R/chat.R

In gptstudio: Use Large Language Models Directly in your Development Environment

#' Chat Interface for gptstudio
#'
#' This function provides a high-level interface for communicating with various
#' services and models supported by gptstudio. It orchestrates the creation,
#' configuration, and execution of a request based on user inputs and options
#' set for gptstudio. The function supports a range of tasks from text
#' generation to code synthesis and can be customized according to skill level
#' and coding style preferences.
#'
#' @param prompt A string containing the initial prompt or question to be sent
#'   to the model. This is a required parameter.
#' @param service The AI service to be used for the request. If not explicitly
#'   provided, this defaults to the value set in
#'   `getOption("gptstudio.service")`. If the option is not set, make sure to
#'   provide this parameter to avoid errors.
#' @param history An optional parameter that can be used to include previous
#'   interactions or context for the current session. Defaults to a system
#'   message indicating "You are an R chat assistant".
#' @param stream A logical value indicating whether the interaction should be
#'   treated as a stream for continuous interactions. If not explicitly
#'   provided, this defaults to the value set in
#'   `getOption("gptstudio.stream")`.
#' @param model The specific model to use for the request. If not explicitly
#'   provided, this defaults to the value set in `getOption("gptstudio.model")`.
#' @param skill A character string indicating the skill or capability level of
#'   the user. This parameter allows for customizing the behavior of the model
#'   to the user. If not explicitly provided, this defaults to the value set in
#'   `getOption("gptstudio.skill")`.
#' @param style The coding style preferred by the user for code generation
#'   tasks. This parameter is particularly useful when the task involves
#'   generating code snippets or scripts. If not explicitly provided, this
#'   defaults to the value set in `getOption("gptstudio.code_style")`.
#' @param task The specific type of task to be performed, ranging from text
#'   generation to code synthesis, depending on the capabilities of the model.
#'   If not explicitly provided, this defaults to the value set in
#'   `getOption("gptstudio.task")`.
#' @param custom_prompt An optional parameter that provides a way to extend or
#'   customize the initial prompt with additional instructions or context.
#' @param process_response A logical indicating whether to process the model's
#'   response. If `TRUE`, the response will be passed to
#'   `gptstudio_response_process()` for further processing. Defaults to `FALSE`.
#'   Refer to `gptstudio_response_process()` for more details.
#' @param ... Reserved for future use.
#'
#' @return Depending on the task and processing, the function returns the
#'   response from the model, which could be text, code, or any other structured
#'   output defined by the task and model capabilities. The precise format and
#'   content of the output depend on the specified options and the capabilities
#'   of the selected model.
#'
#' @examples
#' \dontrun{
#' # Basic usage with a text prompt:
#' result <- chat("What is the weather like today?")
#'
#' # Advanced usage with custom settings, assuming appropriate global options are set:
#' result <- chat(
#'   prompt = "Write a simple function in R",
#'   skill = "advanced",
#'   style = "tidyverse",
#'   task = "coding"
#' )
#'
#' # Usage with explicit service and model specification:
#' result <- chat(
#'   prompt = "Explain the concept of tidy data in R",
#'   service = "openai",
#'   model = "gpt-4-turbo-preview",
#'   skill = "intermediate",
#'   task = "general"
#' )
#' }
#'
#' @export
chat <- function(prompt,
                 service = getOption("gptstudio.service"),
                 history = list(list(role = "system", content = "You are an R chat assistant")),
                 stream = FALSE,
                 model = getOption("gptstudio.model"),
                 skill = getOption("gptstudio.skill"),
                 style = getOption("gptstudio.code_style", "no preference"),
                 task = getOption("gptstudio.task", "coding"),
                 custom_prompt = NULL,
                 process_response = FALSE,
                 ...) {
  response <-
    gptstudio_create_skeleton(
      service = service,
      prompt = prompt,
      history = history,
      stream = stream,
      model = model,
      ...
    ) %>%
    gptstudio_skeleton_build(
      skill = skill,
      style = style,
      task = task,
      custom_prompt = custom_prompt
    ) %>%
    gptstudio_request_perform()

  if (process_response) {
    response %>% gptstudio_response_process()
  } else {
    response$response
  }
}