Facilitating the creation, storage, retrieval, and curation of LLM prompts.
Project description
LLMPrompts Python package
In brief
This Python package provides data and functions for facilitating the creation, storage, retrieval, and curation of Large Language Models (LLM) prompts.
Installation
Install from GitHub
pip install -e git+https://github.com/antononcube/Python-packages.git#egg=LLMPrompts-antononcube\&subdirectory=LLMPrompts
From PyPi
pip install LLMPrompts
Basic usage examples
Synthesizing responses
Here is an example of prompt synthesis with the function llm_synthesize
using prompts from the package "LLMFunctions":
from LLMPrompts import *
print(
llm_synthesize([
llm_prompt("Yoda"),
"Hi! How old are you?",
llm_prompt("HaikuStyled")
]))
Young or old, matters not
Age is just a number, hmm
The Force is with me.
References
Articles
[AA1] Anton Antonov, "Workflows with LLM functions", (2023), RakuForPrediction at WordPress.
[SW1] Stephen Wolfram, "The New World of LLM Functions: Integrating LLM Technology into the Wolfram Language", (2023), Stephen Wolfram Writings.
[SW2] Stephen Wolfram, "Prompts for Work & Play: Launching the Wolfram Prompt Repository", (2023), Stephen Wolfram Writings.
Packages, paclets, repositories
[AAp1] Anton Antonov, LLM::Prompts Raku package, (2023), GitHub/antononcube.
[AAp2] Anton Antonov, LLM::Functions Raku package, (2023), GitHub/antononcube.
[AAp3] Anton Antonov, Jupyter::Chatbook Raku package, (2023), GitHub/antononcube.
[WRIr1] Wolfram Research, Inc., Wolfram Prompt Repository
Project details
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