Generate text using LLMs with customizable prompts
Install with your favorite plugin manager, e.g. lazy.nvim
Example with Lazy
-- Minimal configuration
{ "David-Kunz/gen.nvim" },
-- Custom Parameters (with defaults)
{
"David-Kunz/gen.nvim",
opts = {
model = "mistral", -- The default model to use.
display_mode = "float", -- The display mode. Can be "float" or "split".
show_prompt = false, -- Shows the Prompt submitted to Ollama.
show_model = false, -- Displays which model you are using at the beginning of your chat session.
no_auto_close = false, -- Never closes the window automatically.
init = function(options) pcall(io.popen, "ollama serve > /dev/null 2>&1 &") end,
-- Function to initialize Ollama
command = "curl --silent --no-buffer -X POST http://localhost:11434/api/generate -d $body",
-- The command for the Ollama service. You can use placeholders $prompt, $model and $body (shellescaped).
-- This can also be a lua function returning a command string, with options as the input parameter.
-- The executed command must return a JSON object with { response, context }
-- (context property is optional).
list_models = '<omitted lua function>', -- Retrieves a list of model names
debug = false -- Prints errors and the command which is run.
}
},Here are all available models.
Alternatively, you can call the setup function:
require('gen').setup({
-- same as above
})Use command Gen to generate text based on predefined and customizable prompts.
Example key maps:
vim.keymap.set({ 'n', 'v' }, '<leader>]', ':Gen<CR>')You can also directly invoke it with one of the predefined prompts:
vim.keymap.set('v', '<leader>]', ':Gen Enhance_Grammar_Spelling<CR>')Once a conversation is started, the whole context is sent to the LLM. That allows you to ask follow-up questions with
:Gen Chatand once the window is closed, you start with a fresh conversation.
You can select a model from a list of all installed models with
require('gen').select_model()All prompts are defined in require('gen').prompts, you can enhance or modify them.
Example:
require('gen').prompts['Elaborate_Text'] = {
prompt = "Elaborate the following text:\n$text",
replace = true
}
require('gen').prompts['Fix_Code'] = {
prompt = "Fix the following code. Only ouput the result in format ```$filetype\n...\n```:\n```$filetype\n$text\n```",
replace = true,
extract = "```$filetype\n(.-)```"
}You can use the following properties per prompt:
prompt: (string | function) Prompt either as a string or a function which should return a string. The result can use the following placeholders:$text: Visually selected text$filetype: Filetype of the buffer (e.g.javascript)$input: Additional user input$register: Value of the unnamed register (yanked text)
replace:trueif the selected text shall be replaced with the generated outputextract: Regular expression used to extract the generated resultmodel: The model to use, e.g.zephyr, default:mistral