ColPali, the core innovation of Morphik Core, addresses the key pain point that traditional RAG systems are unable to effectively handle mixed graphical and textual content. This technology enables the system to simultaneously understand the intrinsic associations between textual descriptions and visual content through a unified embedded spatial representation. In terms of technical implementation, ColPali builds a cross-modal attention mechanism that allows the system to localize to the relevant diagram or image area in the document when the user query contains a description of a visual element.
Typical application scenarios include retrieving a specific data visualization chart from an annual financial report, or finding a page containing a specific experimental setup in a scientific paper. Test data shows that the ColPali technology achieves an accuracy improvement of up to 47% in mixed graphical and textual retrieval tasks compared to solutions that process text or images separately.
Developers can activate this advanced feature by simply setting the use_colpali parameter during data ingestion and retrieval. This technology dramatically lowers the threshold for developing multimodal AI applications, enabling ordinary enterprises to build intelligent systems with visual understanding capabilities.
This answer comes from the articleMorphik Core: an open source RAG platform for processing multimodal dataThe
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