llm.rb

if Rails.env.development?
  require 'langchain'
  LLM_CLIENT ||= Langchain::LLM::OpenAI.new(
    api_key: ENV.fetch('CHATGPT_API_KEY'),
    default_options: {
      temperature: 0.2, # Lower temperature for deterministic output
      chat_model: 'gpt-4o'
    }
  )
  Langchain.logger.level = Logger::DEBUG
  Langchain.logger = Logger.new("log/llm.log", **Langchain::LOGGER_OPTIONS)

  ##
  # Quick and dirty `llm` helper
  #
  # Usage:
  #   > llm 'a ruby query to get the biggest row in the PgSearch::Document model'
  #   # => PgSearch::Document.order('length(content) DESC').first
  #   nil
  #   # Use console paste keybinding to paste the result
  #   > PgSearch::Document.order('length(content) DESC').first
  #
  def llm(prompt)
    context = "Please be concise! In a Rails project, give a single line of " \
              "Ruby code that does the following:\n#{prompt}"
    response = LLM_CLIENT.chat(messages: [ { role: "system", content: context } ])
    to_clean = /```ruby|\A`+|`+\z/
    result = response.chat_completion.gsub(to_clean, '').strip

    case RUBY_PLATFORM
    when /darwin/ # macOS: Use `pbcopy`
      IO.popen('pbcopy', 'w') { |clipboard| clipboard << result }
    when /linux/  # Linux: Use `xclip` or `xsel`
      begin
        IO.popen('xclip -selection clipboard', 'w') { |clipboard| clipboard << result }
      rescue Errno::ENOENT
        IO.popen('xsel --clipboard --input', 'w') { |clipboard| clipboard << result }
      end
    when /mswin|mingw|cygwin/ # Windows: Use `clip`
      IO.popen('clip', 'w') { |clipboard| clipboard << result }
    end

    puts "# => #{result}"
  end
end