tools_get_docs: Extract documentation from a function

In tidyprompt: Prompt Large Language Models and Enhance Their Functionality

Extract documentation from a function

Description

This function extracts documentation from a help file (if available, i.e., when the function is part of a package) or from documentation added by tools_add_docs(). The extracted documentation includes the function's name, description, arguments, and return value. This information is used to provide an LLM with information about the functions, so that the LLM can call R functions.

Usage

tools_get_docs(func, name = NULL)

Arguments

func	A function object. The function should belong to a package and have documentation available in a help file, or it should have documentation added by tools_add_docs()
name	The name of the function if already known (optional). If not provided it will be extracted from the documentation or the function object's name

Details

This function will prioritize documentation added by tools_add_docs() over documentation from a help file. Thus, it is possible to override the help file documentation by adding custom documentation

Value

A list with documentation for the function. See tools_add_docs() for more information on the contents

See Also

Other tools: answer_using_tools(), tools_add_docs()

Examples

# When using functions from base R or R packages,
#   documentation is automatically extracted from help files:
prompt_with_dir_function <- "What are the files in my current directory?" |>
  answer_using_tools(dir) # The 'dir' function is from base R
## Not run: 
  send_prompt(prompt_with_dir_function)
  # --- Sending request to LLM provider (llama3.1:8b): ---
  #   What are the files in my current directory?
  #   --- Receiving response from LLM provider: ---
  #   Calling function 'nm' with arguments:
  #   {
  #     "all.files": true,
  #     "full.names": false,
  #     "ignore.case": false,
  #     "include.dirs": false,
  #     "no..": false,
  #     "path": "./",
  #     "pattern": "*",
  #     "recursive": false
  #   }
  # Result:
  #   .git, .github, .gitignore, .Rbuildignore, .Rhistory, ...
  # The files in your current directory are:
  #   .git, .github, .gitignore, .Rbuildignore, .Rhistory, ...
  # [1] "The files in your current directory are:\n\n .git, .github, ..."

## End(Not run)

# Users may provide custom functions in two ways:
#   1) as a function object, optionally documented with `tools_get_docs()`, or
#   2) as an 'ellmer' tool definition, using `ellmer::tool()`

# Take this fake weather function as an example:
temperature_in_location <- function(
  location = c("Amsterdam", "Utrecht", "Enschede"),
  unit = c("Celcius", "Fahrenheit")
) {
  location <- match.arg(location)
  unit <- match.arg(unit)

  temperature_celcius <- switch(
    location,
    "Amsterdam" = 32.5,
    "Utrecht" = 19.8,
    "Enschede" = 22.7
  )

  if (unit == "Celcius") {
    return(temperature_celcius)
  } else {
    return(temperature_celcius * 9/5 + 32)
  }
}


# 1: `tools_add_docs()` --------------------------------------------------------

# Generate documentation for a function, based on formals & help file
docs <- tools_get_docs(temperature_in_location)

# The types get inferred from the function's formals
# However, descriptions are still missing as the function is not from a package
# We can modify the documentation object to add descriptions:
docs$description <- "Get the temperature in a location"
docs$arguments$unit$description <- "Unit in which to return the temperature"
docs$arguments$location$description <- "Location for which to return the temperature"
docs$return$description <- "The temperature in the specified location and unit"
# (See `?tools_add_docs` for more details on the structure of the documentation)

# When we are satisfied with the documentation, we can add it to the function:
temperature_in_location <- tools_add_docs(temperature_in_location, docs)

prompt_with_weather_function <-
  "What is the weather in Enschede? Give me Celcius degrees" |>
  answer_using_tools(temperature_in_location)
## Not run: 
  send_prompt(prompt_with_weather_function)
  # --- Sending request to LLM provider (llama3.1:8b): ---
  #   What is the weather in Enschede? Give me Celcius degrees
  #   --- Receiving response from LLM provider: ---
  #   Calling function 'temperature_in_location' with arguments:
  #   {
  #     "location": "Enschede",
  #     "unit": "Celcius"
  #   }
  # Result:
  #   22.7
  # The temperature in Enschede is 22.7 Celcius degrees.
  # [1] "The temperature in Enschede is 22.7 Celcius degrees."

## End(Not run)

# 2: `ellmer::tool()` -----------------------------------------------

# Alternatively, we can define the function as an 'ellmer' tool

temperature_in_location_ellmer <- ellmer::tool(
  temperature_in_location,
  name = "get_temperature",
  description = "Get the temperature in a location",
  arguments = list(
    location = ellmer::type_string(
      "Location for which to return the temperature", required = TRUE
    ),
    unit = ellmer::type_string(
      "Unit in which to return the temperature", required = TRUE
    )
  )
)

prompt_with_weather_function_ellmer <-
  "What is the weather in Utrecht? Give me Fahrenheit degrees" |>
  answer_using_tools(temperature_in_location_ellmer)
## Not run: 
  send_prompt(prompt_with_weather_function_ellmer)
  # ...

## End(Not run)

# Because `mcptools::mcp_tools()` returns a list of `ellmer:tool()` tools,
#   you can also use Model Context Protocol (MCP) server tools with
#   `answer_using_tools()`:
## Not run: 
  prompt_using_mcp_tools <- mcptools::mcp_tools()
  "Push my latest commit to GitHub" |>
    answer_using_tools(mcp_tools)
  send_prompt(prompt_using_mcp_tools)

## End(Not run)

# `answer_using_tools()` will automatically attempt to use the most appropriate
#   way of sending the tool to the LLM

# If you use a LLM provider of type 'ollama' or 'openai',
#   it will automatically convert the tool definition to parameters
#   appropriate for those APIs
# If you use a LLM provider of type 'ellmer', it will call the appropriate
#   ellmer function directly which will handle the tool call for various
#   providers
# Note that both tool definitions from `tools_add_docs()` and `ellmer::tool()`
#   will work with any LLM provider; `answer_using_tools()` can convert
#   the two types of tool definitions to each other when needed
## Not run: 
  ollama <- llm_provider_ollama()
  # Ollama LLM provider:
  "What is the weather in Amsterdam? Give me Fahrenheit degrees" |>
    answer_using_tools(temperature_in_location) |>
    send_prompt(ollama)

  # Ollama LLM provider also works with `ellmer::tool()` definitions:
  "What is the weather in Amsterdam? Give me Celcius degrees" |>
    answer_using_tools(temperature_in_location_ellmer) |>
    send_prompt(ollama)

  # Similar for OpenAI API:
  openai <- llm_provider_openai()
  "What is the weather in Amsterdam? Give me Celcius degrees" |>
    answer_using_tools(temperature_in_location) |>
    send_prompt(openai)
  # ...

  # Ellmer LLM provider:
  ellmer <- llm_provider_ellmer(ellmer::chat_openai())
  "What is the weather in Amsterdam? Give me Celcius degrees" |>
    answer_using_tools(temperature_in_location_ellmer) |>
    send_prompt(ellmer)

  # Also works with `tools_add_docs()` definition:
  "What is the weather in Amsterdam? Give me Celcius degrees" |>
    answer_using_tools(temperature_in_location) |>
    send_prompt(ellmer)

## End(Not run)

tidyprompt documentation built on Aug. 25, 2025, 5:11 p.m.