MegaParse

MegaParse is an open-source document parser by the Quivr team that converts complex documents, PDFs, Word files, PowerPoint presentations, and Excel spreadsheets, into clean, LLM-ready markdown with particular focus on accurately parsing the content types that standard parsers handle poorly: tables, headers, footers, footnotes, nested lists, and multi-column layouts. The problem it addresses is a common RAG bottleneck: poor document parsing produces garbled input that degrades retrieval quality regardless of how good the vector search or LLM is downstream. MegaParse applies specialized parsing strategies per content type, pdfminer and unstructured.io for standard PDFs, vision-language models for scanned or complex layout documents, selecting the optimal approach for each document automatically. The Megaparse Vision module handles documents that require visual understanding (scanned contracts, forms, complex layouts) by processing pages as images through a vision model.

Output is clean markdown that preserves headings, table structures, and list hierarchies in a format that LLM chunkers can split meaningfully. Available as a Python library and API with a free hosted tier. Most tightly integrated with the Quivr knowledge base platform but usable standalone in any RAG pipeline. Most commonly deployed in document-heavy RAG applications in legal, finance, and enterprise settings where parsing accuracy on complex document layouts directly impacts answer quality.