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natural language processing template engine presentation given at wtc 2022 2023 youtube wolfram february 21 2026 anton antonov antonov ai artificial intelligence chatgpt dsl llm ollama openai python qas technology workflows 1 comment dsl examples with langchain introduction this blog post notebook demonstrates the usage of the python data package dslexamples aap1 with examples of domain specific language dsl commands translations to programming code the provided dsl examples are suitable for llm few shot training langchain can be used to create translation pipelines utilizing those examples the utilization of such llm translation pipelines is exemplified below the python package closely follows the raku package dsl examples aap2 and wolfram language paclet dslexamples aap3 and has or should have the same dsl examples data remark similar translations with much less computational resources are achieved with grammar based dsl translators see dsl translators aap4 setup load the packages used below from dslexamples import dsl_examples dsl_workflow_separators from langchain_core output_parsers import stroutputparser from langchain_ollama import chatollama import pandas as pd import os retrieval get all examples and retrieve specific language workflow slices all_examples dsl_examples python_lsa dsl_examples python lsamon separators dsl_workflow_separators wl lsamon list all_examples keys list python_lsa keys 5 wl python r raku load the package use the documents adocs use dftemp make the document term matrix make the document term matrix with automatic stop words tabulate languages and workflows rows language lang workflow workflow for lang workflows in all_examples items for workflow in workflows keys pd dataframe rows sort_values language workflow reset_index drop true 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 python lsa examples pd dataframe command k code v for k v in python_lsa items command code load the package from latentsemanticanalyzer import use the documents adocs latentsemanticanalyzer adocs use dftemp latentsemanticanalyzer dftemp make the document term matrix make_document_term_matrix make the document term matrix with automatic s make_document_term_matrix stemming_rules none make the document term matrix without stemming make_document_term_matrix stemming_rules false apply term weight functions apply_term_weight_functions apply term weight functions global idf local apply_term_weight_functions global_weight_func extract 30 topics using the method svd extract_topics number_of_topics 24 method svd extract 24 topics using the method nnmf max s extract_topics number_of_topics 24 min_number echo topics table echo_topics_interpretation wide_form true show the topics echo_topics_interpretation wide_form true echo topics table with 10 terms per topic echo_topics_interpretation number_of_terms 10 find the statistical thesaurus for the words n echo_statistical_thesaurus terms stemmerobj st show statistical thesaurus for king castle p echo_statistical_thesaurus terms stemmerobj st langchain few shot prompt build a few shot prompt from the dsl examples then run it over commands from langchain_core prompts import fewshotprompttemplate prompttemplate use a small subset of examples as few shot demonstrations example_pairs list python_lsa items 5 examples command cmd code code for cmd code in example_pairs example_prompt prompttemplate input_variables command code template command command n code code few_shot_prompt fewshotprompttemplate examples examples example_prompt example_prompt prefix you translate dsl commands into python code that builds an lsa pipeline follow the style of the examples suffix command command n code input_variables command print few_shot_prompt format command show the topics you translate dsl commands into python code that builds an lsa pipeline follow the style of the examples command load the package code from latentsemanticanalyzer import command use the documents adocs code latentsemanticanalyzer adocs command use dftemp code latentsemanticanalyzer dftemp command make the document term matrix code make_document_term_matrix command make the document term matrix with automatic stop words code make_document_term_matrix stemming_rules none stopwords true command show the topics code translation with ollama model run the few shot prompt against a local ollama model llm chatollama model os getenv ollama_model gemma3 12b commands use the dataset aabstracts make the document term matrix without stemming extract 40 topics using the method non negative matrix factorization show the topics llm chatollama model gemma3 12b chain few_shot_prompt llm stroutputparser sep dsl_workflow_separators python lsamon result for command in commands result append chain invoke command command print sep join x strip for x in result latentsemanticanalyzer aabstracts make_document_term_matrix stemming_rules none extract_topics 40 method non negative_matrix_factorization show_topics simulated translation with a fake llm for testing purposes it might be useful to use a fake llm so the notebook runs without setup and api keys try from langchain_community llms fake import fakelistllm except exception from langchain_core language_models fake import fakelistllm commands use the dataset aabstracts make the document term matrix without stemming extract 40 topics using the method non negative matrix factorization show the topics fake responses to demonstrate the flow fake_responses lsamon lsamon_use_dataset aabstracts lsamon lsamon_make_document_term_matrix stemming false lsamon lsamon_extract_topics method nmf n_topics 40 lsamon_show_topics lsamon llm fakelistllm responses fake_responses create a simple chain by piping the prompt into the llm chain few_shot_prompt llm for command in commands result chain invoke command command print command command print code result print command use the dataset aabstracts code lsamon lsamon_use_dataset aabstracts command make the document term matrix without stemming code lsamon lsamon_make_document_term_matrix stemming false command extract 40 topics using the method non negative matrix factorization code lsamon lsamon_extract_topics method nmf n_topics 40 command show the topics code lsamon_show_topics lsamon references aap1 anton antonov dslexamples python package 2026 github antononcube aap2 anton antonov dsl examples raku package 2025 2026 github antononcube aap3 anton antonov dslexamples wolfram language paclet 2025 2026 wolfram language paclet repository aap4 anton antonov dsl translators raku package 2020 2024 github antononcube february 12 2026 anton antonov antonov ai artificial intelligence chatgpt dsl few shot trianing llm ollama openai python technology 1 comment llmtextualanswer usage examples introduction this blog post notebook demonstrates the usage of the python package llmtextualanswer aap1 which provides function s for finding sub strings in texts that appear to be answers to given questions according to large language models llms the package implementation closely follows the implementations of the raku package ml findtextualanswer aap1 and wolfram language function llmtextualanswer aaf1 both in turn were inspired by the wolfram language function findtextualanswer wrif1 jl1 remark currently llms are utilized via the python langchain packages remark one of the primary motivations for