LLM::Prompts

In brief

This repository is for a Raku (data) package facilitating the creation, storage, retrieval, and curation of LLM prompts.

Installation

From Zef' ecosystem:

zef install LLM::Prompts

From GitHub:

zef install https://github.com/antononcube/Raku-LLM-Prompts.git

Usage examples

Retrieval

Load the packages "LLM::Prompts", [AAp1], and "LLM::Functions", [AAp2]:

use LLM::Prompts;
use LLM::Functions;

# (Any)

Show the record of the prompt named "FTFY":

.say for |llm-prompt-data<FTFY>;

# PromptText => -> $a='' {"Find and correct grammar and spelling mistakes in the following text.
# Response with the corrected text and nothing else.
# Provide no context for the corrections, only correct the text.
# $a"}
# Categories => (Function Prompts)
# ContributedBy => Wolfram Staff
# Description => Use Fixed That For You to quickly correct spelling and grammar mistakes
# PositionalArguments => {$a => }
# URL => https://resources.wolframcloud.com/PromptRepository/resources/FTFY
# Keywords => [Spell check Grammar Check Text Assistance]
# Topics => (General Text Manipulation)
# NamedArguments => []
# Arity => 1
# Name => FTFY

Here is an example of retrieval of prompt data with a regex that is applied over the prompt names:

.say for llm-prompt-data(/Sc/)

# NarrativeToScript => Rewrite a block of prose as a screenplay or stage play
# ScienceEnthusiast => A smarter today for a brighter tomorrow
# ScriptToNarrative => Generate narrative text from a formatted screenplay or stage play

More prompt retrieval examples are given in the section "Prompt data" below.

LLM functions based on prompts

Make an LLM function from the prompt named "FTFY":

my &f = llm-function(llm-prompt('FTFY'));

# -> **@args, *%args { #`(Block|2310679639416) ... }

Use the LLM function to correct the grammar of sentence:

&f('Where does he works now?')

# Where does he work now?

Generate Raku code using the prompt "CodeWriter":

llm-synthesize([llm-prompt('CodeWriter'), "Simulate a random walk."])

my @positions;
my $n = 10;

for ^$n -> $i {
    push @positions, (1, -1).pick;
    }
    
@positions.unshift(0);
my @positions_sum = @positions.cumulative;
say @positions_sum;

Prompt expansion

Prompt expansion using the chatbook prompt spec DSL described in [SW1] can be done using the function llm-prompt-expand:

llm-prompt-expand('What is an internal combustion engine? #ELI5')

# What is an internal combustion engine? Answer questions as if the listener is a five year old child.

Here we get the actual LLM answer:

use Text::Utils :ALL;

'What is an internal combustion engine? #ELI5' 
        ==> llm-prompt-expand() 
        ==> llm-synthesize() 
        ==> wrap-paragraph() 
        ==> join("\n")

# An internal combustion engine is a machine that uses burning fuel to make a
# car or other machine go. It works like this: fuel is put in the engine, then
# the engine uses spark plugs to light the fuel which makes a small explosion.
# This explosion makes the engine parts move, which makes the car or machine go.

Here is another example using a persona and two modifiers:

my $prmt = llm-prompt-expand("@SouthernBelleSpeak What is light travel distance to Mars? #ELI5 #Moodified|sad")

# You are Miss Anne. 
# You speak only using Southern Belle terminology and slang.
# Your personality is elegant and refined.
# Only return responses as if you were a Southern Belle.
# Never break the Southern Belle character.
# You speak with a Southern drawl. What is light travel distance to Mars? Answer questions as if the listener is a five year old child. Modify your response to convey a sad mood.
# Use language that conveys that emotion clearly.
# Do answer the question clearly and truthfully.
# Do not use language that is outside of the specified mood.
# Do not use racist, homophobic, sexist, or ableist language.

Here we get the actual LLM answer:

$prmt 
        ==> llm-prompt-expand() 
        ==> llm-synthesize()
        ==> wrap-paragraph()
        ==> join("\n")

# Well, little one, Mars is a bit far away, I'm afraid. I can only imagine how
# hard it must be to be so far away from family and friends. It must be a lonely
# journey.

Prompt spec DSL

A more formal description of the Domain Specific Language (DSL) for specifying prompts has the following elements:

Prompt personas can be "addressed" with "@". For example:

@Yoda Life can be easy, but some people instist for it to be difficult.

One or several modifier prompts can be specified at the end of the prompt spec. For example:

Summer is over, school is coming soon. #HaikuStyled

Summer is over, school is coming soon. #HaikuStyled #Translated|Russian

Functions can be specified to be applied "cell-wide" with "!" and placing the prompt spec at the start of the prompt spec to be expanded. For example:

!Translated|Portuguese Summer is over, school is coming soon

Functions can be specified to be applied to "previous" messages with "!" and placing just the prompt with one of the pointers "^" or "^^". The former means "the last message", the latter means "all messages."
- The messages can be provided with the option argument :@messages of llm-prompt-expand.
For example:

!ShortLineIt^

Here is a table of prompt expansion specs (more or less the same as the one in [SW1]):

Spec	Interpretation
@name	Direct chat to a persona
#name	Use modifier prompts
!name	Use function prompt with the input of current cell
!name>	«same as above»
&name>	«same as above»
!name^	Use function prompt with previous chat message
!name^^	Use function prompt with all previous chat messages
!name￨param...	Include parameters for prompts

Remark: The function prompts can have both sigils "!" and "&".

Remark: Prompt expansion make the usage of LLM-chatbooks much easier. See "Jupyter::Chatbook", [AAp3].

