{"repo":"explosion/spacy-layout","free":true,"listed":false,"github":"https://github.com/explosion/spacy-layout","clone":"git clone https://github.com/explosion/spacy-layout.git","description":"📚 Process PDFs, Word documents and more with spaCy","language":"Python","stars":912,"topics":["docx","generative-ai","natural-language-processing","nlp","pdf","pdf-converter","rag","spacy","document-layout","document-layout-analysis"],"license":"MIT","category":"ai-agents","readme_excerpt":"spaCy Layout: Process PDFs, Word documents and more with spaCy This plugin integrates with Docling to bring structured processing of PDFs , Word documents and other input formats to your spaCy pipeline. It outputs clean, structured data in a text-based format and creates spaCy's familiar Doc objects that let you access labelled text spans like sections or headings, and tables with their data converted to a pandas.DataFrame . This workflow makes it easy to apply powerful NLP techniques to your documents, including linguistic analysis, named entity recognition, text classification and more. It's also great for implementing chunking for RAG pipelines. 📖 Blog post: \"From PDFs to AI-ready structured data: a deep dive\" – A new modular workflow for converting PDFs and similar documents to structured data, featuring spacy-layout and Docling. 📝 Usage ⚠️ This package requires Python 3.10 or above. After initializing the spaCyLayout preprocessor with an nlp object for tokenization, you can call it on a document path to convert it to structured data. The resulting Doc object includes layout spans that map into the original raw text and expose various attributes, including the content type and layout features. If you need to process larger volumes of documents at scale, you can use the spaCyLayout.pipe method, which takes an iterable of paths or bytes instead and yields Doc objects: spaCy also allows you to call the nlp object on an already created Doc , so you can easily apply a pipeli","default_branch":null,"files":null,"tree":[],"storefront":"/r/explosion","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/explosion/spacy-layout/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}