ColPali: Enhanced Document Retrieval with Vision Language Models and ColBERT Embedding Strategy

ColPali is a document retrieval model that uses Vision Language Models (VLMs) to index documents through their visual features, capturing both textual and visual elements. It generates ColBERT-style multi-vector representations of text and images, encoding document images directly into a unified embedding space. This approach bypasses complex extraction processes, improving retrieval accuracy and efficiency. The model is built upon Google's PaliGemma-3B model and uses a late interaction similarity mechanism to compare query and document embeddings at query time. ColPali faces challenges due to its high storage demands and computational complexity but has significant potential in transforming how we retrieve visually rich content with textual context in Retrieval Augmented Generation (RAG) systems.

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