askai 1.0.5

Your simple terminal helper

askai is a CLI integration with OpenAI's GPT3, enabling you to ask questions and receive the answers straight in your terminal.

❗ Other model integrations ❗
Currently, askai only supports integration with OpenAI. But as soon as other NLP API-endpoints start popping up, I will work on integrating them as well.

Installation

You can either install it through pip

pip install askai

or directly from the repo

git clone [e-mail]:maxvfischer/askai.git
cd askai
pip install .

Initialize askai

askai needs to be initialized to set up the default config and to connect your OpenAI API key. Note that the key is only stored locally in ~/.askai/key.

askai init

Create OpenAI API-key

For askai to work, you need to add an OpenAI API-key. When creating an OpenAI account, they give you $18 to use for free. After that you need to set up a paid account to continue using their service.

During the development and testing of this CLI, I used $0.67.

An OpenAI API-key can be created by:

1. Creating an account on OpenAI: https://openai.com/api/
2. Logging in and click on New API keys
3. Click Create new secret key

How to use

Simple question

Ask a question using your saved config.

askai "<QUESTION>"

Override config

It's possible to override the default config by using arguments:

askai "<QUESTION>" --num-answers <INT> --model <MODEL_STRING> --temperature <FLOAT> --top_p <FLOAT> --max-tokens <INT> --frequency-penalty <FLOAT> --presence_penalty <FLOAT>

Argument	Allowed values	Description
--num-answers or -n	>0	Number of answers to generate. Note that more answers consume more tokens
--model or -m	See list below	Which model to use. See list of available models below.
--temperature or -t	0.0 <= t <= 1.0	What sampling temperature to use. Higher value makes the model more "creative". Do not use at the same time as top-p.
--top-p	0.0 <= top_p <= 1.0	What sampling nucleus to use. The model considers the results of the tokens with top_p probability mass. Do not use at the same time as temperature.
--max-tokens	>0	Maximum number of tokens used per question (incl. question + answer)
--frequency-penalty	-2.0 <= frequency_penalty <= 2.0	Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
--presence-penalty	-2.0 <= presence_penalty <= 2.0	Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.

Update config

If you find yourself overriding the config a lot when asking questions, you can update the default config instead.

Update all config values

askai config update all

Update individual config values

askai config update num-answers
askai config update model
askai config update temperature
askai config update top-p
askai config update max-tokens
askai config update frequency-penalty
askai config update presence-penalty

Reset to default config

askai config reset

See current config

askai config show

Update API-key

It's possible to update the API-key without re-initializing the CLI.

Overwrite current API-key

askai key add

Remove current API-key

askai key remove

Available models

This list was updated from OpenAI's website on 2022-12-20 and might go out of date at any time. Please check here to see an accurate list.

Text-generating models

--model	Description	Max tokens
text-davinci-003	The most capable GPT3 model	4000
text-curie-001	Worse than davinci. Still capable and faster	2048
text-babbage-001	Can do straight forward tasks. Very fast	2048
text-ada-001	Capable of very simple tasks. Very fast	2048

Code-generating models

--model	Description	Max tokens
code-davinci-002	Most capable code-generating model.	8000
code-cushman-001	Almost as capable as davinci, but faster.	2048

Important notes

Note that the answers generated by OpenAI and shown by askai is by no means a "truth". You always need to be skeptical. And of course, don't execute generated commands or code without verifying that it does what you asked for.