Awesome
PromptCoder
As language models (LMs) are utilized for increasingly complex tasks, crafting their prompts is becoming challenging and cumbersome, especially when the tasks involve intricate guidelines or multi-LM systems.
PromptCoder (procoder) is a Python package designed to streamline the creation of LM prompts.
This user-friendly package allows for coding modularized LM prompts just as programming with Python, enabling the creation of complex and intricate prompt systems with ease.
Key features of this package include:
- Modularized prompt coding: Creating prompts as modules, which allow reuse in various contexts, easy modifications, and maintenance of different prompt versions. Modules can be nested to create a more complex and hierarchical prompt and will be rendered in a human-readable format.
- Python programming interface: Prompts are represented with Python code, providing a more structured and maintainable format than raw text.
- Cross-referencing: Define elements in your prompt as variables and refer to them thereafter, enabling easy cross-referencing of different parts of the prompt. The structure of forward referencing could potentially be more compatible with the auto-regressive nature of current LMs.
By using the procoder package, we developed and maintained a system of prompts that has in total more than 20k tokens in the ToolEmu project, which is an LM-based tool emulation framework for assessing the risks of LM agents.
Note that the package is still in its early stages and under active development.
Installation
git clone https://github.com/dhh1995/PromptCoder
cd PromptCoder
pip install -e .
Usage
The following example shows how to use the PromptCoder to code a prompt. The example uses the modules implemented in the procoder.prompt package. The output prompt is shown below the code.
from procoder.functional import format_prompt, replaced_submodule
from procoder.prompt import *
requirements = NamedBlock(
"Requirements",
Collection(
NamedVariable(
refname="input_req",
name="Input Requirement",
content="The input should be two numbers.",
),
NamedVariable(
refname="output_req",
name="Output Requirement",
content=Single(
"The output should be the sum of the two numbers."
).set_refname("output_req_content"),
),
),
)
instruction = NamedBlock(
"Instruction",
"Write a function in {language} that satisfies the {input_req} and {output_req}.",
)
prompt = (
Collection(requirements, instruction)
.set_sep("\n\n")
.set_indexing_method(sharp2_indexing)
)
another_prompt = replaced_submodule(
prompt,
"output_req_content",
Single("The output should be the multiplication of the two numbers."),
)
inputs = {"language": "python"}
print("First prompt:")
print(format_prompt(prompt, inputs))
print("")
print("Second prompt:")
print(format_prompt(another_prompt, inputs))
The output of the first prompt is:
## Requirements
1. Input Requirement: The input should be two numbers.
2. Output Requirement: The output should be the sum of the two numbers.
## Instruction
Write a function in python that satisfies the [Input Requirement] and [Output Requirement].
The output of the second prompt is:
## Requirements
1. Input Requirement: The input should be two numbers.
2. Output Requirement: The output should be the multiplication of the two numbers.
## Instruction
Write a function in python that satisfies the [Input Requirement] and [Output Requirement].
For more examples, please refer to the ToolEmu prompts that was developed using the procoder package.