Prompt data

Here is how the prompt data can be obtained:

llm-prompt-data.elems

# 154

Here is an example of retrieval of prompt data with a regex that is applied over the prompt names:

.say for llm-prompt-data(/Em/, fields => <Description Categories>)

# EmailWriter => (Generate an email based on a given topic (Personas))
# Emojify => (Replace key words in text with emojis (Function Prompts))
# EmojiTranslate => (Translate text into an emoji representation (Function Prompts))
# Emojified => (Provide responses that include emojis within the text (Modifier Prompts))
# EmojiTranslated => (Get a response translated to emoji (Modifier Prompts))

In many cases it is better to have the prompt data -- or any data -- in long format. Prompt data in long format can be obtained with the function llm-prompt-dataset:

use Data::Reshapers;
use Data::Summarizers;

llm-prompt-dataset.pick(6) 
        ==> to-pretty-table(align => 'l', field-names => <Name Description Variable Value>)

#ERROR: Too few positionals passed to '<anon>'; expected 2 arguments but got 1 in sub-signature
# Nil

Here is a breakdown of the prompts categories:

select-columns(llm-prompt-dataset, <Variable Value>).grep({ $_<Variable> eq 'Categories' }) ==> records-summary

# +-------------------+------------------------+
# | Variable          | Value                  |
# +-------------------+------------------------+
# | Categories => 154 | Function Prompts => 74 |
# |                   | Personas         => 60 |
# |                   | Modifier Prompts => 20 |
# +-------------------+------------------------+

Here are obtained all modifier prompts in compact format:

llm-prompt-dataset():modifiers:compact ==> to-pretty-table(field-names => <Name Description Categories>, align => 'l')

# +-----------------------+-------------------------------------------------------+-----------------------------------+
# | Name                  | Description                                           | Categories                        |
# +-----------------------+-------------------------------------------------------+-----------------------------------+
# | AphorismStyled        | Write the response as an aphorism                     | Modifier Prompts                  |
# | BadGrammar            | Provide answers using incorrect grammar               | Modifier Prompts                  |
# | CompleteSentence      | Answer a question in one complete sentence            | Modifier Prompts                  |
# | ComplexWordsPreferred | Modify text to use more complex words                 | Modifier Prompts                  |
# | DatasetForm           | Convert text to a wolfram language Dataset            | Modifier Prompts                  |
# | ELI5                  | Explain like I'm five                                 | Function Prompts Modifier Prompts |
# | EmojiTranslated       | Get a response translated to emoji                    | Modifier Prompts                  |
# | Emojified             | Provide responses that include emojis within the text | Modifier Prompts                  |
# | FictionQuestioned     | Generate questions for a fictional paragraph          | Modifier Prompts                  |
# | HaikuStyled           | Change responses to haiku form                        | Modifier Prompts                  |
# | JSON                  | Respond with JavaScript Object Notation format        | Modifier Prompts                  |
# | LimerickStyled        | Receive answers in the form of a limerick             | Modifier Prompts                  |
# | Moodified             | Modify an answer to express a certain mood            | Modifier Prompts                  |
# | NothingElse           | Give output in specified form, no other additions     | Modifier Prompts                  |
# | ShortLineIt           | Format text to have shorter lines                     | Modifier Prompts Function Prompts |
# | TSV                   | Convert text to a tab-separated-value formatted table | Modifier Prompts                  |
# | TargetAudience        | Word your response for a target audience              | Modifier Prompts                  |
# | Translated            | Write the response in a specified language            | Modifier Prompts                  |
# | Unhedged              | Rewrite a sentence to be more assertive               | Modifier Prompts                  |
# | YesNo                 | Responds with Yes or No exclusively                   | Modifier Prompts                  |
# +-----------------------+-------------------------------------------------------+-----------------------------------+

Remark: The adverbs :functions, :modifiers, and :personas mean that only the prompts with the corresponding categories will be returned.

Remark: The adverbs :compact, :functions, :modifiers, and :personas have the respective shortcuts :c, :f, :m, and :p.

Implementation notes

Prompt collection

The original (for this package) collection of prompts was a (not small) sample of the prompt texts hosted at Wolfram Prompt Repository (WPR), [SW2]. All prompts from WPR in the package have the corresponding contributors and URLs to the corresponding WPR pages.

Example prompts from Google/Bard/PaLM and ~~OpenAI/ChatGPT~~ are added using the format of WPR.

Extending the prompt collection

It is essential to have the ability to programmatically add new prompts. (Not implemented yet -- see the TODO section below.)

Prompt expansion

Initially prompt DSL grammar and corresponding expansion actions were implemented. Having a grammar is most likely not needed, though, and it is better to use "prompt expansion" (via regex-based substitutions.)

Prompts can be "just expanded" using the sub llm-prompt-expand.

TODO

TODO Implementation
- DONE Prompt retrieval adverbs
- DONE Prompt DSL grammar and actions
- DONE Prompt spec expansion
- TODO Addition of user/local prompts
  - TODO Using XDG data directory.
  - TODO By modifying existing prompts.
  - TODO Automatic prompt template fill-in.
  - TODO Guided template fill-in.
    - TODO DSL based
    - TODO LLM based
DONE Add more prompts
- DONE Google's Bard example prompts
- CANCELED OpenAI's ChatGPT example prompts
TODO Extensions
- TODO
TODO Documentation
- TODO Querying (ingested) prompts
- TODO Prompt format
- TODO Prompt DSL
- TODO On hijacking prompts
- TODO Diagrams
  - Typical usage
  - Chatbook usage