prapti 0.0.2

Description:

prapti 0.0.2

Prapti: converse with LLMs directly in markdown files

Hello! Welcome to Prapti. It's early, and we're just getting started. Please use GitHub Discussions for general queries and suggestions, and Issues for bug reports. See CONTRIBUTING.md for code contributions.

Prapti is a tool for prompting Large Language Models (LLMs) with an editable history. You work within a markdown file where you can edit your prompts and the LLM's responses. Press a hot-key to get the next response from the LLM. Once received, the response is automatically appended to the file.
Features

Work in your favourite editor
Markdown files are the native conversation format (headings with special text delimit message boundaries)
Easily edit the whole conversation history/context window, including previous LLM outputs
Inline configuration syntax: easily change LLM parameters and switch language models ("responders") with each round of the conversation
Designed to be extensible with plugins (responders, commands and processing hooks)
Supports OpenAI, OpenAI-compatible local servers, Koboldcpp, GPT4All (experimental), or add your favourite LLM back-end by implementing a Responder plugin

Using prapti is an interactive experience, but you can get an idea of what it might be like by reading your first prapti conversation.
Installation and Setup
Prapti requires Python 3.10 or newer.
Installation involves the following required steps:

Install the prapti command line tool
Set up your OpenAI API key (or configure a local LLM: see docs)
Check that the prapti tool runs manually in your terminal
Set up a keybinding to run prapti in your editor

1. Install the prapti command line tool
In your terminal, run:
pip install git+https://github.com/prapti-ai/prapti

(or pip3 or py -3 -m pip depending on your system).
We recommend running prapti in a Python virtual environment such as
venv.
2. Set up your OpenAI API key (or configure a local LLM)
export OPENAI_API_KEY=your-key-goes-here

(or setx, depending on your system. There are other ways to manage your environment variables. For details see OpenAI's Best Practices for API Key Safety ).
To select a non-default organisation, use the OPENAI_ORGANIZATION environment variable.
To use an alternate OpenAI API key and/or organization specifically with prapti you can set PRAPTI_OPENAI_API_KEY and/or PRAPTI_OPENAI_ORGANIZATION. If set, prapti will use these environment variables in preference to OPENAI_API_KEY and OPENAI_ORGANIZATION.
Click here for local LLM configuration
3. Check that the prapti tool runs manually in your terminal
First, create a new markdown file with .md extension. You can name it anything you like, for example test-chat.md.
Edit the file so that the final lines consist of a user prompt. A user prompt is a message that you, as the user, write to the LLM. It should look like this:
### @user:

Write your prompt here.

Then run prapti in the terminal to generate a new assistant response:
prapti test-chat.md

After running prapti, refresh or reload the test-chat.md file. You should see that prapti has appended an assistant response to the file. With this test, you've confirmed that prapti works correctly in your terminal. The next step is to set up a convenient way to run prapti, such as a hot-key, for a smoother conversation flow with the LLM.
4. Set up a keybinding to run prapti in your text editor
Choose a key combination (e.g. Ctrl-Enter) in your preferred text editor, and configure it to save the current file and run prapti. This keybinding should be set to function only when you're editing markdown files.
Below are the instructions for VSCode. If you use another editor please contribute instructions.
VSCode

NOTE: This key binding runs prapti in the active VSCode terminal window. So make sure you have the terminal open with the prapti command available.

First use the Quick Open menu (Cmd-Shift-P on Mac, Ctrl-Shift-P on Windows) to run:

Preferences: Open Keyboard Shortcuts (JSON)

then add the following binding to the opened keybindings.json file.
{
"key": "ctrl+enter",
"command": "runCommands",
"args":{
"commands":[
"workbench.action.files.save",
"cursorBottom",
{
"command": "workbench.action.terminal.sendSequence",
"args": { "text": "prapti ${file}\u000D" }
},
"cursorBottom",
]
},
"when": "editorLangId == markdown"
},

Now, when editing your markdown file you should be able to hit Ctrl-Enter to get a response from the LLM. You can watch the terminal window for progress. Be patient, GPT4 can take 30 seconds to generate a full response.
See our documentation for optional text editor setup to streamline your experience.
License
This project is MIT licensed.