Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/microsoft/markitdown

Python tool for converting files and office documents to Markdown.
https://github.com/microsoft/markitdown

autogen autogen-extension langchain markdown microsoft-office openai pdf

Last synced: 5 days ago
JSON representation

Python tool for converting files and office documents to Markdown.

Awesome Lists containing this project

README 

        
# MarkItDown



[![PyPI](https://img.shields.io/pypi/v/markitdown.svg)](https://pypi.org/project/markitdown/)

![PyPI - Downloads](https://img.shields.io/pypi/dd/markitdown)

[![Built by AutoGen Team](https://img.shields.io/badge/Built%20by-AutoGen%20Team-blue)](https://github.com/microsoft/autogen)



> [!TIP]

> MarkItDown now offers an MCP (Model Context Protocol) server for integration with LLM applications like Claude Desktop. See [markitdown-mcp](https://github.com/microsoft/markitdown/tree/main/packages/markitdown-mcp) for more information.



> [!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]'` to have backward-compatible behavior. 

> * convert\_stream() now requires a binary file-like object (e.g., a file opened in binary mode, or an io.BytesIO object). This is a breaking change from the previous version, where it previously also accepted text file-like objects, like io.StringIO.

> * 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](https://github.com/deanmalmgren/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:



- PDF

- PowerPoint

- 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

- EPubs

- ... and more!



## Why Markdown?



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.



## Installation



To install MarkItDown, use pip: `pip install 'markitdown[all]'`. Alternatively, you can install it from the source:



```bash

git clone [email protected]:microsoft/markitdown.git

cd markitdown

pip install -e packages/markitdown[all]

```



## Usage



### Command-Line



```bash

markitdown path-to-file.pdf > document.md

```



Or use `-o` to specify the output file:



```bash

markitdown path-to-file.pdf -o document.md

```



You can also pipe content:



```bash

cat path-to-file.pdf | markitdown

```



### Optional Dependencies

MarkItDown 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:



```bash

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



### Plugins



MarkItDown also supports 3rd-party plugins. Plugins are disabled by default. To list installed plugins:



```bash

markitdown --list-plugins

```



To enable plugins use:



```bash

markitdown --use-plugins path-to-file.pdf

```



To find available plugins, search GitHub for the hashtag `#markitdown-plugin`. To develop a plugin, see `packages/markitdown-sample-plugin`.



### Azure Document Intelligence



To use Microsoft Document Intelligence for conversion:



```bash

markitdown path-to-file.pdf -o document.md -d -e ""

```



More information about how to set up an Azure Document Intelligence Resource can be found [here](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/create-document-intelligence-resource?view=doc-intel-4.0.0)



### Python API



Basic usage in Python:



```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:



```python

from markitdown import MarkItDown



md = MarkItDown(docintel_endpoint="")

result = md.convert("test.pdf")

print(result.text_content)

```



To use Large Language Models for image descriptions, provide `llm_client` and `llm_model`:



```python

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



```sh

docker build -t markitdown:latest .

docker run --rm -i markitdown:latest < ~/your-file.pdf > output.md

```



## Contributing



This 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](https://opensource.microsoft.com/codeofconduct/).

For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or

contact [[email protected]](mailto:[email protected]) with any additional questions or comments.



### How to Contribute



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](https://github.com/microsoft/markitdown/issues) | [Issues open for contribution](https://github.com/microsoft/markitdown/issues?q=is%3Aissue+is%3Aopen+label%3A%22open+for+contribution%22) |

| **PRs**    | [All PRs](https://github.com/microsoft/markitdown/pulls)     | [PRs open for reviewing](https://github.com/microsoft/markitdown/pulls?q=is%3Apr+is%3Aopen+label%3A%22open+for+reviewing%22)              |






### Running Tests and Checks



- Navigate to the MarkItDown package:



  ```sh

  cd packages/markitdown

  ```



- Install `hatch` in your environment and run tests:



  ```sh

  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:



  ```sh

  # Reopen the project in Devcontainer and run:

  hatch test

  ```



- Run pre-commit checks before submitting a PR: `pre-commit run --all-files`



### Contributing 3rd-party Plugins



You can also contribute by creating and sharing 3rd party plugins. See `packages/markitdown-sample-plugin` for more details.



## Trademarks



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](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).

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.