PDF Retrieval with Vision Language Models

The blog post discusses the integration of Vision Language Models (VLMs) into document retrieval systems, particularly focusing on the ColPali model, which simplifies the process by directly embedding screenshots of complex documents like PDFs into vector representations. This approach eliminates the need for traditional preprocessing steps such as Optical Character Recognition (OCR) and text chunking, thus improving retrieval efficiency and accuracy. ColPali demonstrates superior performance on the Visual Document Retrieval (ViDoRe) benchmark, outperforming traditional text-based retrieval models like BM25 and BGE-M3. By utilizing Vespa's tensor framework, ColPali embeddings can be effectively represented and used in retrieval and ranking pipelines, allowing for the combination of powerful Vision LLMs with existing retrieval systems. The article emphasizes that this method not only enhances retrieval performance but also simplifies the process, making it accessible for complex document formats while maintaining flexibility for multilingual and specialized domain applications.