implementing this package is to provide the fundamental functionality of extracting parameter values from domain specific texts needed for the implementation for the python version of the raku package ml nlptemplateengine aap3 setup load packages and define llm access objects from llmtextualanswer import from langchain_ollama import chatollama import os llm chatollama model os getenv ollama_model gemma3 4b usage examples here is an example of finding textual answers text lake titicaca is a large deep lake in the andes on the border of bolivia and peru by volume of water and by surface area it is the largest lake in south america llm_textual_answer text where is titicaca llm llm form none where is titicaca the andes on the border of bolivia peru by default llm_textual_answer tries to give short answers if the option request is none then depending on the number of questions the request is one those phrases give the shortest answer of the question list the shortest answers of the questions in the example above the full query given to llm is given the text lake titicaca is a large deep lake in the andes on the border of bolivia and peru by volume of water and by surface area it is the largest lake in south america give the shortest answer of the question where is titicaca here we get a longer answer by changing the value of request llm_textual_answer text where is titicaca request answer the question llm llm where is titicaca the andes on the border of bolivia peru remark the function find textual answer is inspired by the mathematica function findtextualanswer wrif1 see jl1 for details unfortunately at this time implementing the full signature of findtextualanswer with llm provider apis is not easy multiple answers consider the text textcap born and raised in the austrian empire tesla studied engineering and physics in the 1870s without receiving a degree gaining practical experience in the early 1880s working in telephony and at continental edison in the new electric power industry in 1884 he emigrated to the united states where he became a naturalized citizen he worked for a short time at the edison machine works in new york city before he struck out on his own with the help of partners to finance and market his ideas tesla set up laboratories and companies in new york to develop a range of electrical and mechanical devices p his alternating current ac induction motor and related polyphase ac patents licensed by westinghouse electric in 1888 earned him a considerable amount of money and became the cornerstone of the polyphase system which that company eventually marketed len textcap 862 here we ask a single question and request 3 answers llm_textual_answer textcap where lived n 3 llm llm where lived austrian empire united states new york city here is a rerun without number of answers argument llm_textual_answer textcap where lived llm llm where lived austrian empire united states multiple questions if several questions are given to the function llm_textual_answer then all questions are spliced with the given text into one query that is sent to llm for example consider the following text and questions query make a classifier with the method randomforest over the data dftitanic show precision and accuracy questions what is the dataset what is the method which metrics to show then the query send to the llm is given the text make a classifier with the method randomforest over the data dftitanic show precision and accuracy list the shortest answers of the questions what is the dataset what is the method which metrics to show the answers are assumed to be given in the same order as the questions each answer in a separated line hence by splitting the llm result into lines we get the answers corresponding to the questions remark for some llms if the questions are missing question marks it is likely that the result may have a completion as a first line followed by the answers in that situation the answers are not parsed and a warning message is given here is an example of requesting answers of multiple questions and specifying that the result should be a dictionary res llm_textual_answer query questions llm llm form dict for k v in res items print f k v what is the dataset dftitanic what is the method randomforest which metrics to show precision accuracy mermaid diagram the following flowchart corresponds to the conceptual steps in the package function llm_textual_answer references articles jl1 jérôme louradour new in the wolfram language findtextualanswer 2018 blog wolfram com functions aaf1 anton antonov llmtextualanswer 2024 wolfram function repository wrif1 wolfram research inc findtextualanswer wolfram language function 2018 updated 2020 packages aap1 anton antonov llmtextualanswer python package 2026 github antononcube aap2 anton antonov ml findtextualanswer raku package 2023 2025 github antononcube aap3 anton antonov ml nlptemplateengine raku package 2023 2025 github antononcube january 28 2026 anton antonov antonov ai artificial intelligence chatgpt llm ollama python technology 1 comment rapid specification of regression workflows aka regressionizer demo introduction this blog post provides examples of specifying different regression workflows using the class regressionizer of the python package regressionizer aap1 the primary focus of regressionizer is quantile regression qr rk1 rk2 it closely follows the monadic pipeline design explained in detail in the document a monad for quantile regression workflows aa1 for introduction and overview of quantile regression see the video boston user quantileregression workflows 2019 04 18 summary of regressionizer features the class regressionizer facilitates rapid specifications of regressions workflows to quickly specify data rescaling and summary regression computations outliers finding conditional cumulative distribution functions cdfs reconstruction plotting of data fits residual errors outliers cdfs regressionizer works with data frames numpy arrays lists of numbers and lists of numeric pairs details and arguments the curves computed with quantile regression are called regression quantiles regressionizer has three regression methods quantile_regression quantile_regression_fit least_squares_fit the regression quantiles computed with the methods quantile_regression and quantile_regression_fit correspond to probabilities specified with the argument probs the method quantile_regression computes fits using a b spline functions basis the basis is specified with the arguments knots and order order is 3 by default the methods quantile_regession_fit and least_squares_fit use lists of basis functions to fit with specified with the argument funcs workflows flowchart the following flowchart summarizes the workflows that are supported by regressionizer previous work roger koenker implemented the r package quantreg rkp1 anton antonov implemented the r package qrmon r for the specification of monadic pipelines for doing qr aap1 several wolfram language aka mathematica packages are implemented by anton antonov see aap1 aap2 aaf1 remark the paclets at the wolfram language paclet repository were initially mathematica packages hosted at github the wolfram function repository function quantileregression aaf1 does only b spline fitting setup load the regressionizer and other standard packages from regressionizer import import numpy as np import plotly expre...