kznllm.nvim

The only main command is leader + k, it does nothing more than fill in some LLM completion into the text buffer. It has two main behaviors:

1. If you made a visual selection, it will attempt to replace your selection with a valid code fragment.
2. If you make no visual selection, it can yap freely (or do something else specified by a good template).

Note

project-mode is also available when you have a directory named .kzn. It will use the folder closest to your current working directory and traverse backwards until it finds a .kzn directory or reaches your home directory and exits.

It's easy to hack on and implement customize behaviors without understanding much about nvim plugins. Try the default preset configuration provided below, but I recommend you fork the repo and using the preset as a reference for implementing your own features.

• close-to-natty coding experience
• add custom prompt templates
• pipe any context into template engine
• extend with custom features/modes

kznllm_demo_v2.mp4

Configuration

Make your API keys available via environment variables

export LAMBDA_API_KEY=secret_...
export ANTHROPIC_API_KEY=sk-...
export OPENAI_API_KEY=sk-proj-...
export GROQ_API_KEY=gsk_...
export DEEPSEEK_API_KEY=vllm_...
export VLLM_API_KEY=vllm_...

Note

This plugin depends on minijinja-cli (cargo install minijinja-cli, but double-check) - way easier to compose prompts.

full config w/ supported presets and a switch mechanism and provider-specific debug functions

{
  'chottolabs/kznllm.nvim',
  -- dev = true,
  -- dir = /path/to/your/fork,
  dependencies = {
    { 'nvim-lua/plenary.nvim' }
  },
  config = function(self)
    local presets = require 'kznllm.presets'

    -- bind a key to the preset switcher
    vim.keymap.set({ 'n', 'v' }, '<leader>m', presets.switch_presets, { desc = 'switch between presets' })

    local function llm_fill()
      local spec, preset = presets.load()
      presets.invoke_llm(
        preset.make_data_fn,
        spec.make_curl_args,
        spec.make_job,
        vim.tbl_extend('keep', preset.opts, {})
      )
    end

    vim.keymap.set({ 'n', 'v' }, '<leader>k', llm_fill, { desc = 'Send current selection to LLM llm_fill' })

    -- optional for debugging purposes
    local function debug()
      local spec, preset = presets.load()
      presets.invoke_llm(
        preset.make_data_fn,
        spec.make_curl_args,
        spec.make_job,
        vim.tbl_extend('keep', preset.opts, {
          debug = true,
        })
      )
    end

    vim.keymap.set({ 'n', 'v' }, '<leader>d', debug, { desc = 'Send current selection to LLM debug' })

    vim.api.nvim_set_keymap('n', '<Esc>', '', {
      noremap = true,
      silent = true,
      callback = function()
        vim.api.nvim_exec_autocmds('User', { pattern = 'LLM_Escape' })
      end,
    })
  end
},

for local openai server (e.g. vllm serve w/ --api-key <token> and --served-model-name meta-llama/Meta-Llama-3.1-8B-Instruct) set VLLM_API_KEY=<token>

---

Contributing

See CONTRIBUTING to understand the typical development workflow for Neovim plugins using Lazy and some straightforward ways you can modify the plugin to suit your needs

---

Additional Notes

Originally based on dingllm.nvim - but diverged quite a bit

• prompts user for additional context before filling
• structured to make the inherent coupling between neovim logic, LLM streaming spec, and model-specific templates more explicit
• uses jinja as templating engine for ensuring correctness in more complex prompts
• preset defaults + simple approach for overriding them
• free cursor movement during generation
• avoids "undojoin after undo" error

Alternative Configurations

minimal configuration with custom make_data_fn and no preset switcher. As you can see, the make_data_fn is simply building the data portion of the API call and will accept anything supported by the associated provider.

local presets = require 'kznllm'
local presets = require 'kznllm.presets'
local Path = require 'plenary.path'

local TEMPLATE_DIRECTORY = Path:new(vim.fn.expand(self.dir) .. '/templates')

---Example implementation of a `make_data_fn` compatible with `kznllm.invoke_llm` for groq spec
---@param prompt_args any
---@param opts { model: string, temperature: number, template_directory: Path, debug: boolean }
---@return table
---
local function make_data_for_openai_chat(prompt_args, opts)
  return {
    messages = {
      {
        role = 'system',
        content = kznllm.make_prompt_from_template(opts.template_directory / 'nous_research/fill_mode_system_prompt.xml.jinja', prompt_args),
      },
      {
        role = 'user',
        content = kznllm.make_prompt_from_template(opts.template_directory / 'nous_research/fill_mode_user_prompt.xml.jinja', prompt_args),
      },
    },
    model = opts.model,
    temperature = opts.temperature,
    stream = true,
  }
end

-- set initial preset on load
local spec = require('kznllm.specs.groq')

local function llm_fill()
  presets.invoke_llm(
    make_data_for_openai_chat,
    spec.make_curl_args,
    spec.make_job,
    {
      model = 'llama-3.1-70b-versatile',
      max_tokens = 8192,
      temperature = 0.7,
      base_url = 'https://api.groq.com',
      endpoint = '/openai/v1/chat/completions',
      template_directory = TEMPLATE_DIRECTORY,
    })
  )
end

vim.keymap.set({ 'n', 'v' }, '<leader>k', llm_fill, { desc = 'Send current selection to LLM llm_fill' })