用ColPali实现10万张古地图的多模态检索与跨库搜索。
Retrieval-Augmented Search for Large-Scale Map Collections with ColPali
- 基于ColPali的多模态检索，支持图文混合查询。
- 可对101,233张国会图书馆古地图进行快速搜索与摘要。
- 适合档案馆员、研究者及数字人文领域用户使用。
多模态方法在图书馆、档案馆和博物馆的数字藏品检索与导航中展现出巨大潜力。本文提出map-RAS系统，用于历史地图的检索增强搜索。我们不仅介绍了该框架，还详细展示了公开可用的演示系统，支持对美国国会图书馆持有的101,233张地图图像进行搜索。用户可通过ColPali进行多模态查询，利用Llama 3.2总结搜索结果，并上传自有地图集合实现跨库检索。文中阐述了该系统在档案管理、策展及数字人文学科中的潜在应用场景，以及未来在机器学习与数字人文领域的拓展方向。演示地址：http://www.mapras.com。
原文摘要 · Abstract (English)
Multimodal approaches have shown great promise for searching and navigating digital collections held by libraries, archives, and museums. In this paper, we introduce map-RAS: a retrieval-augmented search system for historic maps. In addition to introducing our framework, we detail our publicly-hosted demo for searching 101,233 map images held by the Library of Congress. With our system, users can multimodally query the map collection via ColPali, summarize search results using Llama 3.2, and upload their own collections to perform inter-collection search. We articulate potential use cases for archivists, curators, and end-users, as well as future work with our system in both machine learning and the digital humanities. Our demo can be viewed at: http://www.mapras.com.
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