man/gpt3_single_completion.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gpt3_single_completion.R
\name{gpt3_single_completion}
\alias{gpt3_single_completion}
\title{Makes a single completion request to the GPT-3 API}
\usage{
gpt3_single_completion(
  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
)
}
\arguments{
\item{prompt_input}{character that contains the prompt to the GPT-3 request}

\item{model}{a character vector that indicates the \href{https://beta.openai.com/docs/models/gpt-3}{model} to use; one of "text-davinci-003" (default), "text-davinci-002", "text-davinci-001", "text-curie-001", "text-babbage-001" or "text-ada-001"}

\item{output_type}{character determining the output provided: "complete" (default), "text" or "meta"}

\item{suffix}{character (default: NULL) (from the official API documentation: \emph{The suffix that comes after a completion of inserted text})}

\item{max_tokens}{numeric (default: 100) indicating the maximum number of tokens that the completion request should return (from the official API documentation: \emph{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)})}

\item{temperature}{numeric (default: 0.9) specifying the sampling strategy of the possible completions (from the official API documentation: \emph{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.})}

\item{top_p}{numeric (default: 1) specifying sampling strategy as an alternative to the temperature sampling (from the official API documentation: \emph{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.})}

\item{n}{numeric (default: 1) specifying the number of completions per request (from the official API documentation: \emph{How many completions to generate for each prompt. \strong{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.})}

\item{logprobs}{numeric (default: NULL) (from the official API documentation: \emph{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 \url{https://help.openai.com/en/} and describe your use case.})}

\item{stop}{character or character vector (default: NULL) that specifies after which character value when the completion should end (from the official API documentation: \emph{Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.})}

\item{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: \emph{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: \url{https://beta.openai.com/docs/api-reference/parameter-details}}

\item{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: \emph{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: \url{https://beta.openai.com/docs/api-reference/parameter-details}}

\item{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: \emph{Generates \code{best_of} completions server-side and returns the "best" (the one with the highest log probability per token)}). See details.}
}
\value{
A list with two data tables (if \code{output_type} is the default "complete"): [\link{1}] contains the data table with the columns \code{n} (= the mo. of \code{n} responses requested), \code{prompt} (= the prompt that was sent), and \code{gpt3} (= the completion as returned from the GPT-3 model). [\link{2}] contains the meta information of the request, including the request id, the parameters of the request and the token usage of the prompt (\code{tok_usage_prompt}), the completion (\code{tok_usage_completion}) and the total usage (\code{tok_usage_total}).

If \code{output_type} is "text", only the data table in slot [\link{1}] is returned.

If \code{output_type} is "meta", only the data table in slot [\link{2}] is returned.
}
\description{
\code{gpt3_single_completion()} sends a single \href{https://beta.openai.com/docs/api-reference/completions}{completion request} to the Open AI GPT-3 API.
}
\details{
For a general guide on the completion requests, see \url{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 \url{https://beta.openai.com/docs/api-reference/completions} and reproduced below.

For the \code{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:
\itemize{
\item \code{logit_bias}: \url{https://beta.openai.com/docs/api-reference/completions/create#completions/create-logit_bias}
\item \code{echo}: \url{https://beta.openai.com/docs/api-reference/completions/create#completions/create-echo}
\item \code{stream}: \url{https://beta.openai.com/docs/api-reference/completions/create#completions/create-stream}
}
}
\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)
}