Programmatic prompt template for Python.
Project description
Prompt4Py
Programmatic prompt template for Python.
Installation
pip install -U prompt4py
Quick Start
-
Create a prompt template
from prompt4py import GeneralTemplate # Create your prompt template prompt_template = GeneralTemplate() prompt_template.role = 'An NER machine' prompt_template.objective = 'Extract all {{entity_type}} from CONTEXT.' prompt_template.instruction = { 1: 'Think deeply on every entities in CONTEXT', 2: 'Extract all {{entity_type}}', 3: 'Output the entities you have extracted' } prompt_template.constraint = 'Do not include any markdown grams' prompt_template.capability = 'Extract entities' prompt_template.context = '{{ent_1}}, {{ent_2}}, {{ent_3}}' prompt_template.output_dtype = 'str' prompt_template.output_format = 'jsonl' prompt_template.output_example = str([ { 'entity_type': '{{example_entity_type_1}}', 'entity_text': '{{example_entity_text_1}}' } ])
-
Render the template
# Render the template prompt = prompt_template.render(entity_type='PERSON', ent_1='John Lennon', ent_2='Joe Biden', ent_3='Charlemagne', example_entity_type_1='PERSON', example_entity_text_1='Elizabeth') print(prompt)
the prompt would be rendered like this:
## _TIMESTAMP [82159.8475038] ## ROLE An NER machine ## OBJECTIVE Extract all PERSON from CONTEXT. ## INSTRUCTION - **1**: Think deeply on every entities in CONTEXT - **2**: Extract all PERSON - **3**: Output the entities you have extracted ## CONSTRAINT Do not include any markdown grams ## CAPABILITY Extract entities ## CONTEXT John Lennon, Joe Biden, Charlemagne ## OUTPUT_DATATYPE str ## OUTPUT_FORMAT jsonl ## OUTPUT_EXAMPLE [{'entity_type': 'PERSON', 'entity_text': 'Elizabeth'}]
-
Invoke a chatbot / causal language model
You would get response like below:
{"entity_type": "PERSON", "entity_text": "John Lennon"} {"entity_type": "PERSON", "entity_text": "Joe Biden"} {"entity_type": "PERSON", "entity_text": "Charlemagne"}
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