path: root/python-gpt3-simple-primer.spec

%global _empty_manifest_terminate_build 0
Name:		python-gpt3-simple-primer
Version:	0.1.2
Release:	1
Summary:	GPT-3 wrapper for Python
License:	MIT License
URL:		https://github.com/happilyeverafter95/gpt-3
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/93/b7/47e32af4f928a355c2f87697ba5fd030939e1b2f9e795761c9fe93ee0db8/gpt3_simple_primer-0.1.2.tar.gz
BuildArch:	noarch

Requires:	python3-openai

%description
# gpt3-simple-primer

Simple GPT-3 primer using `openai`.

## Background

Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. For more information, visit https://openai.com/blog/openai-api/.

The [OpenAI Python library](https://github.com/openai/openai-python) is the official Python wrapper for the OpenAI API. The purpose of this library is to simplify the priming process by providing easy to use methods for setting the instructions and adding examples.

## Priming

Priming is the practice of providing an initial prompt to the language model to improve subsequent model predictions.

GPT-3 generally does very well even with short instructions and a few examples of your intended use case. Examples are typically delimited based on input and output. For instance, GPT-3 can be used to predict food ingredients based on the following prompt:

```
Given the name of a food, list the ingredients used to make this meal.

Food: apple pie
Ingredients: apple, butter, flour, egg, cinnamon, crust, sugar

Food: guacamole
Ingredients: avocado, tomato, onion, lime, salt
```

## Requirements

You will need an API key from OpenAI to access GPT-3.

## Installation

To install, run:

```
pip install gpt3-simple-primer
```

## Usage

`input_text` and `output_text` determines how input and output are delimited in the examples. The default is to use `Input` and `Output`.

```
from gpt3_simple_primer import GPT3Generator, set_api_key

KEY = 'sk-xxxxx'  # openai key
set_api_key(KEY)

generator = GPT3Generator(input_text='Food',
                          output_text='Ingredients')

generator.set_instructions('List the ingredients for this meal.')
generator.add_example('apple pie', 'apple, butter, flour, egg, cinnamon, crust, sugar')
generator.add_example('guacamole', 'avocado, tomato, onion, lime, salt')

# Ingredients: cream, egg yolk, sugar, lime, key lime juice
generator.generate(prompt='key lime pie',
                   engine='davinci',
                   max_tokens=20,
                   temperature=0.5,
                   top_p=1)
```

To see the prompt used for priming:

```
generator.get_prompt()
```

To remove an example from the prompt:

```
generator.remove_example('apple pie')
```

## Examples

The library includes examples of GPT-3 applications based off of specific prompts.

```
from gpt3_simple_primer import set_api_key
from gpt3_simple_primer.examples import idiom_explainer

KEY = 'sk-xxxxx'  # openai key
set_api_key(KEY)

idiom_explainer.generate('hill to die on', max_tokens=15, engine='davinci')
```




%package -n python3-gpt3-simple-primer
Summary:	GPT-3 wrapper for Python
Provides:	python-gpt3-simple-primer
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-gpt3-simple-primer
# gpt3-simple-primer

Simple GPT-3 primer using `openai`.

## Background

Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. For more information, visit https://openai.com/blog/openai-api/.

The [OpenAI Python library](https://github.com/openai/openai-python) is the official Python wrapper for the OpenAI API. The purpose of this library is to simplify the priming process by providing easy to use methods for setting the instructions and adding examples.

## Priming

Priming is the practice of providing an initial prompt to the language model to improve subsequent model predictions.

GPT-3 generally does very well even with short instructions and a few examples of your intended use case. Examples are typically delimited based on input and output. For instance, GPT-3 can be used to predict food ingredients based on the following prompt:

```
Given the name of a food, list the ingredients used to make this meal.

Food: apple pie
Ingredients: apple, butter, flour, egg, cinnamon, crust, sugar

Food: guacamole
Ingredients: avocado, tomato, onion, lime, salt
```

## Requirements

You will need an API key from OpenAI to access GPT-3.

