Scaling ColPali to billions of PDFs with Vespa

ColPali is a sophisticated document retrieval model that leverages vision language models (VLMs) to enhance document retrieval by incorporating both textual and visual information. The blog post explores how ColPali can be scaled to manage billions of PDF documents using Vespa, an AI-powered platform that supports phased retrieval and ranking pipelines. A key innovation is the introduction of a hamming-based MaxSim similarity function, which significantly reduces computational costs and storage requirements by using binary vectors instead of traditional floating-point vectors. This approach allows for efficient real-time indexing and retrieval, enabling faster search results without compromising accuracy. ColPali's ability to generate embeddings directly from images of document pages bypasses the need for text extraction and OCR, simplifying the data ingestion process and making it more suitable for large-scale applications. The blog also provides insights into the performance gains achieved through this method, including a 32x reduction in storage and a 4x increase in efficiency, while maintaining competitive accuracy levels. The post is accompanied by resources and examples to help users implement and test ColPali within Vespa, emphasizing the model's potential to transform document retrieval by integrating advanced visual and text-based analysis.