
mark
Interact with GPT using markdown and images
Stars: 55

Mark is a CLI tool that allows users to interact with large language models (LLMs) using Markdown format. It enables users to seamlessly integrate GPT responses into Markdown files, supports image recognition, scraping of local and remote links, and image generation. Mark focuses on using Markdown as both a prompt and response medium for LLMs, offering a unique and flexible way to interact with language models for various use cases in development and documentation processes.
README:
Mark lets you seamlessly use markdown, images and links to interact with LLMs
"Markdown is still the best format for feeding into language models"
- Simon Willison (co-creator of Django)
Why Markdown + LLMs? For a full rundown of the benefits and use cases, check out Introducing 'Mark', a Markdown CLI tool for LLMs
- Interact with LLMs using Markdown
- Visual recognition of markdown image references via multi-modal LLMs
- Local and remote links are scraped for context
- LLM responses appended directly into Markdown files
-
stdin
andstdout
support for piping - Support for Image Generation
Describe  to me in one short sentence.
# GPT Response (model: gpt-4o-2024-05-13, system: default)
A Bengal tiger is walking on a sandy path with greenery in the background.
# User Response
List each section of content on [this page](https://en.wikipedia.org/wiki/Bengal_tiger)
# GPT Response (model: gpt-4o-2024-05-13, system: default)
## Sections of Content on the Bengal Tiger Wikipedia Page
1. **Taxonomy**
- Genetic ancestry
2. **Characteristics**
- Body weight and size
3. **Distribution and habitat**
- India
- Bangladesh
...
This allows you to carry on a conversation directly in the markdown file - or simply integrate the response back into the document you're working on. This is a powerful, flexible and natural way to interact with LLMs that unlocks countless use cases. mark
's focus on explicitly using Markdown as both a prompt and response medium for LLMs presents a unique approach. By blending interactive, version-controlled Markdown documents with LLM responses in a CLI tool offers an innovative workflow for those looking to integrate LLM interactions into their development and documentation processes.
pip install mark
By default, mark
will read a markdown file, extract any context references, and send them to the LLM. The responses are then appended to the markdown file.
mark path/to/markdown.md
Requires an OpenAI API key in the OPENAI_API_KEY
environment variable
Also supports stdin
with stdout
for piping LLM responses into other tools
cat path/to/markdown.md | mark
# LLM response....
You can specify a different LLM model to use with the --model
(or -m
) flag
mark path/to/markdown.md --model gpt-4o-2024-05-13
The system prompts folder is located at ~/.mark/system_prompts
and it includes a default.md
prompt. You can add any additional system prompts you'd like to use in this folder and use them with the --system
(or -s
) flag.
# ~/.mark/system_prompts/custom.md
mark path/to/markdown.md --system custom
If you want to use a different LLM API endpoint that is fully compatible with the OpenAI API, set the OPENAI_API_BASE_URL
environment variable to that endpoint value. This should enable you to use OpenAI proxy services like credal.ai, or other LLMs that are compatible with the OpenAI SDK.
To generate an image based on the input just add the --generate-image
(or -i
) flag to the command
mark path/to/markdown.md --generate-image
This will generate an image using the 'dall-e-3' model and append it to the markdown file.
poetry install
poetry run mark path/to/markdown.md
poetry run python -m pytest
poetry run autopep8 --in-place --aggressive --aggressive --recursive .
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