Important
Breaking changes between 0.0.1 to 0.1.0:
- Dependencies are now organized into optional feature-groups (further details below). Use
pip install 'markitdown[all]~=0.1.0a1'to have backward-compatible behavior. - The DocumentConverter class interface has changed to read from file-like streams rather than file paths. No temporary files are created anymore. If you are the maintainer of a plugin, or custom DocumentConverter, you likely need to update your code. Otherwise, if only using the MarkItDown class or CLI (as in these examples), you should not need to change anything.
MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. To this end, it is most comparable to textract, but with a focus on preserving important document structure and content as Markdown (including: headings, lists, tables, links, etc.) While the output is often reasonably presentable and human-friendly, it is meant to be consumed by text analysis tools -- and may not be the best option for high-fidelity document conversions for human consumption.
At present, MarkItDown supports:
- PowerPoint (reading in top-to-bottom, left-to-right order)
- Word
- Excel
- Images (EXIF metadata and OCR)
- Audio (EXIF metadata and speech transcription)
- HTML
- Text-based formats (CSV, JSON, XML)
- ZIP files (iterates over contents)
- Youtube URLs
- ... and more!
Markdown is extremely close to plain text, with minimal markup or formatting, but still provides a way to represent important document structure. Mainstream LLMs, such as OpenAI's GPT-4o, natively "speak" Markdown, and often incorporate Markdown into their responses unprompted. This suggests that they have been trained on vast amounts of Markdown-formatted text, and understand it well. As a side benefit, Markdown conventions are also highly token-efficient.
To install MarkItDown, use pip: pip install 'markitdown[all]~=0.1.0a1'. Alternatively, you can install it from the source:
git clone [email protected]:microsoft/markitdown.git
cd markitdown
pip install -e packages/markitdown[all]markitdown path-to-file.pdf > document.mdOr use -o to specify the output file:
markitdown path-to-file.pdf -o document.mdYou can also pipe content:
cat path-to-file.pdf | markitdownMarkItDown has optional dependencies for activating various file formats. Earlier in this document, we installed all optional dependencies with the [all] option. However, you can also install them individually for more control. For example:
pip install markitdown[pdf, docx, pptx]will install only the dependencies for PDF, DOCX, and PPTX files.
At the moment, the following optional dependencies are available:
[all]Installs all optional dependencies[pptx]Installs dependencies for PowerPoint files[docx]Installs dependencies for Word files[xlsx]Installs dependencies for Excel files[xls]Installs dependencies for older Excel files[pdf]Installs dependencies for PDF files[outlook]Installs dependencies for Outlook messages[az-doc-intel]Installs dependencies for Azure Document Intelligence[audio-transcription]Installs dependencies for audio transcription of wav and mp3 files[youtube-transcription]Installs dependencies for fetching YouTube video transcription
MarkItDown also supports 3rd-party plugins. Plugins are disabled by default. To list installed plugins:
markitdown --list-pluginsTo enable plugins use:
markitdown --use-plugins path-to-file.pdfTo find available plugins, search GitHub for the hashtag #markitdown-plugin. To develop a plugin, see packages/markitdown-sample-plugin.
To use Microsoft Document Intelligence for conversion:
markitdown path-to-file.pdf -o document.md -d -e "<document_intelligence_endpoint>"More information about how to set up an Azure Document Intelligence Resource can be found here
Basic usage in Python:
from markitdown import MarkItDown
md = MarkItDown(enable_plugins=False) # Set to True to enable plugins
result = md.convert("test.xlsx")
print(result.text_content)Document Intelligence conversion in Python:
from markitdown import MarkItDown
md = MarkItDown(docintel_endpoint="<document_intelligence_endpoint>")
result = md.convert("test.pdf")
print(result.text_content)To use Large Language Models for image descriptions, provide llm_client and llm_model:
from markitdown import MarkItDown
from openai import OpenAI
client = OpenAI()
md = MarkItDown(llm_client=client, llm_model="gpt-4o")
result = md.convert("example.jpg")
print(result.text_content)docker build -t markitdown:latest .
docker run --rm -i markitdown:latest < ~/your-file.pdf > output.mdThis project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
You can help by looking at issues or helping review PRs. Any issue or PR is welcome, but we have also marked some as 'open for contribution' and 'open for reviewing' to help facilitate community contributions. These are ofcourse just suggestions and you are welcome to contribute in any way you like.
| All | Especially Needs Help from Community | |
|---|---|---|
| Issues | All Issues | Issues open for contribution |
| PRs | All PRs | PRs open for reviewing |
-
Navigate to the MarkItDown package:
cd packages/markitdown -
Install
hatchin your environment and run tests:pip install hatch # Other ways of installing hatch: https://hatch.pypa.io/dev/install/ hatch shell hatch test
(Alternative) Use the Devcontainer which has all the dependencies installed:
# Reopen the project in Devcontainer and run: hatch test
-
Run pre-commit checks before submitting a PR:
pre-commit run --all-files
You can also contribute by creating and sharing 3rd party plugins. See packages/markitdown-sample-plugin for more details.
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
