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)]), 2), Assoc(Atom((Str)), Tuple([Assoc(Atom((Str)), Atom((Str)), 20), Assoc(Atom((Str)), Atom((Str)), 17), Assoc(Atom((Str)), Atom((Str)), 27)]), 3)]), 2)

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>);

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 })
.join("⟹\n")

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

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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.