Building a RAG (Retrieval-Augmented Generation) A more robust version optimized

Building a RAG (Retrieval-Augmented Generation) System with PowerBuilder. A more robust version optimized for handling larger data sets.

Artificial Intelligence is transforming enterprise software development. PowerBuilder developers can now integrate Retrieval-Augmented Generation (RAG) capabilities directly into their applications, enabling an intelligent document-based question-and-answering system powered by Large Language Models (LLMs).

This article demonstrates how to build a complete RAG client in PowerBuilder that connects to a Python-based backend with FastAPI. The system allows users to ingest documents, query them using natural language, and receive AI-generated responses with source citations—all rendered in a professional HTML interface using the Chromium WebBrowser control. Furthermore, this version features a much more robust architecture optimized for handling larger volumes of information compared to previously published code. All the complete explanations and comprehensive details can be found in the article: Building a RAG (Retrieval-Augmented Generation) System with PowerBuilder.

Luis Avilan