prompt_peel 0.1.0

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Prompt-peel 🍌

A Python prompt design library heavily based on Priompt from Cursor/Anysphere. Build declarative prompts that automatically select the "optimal" prompt based on priority

What's wrong with prompt design today?

TODO

How does priompt/prompt-peel aim to fix it?

TODO

DSL

Top level

• system_prompt(*children): Self explanatory
• user_prompt(*children): Self explanatory
• assistant_prompt(*children): Self explanatory
• scope(*children): Create a new scope
• top_k(*children, top_k_value=N)
• empty(tokens=N): Empty cell used to to define how many tokens you require

Getting started

Using the library

poetry add prompt-peel

Contributing to the library

TODO
git checkout ____

• Look to the tests to get the best understanding of library features and practices. Ensure tests pass before PR-ing
• Before PRs, run linting via ./lint.sh

TODO

• Token counting logic
• Binary search for optimal priority
• Empty node to save space for N tokens
• Top K node to only take top k elements from a list
• Accept function calling
• Allow images in prompts

Caveats

• JSX is much more ergonomic than python strings. Automatic node splitting (when you embed elements amonst strings), automatic spacing on new line, automatic de-tabbing, etc. You must actively account for this in python (as seen in the examples)
• The aim is not to have feature parity with Priompt or even to follow their architecture in the long run. We think they've done a great job and currently provide the functionality we ourselves need,

Contributing

Contributions are welcome. Please open an issue or a pull request. Test cases are required.

Relevant reading

• Priompt: What this library is based off. A good read to understand their foundational principals.
• Writing DSLs: A short primer for what a DSL is and why you'd want to write one
• Build your own React: A good look into how the DSL of React/JSX is implemented and handled