## Installation

To install, run:

```
pip install gpt3-simple-primer
```

## Usage

`input_text` and `output_text` determines how input and output are delimited in the examples. The default is to use `Input` and `Output`.

```
from gpt3_simple_primer import GPT3Generator, set_api_key

KEY = 'sk-xxxxx'  # openai key
set_api_key(KEY)

generator = GPT3Generator(input_text='Food',
                          output_text='Ingredients')

generator.set_instructions('List the ingredients for this meal.')
generator.add_example('apple pie', 'apple, butter, flour, egg, cinnamon, crust, sugar')
generator.add_example('guacamole', 'avocado, tomato, onion, lime, salt')

# Ingredients: cream, egg yolk, sugar, lime, key lime juice
generator.generate(prompt='key lime pie',
                   engine='davinci',
                   max_tokens=20,
                   temperature=0.5,
                   top_p=1)
```

To see the prompt used for priming:

```
generator.get_prompt()
```

To remove an example from the prompt:

```
generator.remove_example('apple pie')
```

## Examples

The library includes examples of GPT-3 applications based off of specific prompts.

```
from gpt3_simple_primer import set_api_key
from gpt3_simple_primer.examples import idiom_explainer

KEY = 'sk-xxxxx'  # openai key
set_api_key(KEY)

idiom_explainer.generate('hill to die on', max_tokens=15, engine='davinci')
```




%package help
Summary:	Development documents and examples for gpt3-simple-primer
Provides:	python3-gpt3-simple-primer-doc
%description help
# gpt3-simple-primer

Simple GPT-3 primer using `openai`.

## Background

Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. For more information, visit https://openai.com/blog/openai-api/.

The [OpenAI Python library](https://github.com/openai/openai-python) is the official Python wrapper for the OpenAI API. The purpose of this library is to simplify the priming process by providing easy to use methods for setting the instructions and adding examples.

## Priming

Priming is the practice of providing an initial prompt to the language model to improve subsequent model predictions.

GPT-3 generally does very well even with short instructions and a few examples of your intended use case. Examples are typically delimited based on input and output. For instance, GPT-3 can be used to predict food ingredients based on the following prompt:

```
Given the name of a food, list the ingredients used to make this meal.

Food: apple pie
Ingredients: apple, butter, flour, egg, cinnamon, crust, sugar

Food: guacamole
Ingredients: avocado, tomato, onion, lime, salt
```

## Requirements

You will need an API key from OpenAI to access GPT-3.

## Installation

To install, run:

```
pip install gpt3-simple-primer
```

## Usage

`input_text` and `output_text` determines how input and output are delimited in the examples. The default is to use `Input` and `Output`.

```
from gpt3_simple_primer import GPT3Generator, set_api_key

KEY = 'sk-xxxxx'  # openai key
set_api_key(KEY)

generator = GPT3Generator(input_text='Food',
                          output_text='Ingredients')

generator.set_instructions('List the ingredients for this meal.')
generator.add_example('apple pie', 'apple, butter, flour, egg, cinnamon, crust, sugar')
generator.add_example('guacamole', 'avocado, tomato, onion, lime, salt')

# Ingredients: cream, egg yolk, sugar, lime, key lime juice
generator.generate(prompt='key lime pie',
                   engine='davinci',
                   max_tokens=20,
                   temperature=0.5,
                   top_p=1)
```

To see the prompt used for priming:

```
generator.get_prompt()
```

To remove an example from the prompt:

```
generator.remove_example('apple pie')
```

## Examples

The library includes examples of GPT-3 applications based off of specific prompts.

```
from gpt3_simple_primer import set_api_key
from gpt3_simple_primer.examples import idiom_explainer

KEY = 'sk-xxxxx'  # openai key
set_api_key(KEY)

idiom_explainer.generate('hill to die on', max_tokens=15, engine='davinci')
```




%prep
%autosetup -n gpt3-simple-primer-0.1.2

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-gpt3-simple-primer -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.2-1
- Package Spec generated