Parse files for optimal RAG
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Updated
Dec 18, 2024 - Python
Parse files for optimal RAG
E2M converts various file types (doc, docx, epub, html, htm, url, pdf, ppt, pptx, mp3, m4a) into Markdown. It’s easy to install, with dedicated parsers and converters, supporting custom configs. E2M offers an all-in-one, flexible, and open-source solution.
Easily deployable and scalable backend server that efficiently converts various document formats (pdf, docx, pptx, html, images, etc) into Markdown. With support for both CPU and GPU processing, it is Ideal for large-scale workflows, it offers text/table extraction, OCR, and batch processing with sync/async endpoints.
Parse PDFs into markdown using Vision LLMs
Conversion of PDF documents to structured Markdown, optimized for Retrieval Augmented Generation (RAG) and other NLP tasks. Extract text, tables, and images with preserved formatting for enhanced information retrieval and processing.
Turn a supported list of filetypes (e.g. .docx) into a markdown structured text file. Also optionally defangs indicators and extract texts from images. Built for threat intel use-cases.
Quick way to convert files (PDF, DOCX, HTML, PPTX, Images) to (MD, JSON, YAML) using Docling and Streamlit
RAG-Ingest: A tool for converting PDFs to markdown and indexing them for enhanced Retrieval Augmented Generation (RAG) capabilities.
DocuParse is a high-performance tool for converting PDF documents into clean, structured Markdown files. Designed for speed and accuracy, it extracts and formats content while minimizing errors like hallucinations and repetitions.
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