ColPali, a vision-language model, offers a simplified pipeline for document retrieval by converting pages to images and leveraging multi-vector representations. This approach captures both textual and visual information, including tables, figures, and layout, leading to more comprehensive document understanding. ColPali outperforms traditional text-based retrieval methods, especially for visually complex documents. The combination of ColPali with Milvus provides fast and scalable vector search capabilities, making it ideal for storing and retrieving multi-vector representations. ColPali can visualize which parts of a document match specific query terms, providing insights into why a document was retrieved. This technology has real-world applications in legal document search, scientific literature review, technical documentation, and financial analysis. ColPali represents a paradigm shift in document retrieval by moving from "what you extract is what you search" to "what you see is what you search."