Collection of utilities for use with OpenAI's GPT-3 language model
Project description
gpt-utils
Helper tools for use with OpenAI's GPT-3 API.
The API key can be provided in three ways:
- In the environment variable
OPENAI_API_KEY
- Via class attribute
GPT.api_key
of classGPT
in modulegpt_utils.core
- Via keyword argument
api_key
in any method or function which obtains a response from OpenAI's servers
Disclaimer
These don't work perfectly.
gpt_utils.utils
from gpt_utils import utils
utils
contains simple functions which transform text in a specific way. Some functions are bidirectional; these take an optional reverse keyword argument which reverses the conversion's input and output.
utils.case
>>> from gpt_utils.utils import case
Contains functions which modify the case of words in a string. These functions are meant to selectively preserve the case of proper nouns, etc. with minimal involvement. The first, title()
, takes a str
containing a word or phrase with proper capitalization for use in a heading or title:
>>> case.title('the ant: an introduction')
'The Ant: An Introduction'
The second, mid_sentence()
, adjusts the case of input words/phrases for insertion mid-sentence:
>>> case.mid_sentence('bicycles on inman street')
'bicycles on Inman Street'
>>> case.mid_sentence('An italian restaurant in somerville')
'an Italian restaurant in Somerville'
utils.location
>>> from gpt_utils.utils import location
Currently contains one function, fix_location()
, which takes an unformatted string containing a location and formats it properly for display:
>>> location.fix_location('lenox ma')
'Lenox, MA'
utils.number
>>> from gpt_utils.utils import number
Contains two functions which convert numbers to strings. The first, number_to_str()
, takes an int
or float
and writes out the same number as a str
:
>>> number.number_to_str(83)
'eighty-three'
>>> number.number_to_string(3.14)
'three point one four'
The second, number_to_ordinal()
, takes an int
and returns it in ordinal form as a str
:
>>> number.number_to_ordinal(43)
'forty-third'
utils.dechatify()
I have found that GPT tends to produce output of lower quality when it is prompted with text containing Internet/SMS vernacular. As a remedy, dechatify()
converts text to standard/written English:
>>> utils.dechatify('how r u')
'How are you?'
>>> utils.dechatify('if the implementation is easy to explain then it might b a good idea')
'If the implementation is easy to explain, then it might be a good idea.'
utils.generate_stem()
This function accepts a str
containing a word/phrase and returns a sentence stem pairing the word/phrase with the appropriate form of the verb "to be":
>>> utils.generate_stem('GitHub')
'GitHub is'
>>> utils.generate_stem("pleiades")
'The Pleiades are'
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