A command-line interface (CLI) productivity tool powered by OpenAI's Davinci model, that will help you accomplish your tasks faster and more efficiently.
shell-gpt: An interface to OpenAI's GPT-3 API
This module provides a simple interface for OpenAI's GPT-3 API using Typer as the command line interface. It supports different modes of output including shell commands and code, and allows users to specify the desired OpenAI model and length and other options of the output. Additionally, it supports executing shell commands directly from the interface.
Access token is stored locally for easy use in future sessions.
pip install shell-gpt --user
On first start you would need to generate and provide your access token, get one here.
sgpt
has a variety of use cases, including simple queries, shell queries, and code queries.
We can use it pretty much as normal search engine, asking about anything, for example:
sgpt "nginx default config file location"
# -> The default Nginx config location is /etc/nginx/nginx.conf
sgpt "docker show all local images"
# -> You can view all locally available Docker images by running: `docker images`
sgpt "mass of sun"
# -> = 1.99 × 10^30 kg
Usually we are forgetting commands like chmod 444
and we want quickly find the answer in google, but now we "google" and execute it right in the terminal using --shell
flag sgpt
will provide only shell commands:
sgpt --shell "make all files in current directory read only"
# -> chmod 444 *
Since we are receiving valid shell command, we can execute it using eval $(sgpt --shell "make all files in current directory read only")
but this is not very convenient, instead we can use --execute
(or shortcut -se
for --shell
--execute
) parameter:
sgpt --shell --execute "make all files in current directory read only"
# -> chmod 444 *
# -> Execute shell command? [y/N]: y
# ...
Let's try some docker containers:
sgpt -se "start nginx using docker, forward 443 and 80 port, mount current folder with index.html"
# -> docker run -d -p 443:443 -p 80:80 -v $(pwd):/usr/share/nginx/html nginx
# -> Execute shell command? [y/N]: y
# ...
Also, we can provide some parameters name in our prompt, for example, passing output file name to ffmpeg:
sgpt -se "slow down video twice using ffmpeg, input video name \"input.mp4\" output video name \"output.mp4\""
# -> ffmpeg -i input.mp4 -filter:v "setpts=2.0*PTS" output.mp4
# -> Execute shell command? [y/N]: y
# ...
We can apply additional shell magic in our prompt, in this example passing file names to ffmpeg:
ls
# -> 1.mp4 2.mp4 3.mp4
sgpt -se "using ffmpeg combine multiple videos into one without audio. Video file names: $(ls -m)"
# -> ffmpeg -i 1.mp4 -i 2.mp4 -i 3.mp4 -filter_complex "[0:v] [1:v] [2:v] concat=n=3:v=1 [v]" -map "[v]" out.mp4
# -> Execute shell command? [y/N]: y
# ...
Since GPT-3 models can also do summarization and analyzing of input text, we can ask text-davinci-003
to generate commit message:
sgpt "Generate git commit message with details, my changes: $(git diff)"
# -> Commit message: Implement Model enum and get_edited_prompt() func, add temperature, top_p and editor args for OpenAI request.
Or ask it to find error in logs and provide more details:
sgpt "check these logs, find errors, and explain what the error is about: ${docker logs -n 20 container_name}"
# ...
With --code
parameters we can query only code as output, for example:
sgpt --code "Solve classic fizz buzz problem using Python"
for i in range(1, 101):
if i % 3 == 0 and i % 5 == 0:
print("FizzBuzz")
elif i % 3 == 0:
print("Fizz")
elif i % 5 == 0:
print("Buzz")
else:
print(i)
Since it is valid python code, we can redirect the output to file:
sgpt --code "solve classic fizz buzz problem using Python" > fizz_buzz.py
python fizz_buzz.py
# 1
# 2
# Fizz
# 4
# Buzz
# Fizz
# ...
This is, just some examples of what we can do using GPT-3 models, I'm sure you will find it useful for your specific use cases.
╭─ Arguments ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ prompt [PROMPT] The prompt to generate completions for. │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --shell -s Provide shell command as output. │
│ --execute -e Will execute --shell command. │
│ --code --no-code Provide code as output. [default: no-code] │
│ --editor --no-editor Open $EDITOR to provide a prompt. [default: no-editor] │
│ --animation --no-animation Typewriter animation. [default: animation] │
│ --spinner --no-spinner Show loading spinner during API request. [default: spinner] │
│ --help Show this message and exit. │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
Use the provided Dockerfile
to build a container:
docker build -t sgpt .
You may use a named volume (therefore sgpt will ask your API key only once) to run the container:
docker run --rm -ti -v gpt-config:/home/app/.config/shell-gpt sgpt "what are the colors of a rainbow"