lang

Use an LLM to translate a function’s help documentation on the fly. lang overrides the ? and help() functions in your R session. If you are using RStudio or Positron, the translated help page will appear in the ‘Help’ pane.

Installing

To install the CRAN version of lang use:

install.packages("lang")

To install the GitHub version of lang, use:

install.packages("pak")
pak::pak("mlverse/lang")

Using lang

In order to work, lang needs two things:

1. An LLM connection

2. A target language (e.g.: Spanish, French, Korean)

These two can be defined using lang_use(). For example, the following code shows how to use OpenAI’s GPT-4o model to translate lm()’s help into Spanish:

library(lang)

chat <- ellmer::chat_openai(model = "gpt-4o")

lang_use(backend = chat, .lang = "spanish")

?lm
#> ■■                                 4% | Title

After setup, simply use ? to trigger and display the translated documentation. Note that R enforces the printed names of each section, so titles such as “Description”, “Usage”, and “Arguments” will always remain untranslated.

During translation, lang will display its progress by showing which section of the documentation is currently translating. Because each section of a help page is translated independently, the LLM can lose track of the broader topic and produce inconsistent or out-of-context translations. To address this, lang first summarizes the full help page in English, translates that summary into the target language, and then uses it as context when translating each individual section. You can control the length of this summary with the context_size argument in lang_use() or lang_help() — set it to 0 to disable it, or increase it to give the LLM more context. During the R session, if you request the same R function’s help more than one time then lang will use its cached results, which will run immediately.

LLM connections

There are two ways to define the LLM in lang_use():

1. Use an ellmer chat object:

   lang_use(backend = ellmer::chat_openai(model = "gpt-4o"))

2. Use local LLMs available through Ollama. Pass "ollama" as the backend argument, and specify which installed model to use:

   lang_use(backend = "ollama", model = "llama3.2", seed = 100)

   Under the hood, lang uses the ollamar package to integrate with Ollama. Any additional arguments, such as seed as shown above, will be passed as-is to ollamar’s chat() function.

Target language

In order of priority, these are the ways in which lang determines the language it will translate to:

1. Value in .lang when calling lang_use()
2. LANGUAGE environment variable
3. LANG environment variable

It is likely that your LANG variable already defaults to your locale. For example, mine is set to: en_US.UTF-8 (that means English, United States). For someone in France, the locale would be something such as fr_FR.UTF-8. Llama3.2 recognizes these UTF locales, and using lang, calling ? will result in translating the function’s help documentation into French.

If both environment variables are set, and are different from each other, lang will display a one-time message indicating which value it will use. If the target language is English, lang will re-route help calls back to base R.

To check the current target language at any point during the R session, simply run: lang_use(), with no arguments, and it will print out the current settings, which include language:

lang_use()
#> Model: gpt-4o via OpenAI
#> Lang: spanish

Tips

Caching

By default, lang will cache the translations it performs in a temporary folder. If R is restarted, a new folder will be used.

If you notice that you are translating the same function’s help over and over and across different R sessions, then fixing the cache location would be helpful. Use .cache to define the folder:

lang::lang_use(
  backend = "ollama", 
  model = "llama3.2", 
  .cache = "~/help-translations/", 
  .lang = "spanish"
  )

Auto-initialize at startup

If lang becomes a regular part of your workflow, and running lang_use() at the beginning of every R session becomes cumbersome, then consider letting R connect at start up.

If present, the .Rprofile file runs at the beginning of any R session. If you wish to automatically set the model and language to use, add a call to lang_use() to this file. You can call usethis::edit_r_profile() to open your .Rprofile file so you can add the option.

Here is an example using Ollama:

lang::lang_use(
  backend = "ollama",
  model = "llama3.2",
  .cache = "~/help-translations/",
  .lang = "spanish",
  .silent = TRUE
  )

And here is an example using an ellmer chat object:

lang::lang_use(
  backend = ellmer::chat_openai(model = "gpt-4o"),
  .cache = "~/help-translations/",
  .lang = "spanish",
  .silent = TRUE
  )

In both examples, .silent is set to TRUE so that there is no message every time the R session is restarted. The .cache argument points to a fixed folder so that translations persist across sessions. You can also set .context_size here to control how much context the LLM receives when translating each section.

Considerations

Translations are not perfect

As you can imagine, the quality of translation will mostly depend on the LLM being used. This solution is meant to be as helpful as possible, but we acknowledge that at this stage of LLMs, only a human curated translation will be the best solution. Having said that, I believe that even an imperfect translation could go a long way with someone who is struggling to understand how to use a specific function in a package and may also struggle with the English language.

Debugging

If the original English help page displays, check your environment variables:

Sys.getenv("LANG")
#> [1] ""
Sys.getenv("LANGUAGE")
#> [1] ""

In my case, lang recognizes that the environment is set to English, because of the en code in the variable. If your LANG variable is set to en_... then no translation will occur.

If this is your case, set the LANGUAGE variable to your preference. You can use the full language name, such as ‘spanish’, or ‘french’, etc. You can use Sys.setenv(LANGUAGE = "[my language]"), or, for a more permanent solution, add the entry to your .Renviron file (usethis::edit_r_environ()).

Interaction with mall

lang uses the mall package to produce the translations. To avoid conflicts in the setup and use of both packages during the R session, lang runs mall in a separate R process which is only alive while translating the documentation. This means that you can have a specific LLM setup for lang, and a different one for mall during your R session.