Updated all instances of mlm_client and mlm_model to llm_client and llm_model in the readme. The previous terms (mlm_client and mlm_model) are incorrect in the context of configuring Large Language Models (LLMs), as "MLM" typically refers to Masked Language Models, which is unrelated to the intended functionality. This change aligns the documentation with standard naming conventions for LLM configuration parameters and improves clarity for users integrating with LLMs like OpenAI's GPT models.
MarkItDown
The MarkItDown library is a utility tool for converting various files to Markdown (e.g., for indexing, text analysis, etc.)
It presently supports:
- PDF (.pdf)
- PowerPoint (.pptx)
- Word (.docx)
- Excel (.xlsx)
- Images (EXIF metadata, and OCR)
- Audio (EXIF metadata, and speech transcription)
- HTML (special handling of Wikipedia, etc.)
- Various other text-based formats (csv, json, xml, etc.)
Installation
You can install markitdown using pip:
pip install markitdown
or from the source
pip install -e .
Usage
The API is simple:
from markitdown import MarkItDown
markitdown = MarkItDown()
result = markitdown.convert("test.xlsx")
print(result.text_content)
You can also configure markitdown to use Large Language Models to describe images. To do so you must provide llm_client and llm_model parameters to MarkItDown object, according to your specific client.
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)
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. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
Running Tests
To run the tests for this project, use the following command:
hatch shell
hatch test
Running Pre-commit Checks
pre-commit run --all-files
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. 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.