Python package for finding textual answers via LLMs. This is a Python port of the Wolfram Language LLMTextualAnswer function, focused on building prompts, wiring LangChain models, and parsing structured outputs.
pip install LLMTextualAnswerfrom LLMTextualAnswer import LLMTextualAnswer
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o-mini")
text = (
"Born and raised in the Austrian Empire, Tesla studied engineering and physics "
"in the 1870s without receiving a degree."
)
questions = ["Where born?"]
result = LLMTextualAnswer(
text,
questions,
llm=llm,
form=dict,
)
print(result)Here is a list of workflow construction specifications:
queries = [
'Make a classifier with the method RandomForest over the data dfTitanic; show precision and accuracy; plot True Positive Rate vs Positive Predictive Value.',
'Make a recommender over the data frame dfOrders. Give the top 5 recommendations for the profile year:2022, type:Clothing, and status:Unpaid',
'Create an LSA object over the text collection aAbstracts; extract 40 topics; show statistical thesaurus for "notebook", "equation", "changes", and "prediction"',
'Compute quantile regression for dfTS with interpolation order 3 and knots 12 for the probabilities 0.2, 0.4, and 0.9.'
]Here are possible workflows names:
workflows = ['Classification', 'Latent Semantic Analysis', 'Quantile Regression', 'Recommendations']For each workflow spec give the corresponding (most likely) workflow name:
for q in queries:
print("Spec : " + q)
print("Class : " + llm_classify(q, workflows, llm = llm, form=dict) + "\n")# Spec : Make a classifier with the method RandomForest over the data dfTitanic; show precision and accuracy; plot True Positive Rate vs Positive Predictive Value.
# Class : Classification
#
# Spec : Make a recommender over the data frame dfOrders. Give the top 5 recommendations for the profile year:2022, type:Clothing, and status:Unpaid
# Class : Recommendations
#
# Spec : Create an LSA object over the text collection aAbstracts; extract 40 topics; show statistical thesaurus for "notebook", "equation", "changes", and "prediction"
# Class : Latent Semantic Analysis
#
# Spec : Compute quantile regression for dfTS with interpolation order 3 and knots 12 for the probabilities 0.2, 0.4, and 0.9.
# Class : Quantile Regression
#
- For more detailed examples see the notebook "Basic-usage.ipynb.
llm_textual_answerandllm_classifyaccept "LangChain" chat/text models that support.invoke.- Use
prompt_style="chat"orprompt_style="text"if auto-detection is not desired. - When you want only the prompt template, pass
form="StringTemplate".
