原文链接

🚀 E2M: Everything to Markdown

Everything to Markdown

E2M is a Python library that can parse and convert various file types into Markdown format. By utilizing a parser-converter architecture, it supports the conversion of multiple file formats, including doc, docx, epub, html, htm, url, pdf, ppt, pptx, mp3, and m4a.

✨The ultimate goal of the E2M project is to provide high-quality data for Retrieval-Augmented Generation (RAG) and model training or fine-tuning.

Core Architecture of the Project:

• Parser: Responsible for parsing various file types into text or image data.
• Converter: Responsible for converting text or image data into Markdown format.

Generally, for any type of file, the parser is run first to extract internal data such as text and images. Then, the converter is used to transform this data into Markdown format.

📹 Video Introduction

📂 All Converters and Parsers

Supported Models

1. Litellm: https://docs.litellm.ai/docs/providers/
2. Zhipuai: https://open.bigmodel.cn/dev/howuse/model

📦 Installation

Create Environment:

conda create -n e2m python=3.10
conda activate e2m

Update pip:

pip install --upgrade pip

Install E2M using pip:

# Option 1: Install via git, most recommended
pip install git+https://github.com/wisupai/e2m.git --index-url https://pypi.org/simple
# Option 2: Install via pip
pip install --upgrade wisup_e2m
# Option 3: Manual installation
git clone https://github.com/wisupai/e2m.git
cd e2m
pip install poetry
poetry build
pip install dist/wisup_e2m-0.1.63-py3-none-any.whl

Start API Service

gunicorn wisup_e2m.api.main:app --workers 4 --worker-class uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000

API Documentation:

• http://127.0.0.1:8000/docs

⚡️ Parser Quick Start

Here’s simple examples demonstrating how to use E2M Parsers:

📄 Pdf Parser

from wisup_e2m import PdfParser

pdf_path = "./test.pdf"
parser = PdfParser(engine="marker") # pdf engines: marker, unstructured, surya_layout
pdf_data = parser.parse(pdf_path)
print(pdf_data.text)

📝 Doc Parser

from wisup_e2m import DocParser

doc_path = "./test.doc"
parser = DocParser(engine="pandoc") # doc engines: pandoc, xml
doc_data = parser.parse(doc_path)
print(doc_data.text)

📜 Docx Parser

from wisup_e2m import DocxParser

docx_path = "./test.docx"
parser = DocxParser(engine="pandoc") # docx engines: pandoc, xml
docx_data = parser.parse(docx_path)
print(docx_data.text)

📚 Epub Parser

from wisup_e2m import EpubParser

epub_path = "./test.epub"
parser = EpubParser(engine="unstructured") # epub engines: unstructured
epub_data = parser.parse(epub_path)
print(epub_data.text)

🌐 Html Parser

from wisup_e2m import HtmlParser

html_path = "./test.html"
parser = HtmlParser(engine="unstructured") # html engines: unstructured
html_data = parser.parse(html_path)
print(html_data.text)

🔗 Url Parser

from wisup_e2m import UrlParser

url = "https://www.example.com"
parser = UrlParser(engine="jina") # url engines: jina, firecrawl, unstructured
url_data = parser.parse(url)
print(url_data.text)

🖼️ Ppt Parser

from wisup_e2m import PptParser

ppt_path = "./test.ppt"
parser = PptParser(engine="unstructured") # ppt engines: unstructured
ppt_data = parser.parse(ppt_path)
print(ppt_data.text)

🖼️ Pptx Parser

from wisup_e2m import PptxParser

pptx_path = "./test.pptx"
parser = PptxParser(engine="unstructured") # pptx engines: unstructured
pptx_data = parser.parse(pptx_path)
print(pptx_data.text)

🎤 Voice Parser

from wisup_e2m import VoiceParser

voice_path = "./test.mp3"
parser = VoiceParser(
  engine="openai_whisper_local", # voice engines: openai_whisper_api, openai_whisper_local
  model="large" # available models: https://github.com/openai/whisper#available-models-and-languages
  )

voice_data = parser.parse(voice_path)
print(voice_data.text)

🔄 Converter Quick Start

Here’s simple examples demonstrating how to use E2M Converters:

📝 Text Converter

from wisup_e2m import TextConverter

text = "Parsed text data from any parser"
converter = TextConverter(
  engine="litellm", # text engines: litellm
  model="deepseek/deepseek-chat",
  api_key="your api key",
  base_url="your base url"
  )
text_data = converter.convert(text)
print(text_data)

🖼️ Image Converter

from wisup_e2m import ImageConverter

images = ["./test1.png", "./test2.png"]
converter = ImageConverter(
  engine="litellm", # image engines: litellm
  model="gpt-4o",
  api_key="your api key",
  base_url="your base url"
  )
image_data = converter.convert(image_path)
print(image_data)

🆙 Next Level

🛠️ E2MParser

E2MParser is an integrated parser that supports multiple file types. It can be used to parse a wide range of file types into Markdown format.

from wisup_e2m import E2MParser

# Initialize the parser with your configuration file
ep = E2MParser.from_config("config.yaml")

# Parse the desired file
data = ep.parse(file_name="/path/to/file.pdf")

# Print the parsed data as a dictionary
print(data.to_dict())

🛠️ E2MConverter

E2MConverter is an integrated converter that supports text and image conversion. It can be used to convert text and images into Markdown format.

from wisup_e2m import E2MConverter

ec = E2MConverter.from_config("./config.yaml")

text = "Parsed text data from any parser"

ec.convert(text=text)

images = ["test.jpg", "test.png"]
ec.convert(images=images)

You can use a config.yaml file to specify the parsers and converters you want to use. Here is an example of a config.yaml file:

parsers:
    doc_parser:
        engine: "pandoc"
        langs: ["en", "zh"]
    docx_parser:
        engine: "pandoc"
        langs: ["en", "zh"]
    epub_parser:
        engine: "unstructured"
        langs: ["en", "zh"]
    html_parser:
        engine: "unstructured"
        langs: ["en", "zh"]
    url_parser:
        engine: "jina"
        langs: ["en", "zh"]
    pdf_parser:
        engine: "marker"
        langs: ["en", "zh"]
    pptx_parser:
        engine: "unstructured"
        langs: ["en", "zh"]
    voice_parser:
        # option 1: use openai whisper api
        # engine: "openai_whisper_api"
        # api_base: "https://api.openai.com/v1"
        # api_key: "your_api_key"
        # model: "whisper"

        # option 2: use local whisper model
        engine: "openai_whisper_local"
        model: "large" # available models: https://github.com/openai/whisper#available-models-and-languages

converters:
    text_converter:
        engine: "litellm"
        model: "deepseek/deepseek-chat"
        api_key: "your_api_key"
        # base_url: ""
    image_converter:
        engine: "litellm"
        model: "gpt-4o-mini"
        api_key: "your_api_key"
        # base_url: ""

❓ Q&A

FAQ Document

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

📧 Contact

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For any questions or inquiries, please open an issue on GitHub or contact us at [email protected].

Contact for business cooperation: [email protected]

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🌟 Contributing