Visual RAG over PDFs with Vespa - A demo application in Python

The blog post outlines the development of a live demo application using Vespa to enhance Visual RAG (Retrieve and Generate) capabilities over PDFs, focusing on the challenges of making PDFs searchable, particularly those containing images, charts, and non-extractable text. The project employed ColPali embeddings and Vision Language Models (VLMs) to improve semantic search efficiency across various industries. Built entirely in Python, using the FastHTML framework, the application aims to bridge the gap between backend and frontend development, offering a professional-looking UI and efficient performance. The team used a custom dataset from the Norwegian Government Pension Fund Global, generating synthetic queries for testing. The application leverages Vespa's advanced features like phased ranking and type-ahead suggestions to optimize search results, demonstrating the utility of combining text-based and visual retrieval methods. The blog also highlights the project's collaborative nature and the potential to scale and adapt the demo for other datasets and technologies.