MegaParse - Your Parser for every type of documents

MegaParse is a powerful and versatile parser that can handle various types of documents with ease. Whether you're dealing with text, PDFs, Powerpoint presentations, Word documents MegaParse has got you covered. Focus on having no information loss during parsing.

Key Features 🎯

• Versatile Parser: MegaParse is a powerful and versatile parser that can handle various types of documents with ease.
• No Information Loss: Focus on having no information loss during parsing.
• Fast and Efficient: Designed with speed and efficiency at its core.
• Wide File Compatibility: Supports Text, PDF, Powerpoint presentations, Excel, CSV, Word documents.
• Open Source: Freedom is beautiful, and so is MegaParse. Open source and free to use.

Support

• Files: ✅ PDF ✅ Powerpoint ✅ Word
• Content: ✅ Tables ✅ TOC ✅ Headers ✅ Footers ✅ Images

Example

https://github.com/QuivrHQ/MegaParse/assets/19614572/1b4cdb73-8dc2-44ef-b8b4-a7509bc8d4f3

Installation

required python version >= 3.11

pip install megaparse

Usage

1. Add your OpenAI or Anthropic API key to the .env file

2. Install poppler on your computer (images and PDFs)

3. Install tesseract on your computer (images and PDFs)

4. If you have a mac, you also need to install libmagic brew install libmagic

Use MegaParse as it is :

from megaparse import MegaParse
from langchain_openai import ChatOpenAI

megaparse = MegaParse()
response = megaparse.load("./test.pdf")
print(response)

Use MegaParse Vision

from megaparse.parser.megaparse_vision import MegaParseVision

model = ChatOpenAI(model="gpt-4o", api_key=os.getenv("OPENAI_API_KEY"))  # type: ignore
parser = MegaParseVision(model=model)
response = parser.convert("./test.pdf")
print(response)

Note: The model supported by MegaParse Vision are the multimodal ones such as claude 3.5, claude 4, gpt-4o and gpt-4.

Use as an API

There is a MakeFile for you, simply use : make dev at the root of the project and you are good to go.

See localhost:8000/docs for more info on the different endpoints !

BenchMark

Parser	similarity_ratio
megaparse_vision	0.87
unstructured_with_check_table	0.77
unstructured	0.59
llama_parser	0.33

Higher the better

Note: Want to evaluate and compare your Megaparse module with ours ? Please add your config in evaluations/script.py and then run python evaluations/script.py. If it is better, do a PR, I mean, let's go higher together .

In Construction 🚧

• Improve table checker
• Create Checkers to add modular postprocessing ⚙️
• Add Structured output, let's get computer talking 🤖

Star History