Speaker
Description
In this talk, I demo Fichero. Fichero is a macOS app, a SwiftUI front end over a FastAPI Python engine, that puts machine-learning and AI workflows, using local and hosted models, in researchers' hands. Developed from a collaboration with Andy Janco (Princeton) and Ann Farnsworth-Alvear (UPenn), I demonstrate Fichero on the Circuit Court Archive of Istmina, Colombia. The archive is a British Library Endangered Archives Programme project consisting of 61,000 documents of Afro-Colombian legal records (1870–1930) from the Chocó, Colombia. These handwritten and typed documents are often faded and damaged, divided into 834 cases, and contain material on mining, land disputes, workplace injustices, and much more. Fichero allows researchers to import the archives as folders and images, use a Mac interface to enhance degraded images (deskew, denoise, and adjust contrast with OpenCV); transcribe handwritten and typed pages (OCR/HTR with Apple Vision, open-source, and frontier vision-language models); run (and visually edit) AI workflows powered by LangGraph and LangChain to break steps down to produce metadata; extract people, places, and subject–verb–object claims into a knowledge graph (named-entity and relation extraction via Apple Intelligence, spaCy, or local and hosted LLMs); write catalogue and finding-aid entries (using frontier models); search by meaning (vector embeddings in LanceDB alongside a DuckDB graph index); query the corpus through GraphRAG chat (retrieval over the graph plus vectors, with cited sources); compare model outputs; and publish a catalogue (an 11ty site with search). Fichero offers an easier to use tool so that skilled archivists, researchers, and others can work with archives, deploy workflows, and use AI and machine-learning tools and techniques.