#' Makes bunch chat completion requests to the ChatGPT API
#'
#' @description
#' `chatgpt()` is the package's main function for the ChatGPT functionality and takes as input a vector of prompts and processes each prompt as per the defined parameters. It extends the `chatgpt_single()` function to allow for bunch processing of requests to the Open AI GPT API.
#' @details
#' The easiest (and intended) use case for this function is to create a data.frame or data.table with variables that contain the prompts to be requested from ChatGPT and a prompt id (see examples below).
#' For a general guide on the chat completion requests, see [https://platform.openai.com/docs/guides/chat/chat-completions-beta](https://platform.openai.com/docs/guides/chat/chat-completions-beta). This function provides you with an R wrapper to send requests with the full range of request parameters as detailed on [https://platform.openai.com/docs/api-reference/chat/create](https://platform.openai.com/docs/api-reference/chat/create) and reproduced below.
#'
#'
#' If `id_var` is not provided, the function will use `prompt_1` ... `prompt_n` as id variable.
#'
#' Parameters not included/supported:
#'   - `logit_bias`: [https://platform.openai.com/docs/api-reference/chat/create#chat/create-logit_bias](https://platform.openai.com/docs/api-reference/chat/create#chat/create-logit_bias)
#'   - `stream`: [https://platform.openai.com/docs/api-reference/chat/create#chat/create-stream](https://platform.openai.com/docs/api-reference/chat/create#chat/create-stream)
#'
#' @param prompt_role_var character vector that contains the role prompts to the ChatGPT request. Must be one of 'system', 'assistant', 'user' (default), see [https://platform.openai.com/docs/guides/chat](https://platform.openai.com/docs/guides/chat)
#' @param prompt_content_var character vector that contains the content prompts to the ChatGPT request. This is the key instruction that ChatGPT receives.
#' @param id_var (optional) character vector that contains the user-defined ids of the prompts. See details.
#' @param param_model a character vector that indicates the [ChatGPT model](https://platform.openai.com/docs/api-reference/chat/create#chat/create-model) to use; one of "gpt-3.5-turbo" (default), "gpt-3.5-turbo-0301"
#' @param param_output_type character determining the output provided: "complete" (default), "text" or "meta"
#' @param param_max_tokens numeric (default: 100) indicating the maximum number of tokens that the completion request should return (from the official API documentation: _The maximum number of tokens allowed for the generated answer. By default, the number of tokens the model can return will be (4096 - prompt tokens)._)
#' @param param_temperature numeric (default: 1.0) specifying the sampling strategy of the possible completions (from the official API documentation: _What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or `top_p` but not both._)
#' @param param_top_p numeric (default: 1) specifying sampling strategy as an alternative to the temperature sampling (from the official API documentation: _An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or `temperature` but not both._)
#' @param param_n numeric (default: 1) specifying the number of completions per request (from the official API documentation: _How many chat completion choices to generate for each input message. **Note: Because this parameter generates many completions, it can quickly consume your token quota.** Use carefully and ensure that you have reasonable settings for max_tokens and stop._)
#' @param param_stop character or character vector (default: NULL) that specifies after which character value when the completion should end (from the official API documentation: _Up to 4 sequences where the API will stop generating further tokens._)
#' @param param_presence_penalty numeric (default: 0) between -2.00  and +2.00 to determine the penalisation of repetitiveness if a token already exists (from the official API documentation: _Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics._). See also: [https://beta.openai.com/docs/api-reference/parameter-details](https://beta.openai.com/docs/api-reference/parameter-details)
#' @param param_frequency_penalty numeric (default: 0) between -2.00  and +2.00 to determine the penalisation of repetitiveness based on the frequency of a token in the text already (from the official API documentation: _Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim._). See also: [https://beta.openai.com/docs/api-reference/parameter-details](https://beta.openai.com/docs/api-reference/parameter-details)
#'
#' @return A list with two data tables (if `output_type` is the default "complete"): [[1]] contains the data table with the columns `n` (= the mo. of `n` responses requested), `prompt_role` (= the role that was set for the prompt), `prompt_content` (= the content that was set for the prompt), `chatgpt_role` (= the role that ChatGPT assumed in the chat completion) and `chatgpt_content` (= the content that ChatGPT provided with its assumed role in the chat completion). [[2]] contains the meta information of the request, including the request id, the parameters of the request and the token usage of the prompt (`tok_usage_prompt`), the completion (`tok_usage_completion`), the total usage (`tok_usage_total`) and the `id` (= the provided `id_var` or its default alternative).
#'
#' If `output_type` is "text", only the data table in slot [[1]] is returned.
#'
#' If `output_type` is "meta", only the data table in slot [[2]] is returned.
#' @examples
#' # First authenticate with your API key via `gpt3_authenticate('pathtokey')`
#'
#' # Once authenticated:
#' # Assuming you have a data.table with 3 different prompts:
#' dt_prompts = data.table::data.table('prompts_content' = c('What is the meaning if life?', 'Write a tweet about London:', 'Write a research proposal for using AI to fight fake news:')
#'     , 'prompts_role' = rep('user', 3)
#'     , 'prompt_id' = c(LETTERS[1:3]))
#'chatgpt(prompt_role_var = dt_prompts$prompts_role
#'    , prompt_content_var = dt_prompts$prompts_content
#'    , id_var = dt_prompts$prompt_id)
#'
#' ## With more controls
#' chatgpt(prompt_role_var = dt_prompts$prompts_role
#'     , prompt_content_var = dt_prompts$prompts_content
#'     , id_var = dt_prompts$prompt_id
#'     , param_max_tokens = 50
#'     , param_temperature = 0.5
#'     , param_n = 5)
#'
#' ## Reproducible example (deterministic approach)
#' chatgpt(prompt_role_var = dt_prompts$prompts_role
#'     , prompt_content_var = dt_prompts$prompts_content
#'     , id_var = dt_prompts$prompt_id
#'     , param_max_tokens = 50
#'     , param_temperature = 0
#'     , param_n = 3)
#'
#' @export
chatgpt = function(prompt_role_var
                   , prompt_content_var
                   , id_var
                   , param_output_type = 'complete'
                   , param_model = 'gpt-3.5-turbo'
                   , param_max_tokens = 100
                   , param_temperature = 1.0
                   , param_top_p = 1
                   , param_n = 1
                   , param_stop = NULL
                   , param_presence_penalty = 0
                   , param_frequency_penalty = 0){

