R/gpt3_single_completion.R

#' Makes a single completion request to the GPT-3 API
#'
#' @description
#' `gpt3_single_completion()` sends a single [completion request](https://beta.openai.com/docs/api-reference/completions) to the Open AI GPT-3 API.
#' @details For a general guide on the completion requests, see [https://beta.openai.com/docs/guides/completion](https://beta.openai.com/docs/guides/completion). This function provides you with an R wrapper to send requests with the full range of request parameters as detailed on [https://beta.openai.com/docs/api-reference/completions](https://beta.openai.com/docs/api-reference/completions) and reproduced below.
#'
#' For the `best_of` parameter: When used with n, best_of controls the number of candidate completions and n specifies how many to return – best_of must be greater than n. Note that this is handled by the wrapper automatically   if(best_of <= n){ best_of = n}.
#'
#' Parameters not included/supported:
#'   - `logit_bias`: [https://beta.openai.com/docs/api-reference/completions/create#completions/create-logit_bias](https://beta.openai.com/docs/api-reference/completions/create#completions/create-logit_bias)
#'   - `echo`: [https://beta.openai.com/docs/api-reference/completions/create#completions/create-echo](https://beta.openai.com/docs/api-reference/completions/create#completions/create-echo)
#'   - `stream`: [https://beta.openai.com/docs/api-reference/completions/create#completions/create-stream](https://beta.openai.com/docs/api-reference/completions/create#completions/create-stream)
#'
#' @param prompt_input character that contains the prompt to the GPT-3 request
#' @param model a character vector that indicates the [model](https://beta.openai.com/docs/models/gpt-3) to use; one of "text-davinci-003" (default), "text-davinci-002", "text-davinci-001", "text-curie-001", "text-babbage-001" or "text-ada-001"
#' @param output_type character determining the output provided: "complete" (default), "text" or "meta"
#' @param suffix character (default: NULL) (from the official API documentation: _The suffix that comes after a completion of inserted text_)
#' @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 to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096)_)
#' @param temperature numeric (default: 0.9) specifying the sampling strategy of the possible completions (from the official API documentation: _What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend altering this or top_p but not both._)
#' @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 n numeric (default: 1) specifying the number of completions per request (from the official API documentation: _How many completions to generate for each prompt. **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 logprobs numeric (default: NULL) (from the official API documentation: _Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 5, the API will return a list of the 5 most likely tokens. The API will always return the logprob of the sampled token, so there may be up to logprobs+1 elements in the response. The maximum value for logprobs is 5. If you need more than this, please go to [https://help.openai.com/en/](https://help.openai.com/en/) and describe your use case._)
#' @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. The returned text will not contain the stop sequence._)
#' @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 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)
#' @param best_of numeric (default: 1) that determines the space of possibilities from which to select the completion with the highest probability (from the official API documentation: _Generates `best_of` completions server-side and returns the "best" (the one with the highest log probability per token)_). See 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` (= the prompt that was sent), and `gpt3` (= the completion as returned from the GPT-3 model). [[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`) and the total usage (`tok_usage_total`).
#'
#' 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:
#'
#' ## Simple request with defaults:
#' gpt3_single_completion(prompt_input = 'How old are you?')
#'
#' ## Instruct GPT-3 to write ten research ideas of max. 150 tokens with some controls:
#'gpt3_single_completion(prompt_input = 'Write a research idea about using text data to understand human behaviour:'
#'    , temperature = 0.8
#'    , n = 10
#'    , max_tokens = 150)
#'
#' ## For fully reproducible results, we need `temperature = 0`, e.g.:
#' gpt3_single_completion(prompt_input = 'Finish this sentence:/n There is no easier way to learn R than'
#'     , temperature = 0.0
#'     , max_tokens = 50)
#'
#' ## The same example with a different GPT-3 model:
#' gpt3_single_completion(prompt_input = 'Finish this sentence:/n There is no easier way to learn R than'
#'     , model = 'text-babbage-001'
#'     , temperature = 0.0
#'     , max_tokens = 50)
#' @export
gpt3_single_completion = function(prompt_input
                              , model = 'text-davinci-003'
                              , output_type = 'complete'
                              , suffix = NULL
                              , max_tokens = 100
                              , temperature = 0.9
                              , top_p = 1
                              , n = 1
                              , logprobs = NULL
                              , stop = NULL
                              , presence_penalty = 0
                              , frequency_penalty = 0
                              , best_of = 1){

  #check for request issues with `n` and `best_of`
  if(best_of < n){
    best_of = n
    message('To avoid an `invalid_request_error`, `best_of` was set to equal `n`')
  }

  if(temperature == 0 & n > 1){
    n = 1
    message('You are running the deterministic model, so `n` was set to 1 to avoid unnecessary token quota usage.')
  }

  parameter_list = list(prompt = prompt_input
                        , model = model
                        , suffix = suffix
                        , max_tokens = max_tokens
                        , temperature = temperature
                        , top_p = top_p
                        , n = n
                        , logprobs = logprobs
                        , stop = stop
                        , presence_penalty = presence_penalty
                        , frequency_penalty = frequency_penalty
                        , best_of = best_of)

  request_base = httr::POST(url = url.completions
                            , body = parameter_list
                            , httr::add_headers(Authorization = paste("Bearer", api_key))
                            , encode = "json")

  request_content = httr::content(request_base)
  # request_content = httr::content(request_base, encoding = "Latin-ASCII")

  if(request_base$status_code != 200){
    warning(paste0("Request completed with error. Code: ", request_base$status_code
                  , ", message: ", request_content$error$message))
  }

  if(n == 1){
    core_output = data.table::data.table('n' = 1
                                         , 'prompt' = prompt_input
                                         , 'gpt3' = request_content$choices[[1]]$text)
  } else if(n > 1){

    core_output = data.table::data.table('n' = 1:n
                                         , 'prompt' = rep(prompt_input, n)
                                         , 'gpt3' = rep("", n))

    for(i in 1:n){
      core_output$gpt3[i] = request_content$choices[[i]]$text
    }

  }


  meta_output = data.table::data.table('request_id' = request_content$id
                           , 'object' = request_content$object
                           , 'model' = request_content$model
                           , 'param_prompt' = prompt_input
                           , 'param_model' = model
                           , 'param_suffix' = suffix
                           , 'param_max_tokens' = max_tokens
                           , 'param_temperature' = temperature
                           , 'param_top_p' = top_p
                           , 'param_n' = n
                           , 'param_logprobs' = logprobs
                           , 'param_stop' = stop
                           , 'param_presence_penalty' = presence_penalty
                           , 'param_frequency_penalty' = frequency_penalty
                           , 'param_best_of' = best_of
                           , 'tok_usage_prompt' = request_content$usage$prompt_tokens
                           , 'tok_usage_completion' = request_content$usage$completion_tokens
                           , 'tok_usage_total' = request_content$usage$total_tokens)

  if(output_type == 'complete'){
    output = list(core_output
                  , meta_output)
  } else if(output_type == 'meta'){
    output = meta_output
  } else if(output_type == 'text'){
    output = core_output
  }

  return(output)

}