings in a structured format for efficient retrieval.
• The complete codebase is available on GitHub for replication and customization.