  data_length = length(prompt_role_var)
  if(missing(id_var)){
    data_id = paste0('prompt_', 1:data_length)
  } else {
    data_id = id_var
  }

  empty_list = list()
  meta_list = list()

  for(i in 1:data_length){

    print(paste0('Request: ', i, '/', data_length))

    row_outcome = chatgpt_single(prompt_role = prompt_role_var[i]
                                 , prompt_content = prompt_content_var[i]
                                 , model = param_model
                                 , output_type = param_output_type
                                 , max_tokens = param_max_tokens
                                 , temperature = param_temperature
                                 , top_p = param_top_p
                                 , n = param_n
                                 , stop = param_stop
                                 , presence_penalty = param_presence_penalty
                                 , frequency_penalty = param_frequency_penalty)

    row_outcome[[1]]$id = data_id[i]
    row_outcome[[2]]$id = data_id[i]

    empty_list[[i]] = row_outcome[[1]]
    meta_list[[i]] = row_outcome[[2]]

  }


  bunch_core_output = try(data.table::rbindlist(empty_list), silent = T)
  if("try-error" %in% class(bunch_core_output)){
    bunch_core_output = data.table::rbindlist(empty_list, fill = T)
  }
  bunch_meta_output = try(data.table::rbindlist(meta_list), silent = T)
  if("try-error" %in% class(bunch_meta_output)){
    bunch_meta_output = data.table::rbindlist(meta_list, fill = T)
  }

  if(param_output_type == 'complete'){
    output = list(bunch_core_output
                  , bunch_meta_output)
  } else if(param_output_type == 'meta'){
    output = bunch_meta_output
  } else if(param_output_type == 'text'){
    output = bunch_core_output
  }

  return(output)
}