PACman

Prompts Are Code, man

A package to make prompts first-party objects in Python

Examples

example.yaml

Prompts:
  thinker_prompt:
    prompt: |- # this is a multiline way of writing strings 
      You are a large language model.
      Context:\ {context}
      {question}
      A:\ Let's think step by step
    inputs: [context, question]
    config:
      model: "text-davinci-003" 
      max_tokens: 100
      temperature: 0.8
      top_p: 0.9
  final_prompt:
      prompt: |- # this is a multiline way of writing strings 
        You are a large language model.
        {question}
        Reasoning:\ {reasoning}
        A:\
      inputs: [question, reasoning]
      config:
        model: "text-davinci-003" 
        max_tokens: 100
        temperature: 0.8
        top_p: 0.9

example.py

import pacman.pacman as pacman

#Initialize prompts
prompts = pacman.load('./example.yaml')

thinker_prompt = prompts['thinker_prompt']
final_prompt = prompts['final_prompt']

def QAchain(context, question):
    input = {'context': context, 'question': question}
    reasoning = thinker_prompt(input, debug=True)
    
    input = {'question': question, 'reasoning': reasoning}
    out = final_prompt(input, debug=True)
    return out

QAchain('I have a sister named Anna, Anna has a sister named Elsa', 'How many sisters do I have?')

Why I made this

A lot of prompt-heavy code is hard to grasp due to mixed use of large multi-line strings and orchestrating code. Some prompt libraries automate portions of these away, but we lose flexibility in prompting.

By extrapolating the prompt config to a yaml file and generating pure Python functions from it, we can focus our business code on orchestrating the input/outputs of prompt functions.

Another benefit of moving prompts to YAML files is that it's easy to move prompts between languages, and to track prompt changes via git.

Also, procrastination

Install

pip install pacman-prompts

Roadmap:

[x] publish package [] type checking on prompt inputs