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.
| Parser | ||
|---|---|---|
| Parser Type | Engine | Supported File Type |
| PdfParser | surya_layout, marker, unstructured | |
| DocParser | pandoc, xml | doc |
| DocxParser | pandoc, xml | docx |
| PptParser | unstructured | ppt |
| PptxParser | unstructured | pptx |
| UrlParser | unstructured, jina, firecrawl | url |
| EpubParser | unstructured | epub |
| HtmlParser | unstructured | html, htm |
| VoiceParser | openai_whisper_api, openai_whisper_local, SpeechRecognition | mp3, m4a |
| Converter | ||
|---|---|---|
| Converter Type | Engine | Strategy |
| ImageConverter | litellm, zhipuai (Not Well in Image Recognition, Not Recommended) | default |
| TextConverter | litellm, zhipuai | default |
Create Environment:
conda create -n e2m python=3.10
conda activate e2mUpdate pip:
pip install --upgrade pipInstall 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.whlgunicorn wisup_e2m.api.main:app --workers 4 --worker-class uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000API Documentation:
Here's simple examples demonstrating how to use E2M Parsers:
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)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)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)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)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)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)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)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)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)Here's simple examples demonstrating how to use E2M Converters:
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)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)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 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: ""This project is licensed under the MIT License. See the LICENSE file for details.
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