DSL::Examples

Raku data package with examples of DSL commands translations to programming code. (

The DSL examples are suitable for LLM few-shot training. The sub llm-example-function provided by "LLM::Functions", [AAp2], can be effectively used to create translation functions utilizing those examples.

The utilization of such LLM-translation functions is exemplified below. Also in the presentation "Robust LLM pipelines (Mathematica, Python, Raku)":

Similar translations -- with much less computational resources -- are achieved with grammar-based DSL translators; see "DSL::Translators", [AAp1].

Installation

From Zef ecosystem:

zef install DSL::Examples;

From GitHub:

zef install https://github.com/antononcube/Raku-DSL-Examples.git

Usage examples

Get all examples:

use DSL::Examples;
use Data::TypeSystem;

dsl-examples()
    ==> deduce-type()

# Assoc(Atom((Str)), Tuple([Assoc(Atom((Str)), Tuple([Assoc(Atom((Str)), Atom((Str)), 27), Assoc(Atom((Str)), Atom((Str)), 20), Assoc(Atom((Str)), Atom((Str)), 20), Assoc(Atom((Str)), Atom((Str)), 17)]), 4), Assoc(Atom((Str)), Tuple([Assoc(Atom((Str)), Atom((Str)), 20), Assoc(Atom((Str)), Atom((Str)), 26), Assoc(Atom((Str)), Atom((Str)), 17)]), 3), Assoc(Atom((Str)), Tuple([Assoc(Atom((Str)), Atom((Str)), 15), Assoc(Atom((Str)), Atom((Str)), 20), Assoc(Atom((Str)), Atom((Str)), 23)]), 3), Assoc(Atom((Str)), Assoc(Atom((Str)), Atom((Str)), 20), 1)]), 4)

Tabulate all languages and available workflow examples:

use Data::Translators;
dsl-examples().map({ $_.key X $_.value.keys }).flat(1).map({ <language workflow> Z=> $_ })».Hash.sort.Array
==> to-dataset()
==> to-html(field-names => <language workflow>)

language	workflow
Python	LSAMon
Python	QRMon
Python	SMRMon
R	LSAMon
R	QRMon
R	SMRMon
Raku	SMRMon
WL	ClCon
WL	LSAMon
WL	QRMon
WL	SMRMon

Get the examples for Latent Semantic Analysis (LSA) Monadic pipeline segments in Python:

dsl-examples('Python', 'LSAMon')
    ==> deduce-type(:tally)

# Assoc(Atom((Str)), Atom((Str)), 15)

Make an LLM example function for translation of LSA workflow building commands:

use LLM::Functions;
my &llm-pipeline-segment = llm-example-function(dsl-examples()<WL><LSAMon>);

# LLM::Function(-> **@args, *%args { #`(Block|4301755332504) ... }, 'chatgpt')

Run the LLM function over a list of DSL commands:

my @commands = 
"use the dataset aAbstracts",
"make the document-term matrix without stemming",
"exract 40 topics using the method non-negative matrix factorization",
"show the topics";

@commands
.map({ .&llm-pipeline-segment })
.map({ .subst(/:i Output ':'?/):g })
.join("⟹\n")

# LSAMonUnit[aAbstracts]⟹
# LSAMonMakeDocumentTermMatrix["StemmingRules"->{},"StopWords"->Automatic]⟹
# LSAMonExtractTopics["NumberOfTopics"->40,Method->"NNMF"]⟹
# LSAMonEchoTopicsTable[]