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)":

• Short introduction
• Detailed explanations

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

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Installation

From Zef ecosystem:

zef install DSL::Examples;

From GitHub:

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

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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)), 23), Assoc(Atom((Str)), Atom((Str)), 15), Assoc(Atom((Str)), Atom((Str)), 20), Assoc(Atom((Str)), Atom((Str)), 33)]), 4), Assoc(Atom((Str)), Tuple([Assoc(Atom((Str)), Atom((Str)), 20), Assoc(Atom((Str)), Atom((Str)), 26), Assoc(Atom((Str)), Atom((Str)), 17), Assoc(Atom((Str)), Atom((Str)), 10)]), 4), Assoc(Atom((Str)), Tuple([Assoc(Atom((Str)), Atom((Str)), 20), Assoc(Atom((Str)), Atom((Str)), 20), Assoc(Atom((Str)), Atom((Str)), 14), Assoc(Atom((Str)), Atom((Str)), 6), Assoc(Atom((Str)), Atom((Str)), 17), Assoc(Atom((Str)), Atom((Str)), 27), Assoc(Atom((Str)), Atom((Str)), 32)]), 7), Assoc(Atom((Str)), Tuple([Assoc(Atom((Str)), Atom((Str)), 20), Assoc(Atom((Str)), Atom((Str)), 6), Assoc(Atom((Str)), Atom((Str)), 10)]), 3)]), 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
Python	pandas
R	DataReshaping
R	LSAMon
R	QRMon
R	SMRMon
Raku	DataReshaping
Raku	SMRMon
Raku	TriesWithFrequencies
WL	ClCon
WL	DataReshaping
WL	LSAMon
WL	QRMon
WL	SMRMon
WL	Tabular
WL	TriesWithFrequencies

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|3817446106192) ... }, '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[]

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CLI

The package provides the Command Line Interface (CLI) script dsl-examples. Here is its usage message:

dsl-examples --help

# Usage:
#   dsl-examples [<lang>] [<workflow>] [-f|--format=<Str>] -- Give DSL examples for specified language and workflow.
#   dsl-examples [-l|--lang=<Str>] [-w|--workflow=<Str>] [-f|--format=<Str>]
#   
#     [<lang>]               Language. [default: 'Whatever']
#     [<workflow>]           Workflow. [default: 'Whatever']
#     -f|--format=<Str>      Format of the result, one of "json" or "raku". [default: 'json']
#     -l|--lang=<Str>        Language. [default: 'Whatever']
#     -w|--workflow=<Str>    Workflow. [default: 'Whatever']

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References

Packages

[AAp1] Anton Antonov, DSL::Translators Raku package, (2020-2024), GitHub/antononcube.

[AAp2] Anton Antonov, LLM::Functions Raku package, (2023-2024), GitHub/antononcube.

[AAp3] Anton Antonov, LLM::Prompts Raku package, (2023-2024), GitHub/antononcube.

Videos

[AAv1] Anton Antonov, "Robust LLM pipelines (Mathematica, Python, Raku)", (2024), YouTube/AAA4prediction.