Gettext LLM

Automated translations

Translating from one language to another requires the translator to be accurate and to maintain a consistent tone/persona and style. Scaling these to multiple languages is challenging. Automated translation with LLM's provide a "good enough" alternative in many use cases.

Elixir Gettext LLM library allows you to translate all Gettext PO folders/files in your project using any LLM endpoint supported by langchain. The library provides several mix tasks that can be run directly in your Elixir/Phoenix project from the command line (ie. locally on the dev machine) or part of a CI/CD pipeline.

gettext_llm provides configurable tone/persona and style. This allows you to "shape" your resulting translations into something that is compatible with your app audience & brand.

Installation

The package can be installed by adding gettext_llm to your list of dependencies in mix.exs:

def deps do
  [
      {:gettext_llm, "0.1.9", only: [:dev, :test]}
  ]
end

Usage

1. Use gettext to extract & merge

gettext_llm translates PO files. Use gettext to extract all the translated messages from your app into POT files & merge them into their respective PO files

mix gettext.extract
mix gettext.merge priv/gettext --no-fuzzy

2. Add the gettext_llm in your config.exs

gettext_llm uses langchain to call the LLM endpoints. As such gettext_llm can translate using any LLM endpoint supported by langchain. gettext_llm reads the endpoint specific config and passes it directly to langchain.

Example configuration with OpenAI

# General application configuration
import Config

config :gettext_llm, GettextLLM,
  # ignored_languages: ["en"] <--- Optional but good to skip translating your reference language 
  persona:
    "You are translating messages for a website that connects people needing help with people that can provide help. You will provide translation that is casual but respectful and uses plain language.",
  style:
    "Casual but respectul. Uses plain plain language that can be understood by all age groups and demographics.",
  endpoint: LangChain.ChatModels.ChatOpenAI,
  endpoint_model: "gpt-4",
  endpoint_temperature: 0,
  endpoint_config: %{
    "openai_key" =>
      "<YOUR_OPENAI_KEY>",
    "openai_org_id" => "<YOUR_ORG_ID>"
  }

Example configuration with Anthropic

# General application configuration
import Config

config :gettext_llm, GettextLLM,
  # ignored_languages: ["en"] <--- Optional but good to skip translating your reference language 
  persona:
    "You are translating messages for a website that connects people needing help with people that can provide help. You will provide translation that is casual but respectful and uses plain language.",
  style:
    "Casual but respectul. Uses plain plain language that can be understood by all age groups and demographics.",
  endpoint: LangChain.ChatModels.ChatAnthropic,
  endpoint_model: "claude-3-5-sonnet-latest",
  endpoint_temperature: 0,
  endpoint_config: %{
    "anthropic_key" =>
      "<YOUR_ANTHROPIC_KEY>"
  }

3. Run gettext_llm mix task

Run using the default gettext location (ie. priv/gettext)

mix gettext_llm.translate translate

Run using a specific gettext location

mix gettext_llm.translate translate my_path/gettext 

Other gettext_llm mix task

Check that your configuration is correct

mix gettext_llm.translate info

Display help

mix help gettext_llm.translate 

Documentation

Documentation can be be found at https://hexdocs.pm/gettext_llm.

Misc

For some apps or languages LLM's are not good enough. In these cases you will probably be better off with a human translator. The human translator could work on it's own or part of a hybrind setup. A typical setup has the draft translation version proposed by an LLM and the final approval (and corrections) are performed by the human. Good open source solutions for such a setup are Kanta or Weblate.

Thanks

Special thanks to Adrian Codausi & Goran Codausi for inspiring me to build this. They have build an earlier prototype of a similar functionality in another project.