man/gpt3_completions.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gpt3_completions.R
\name{gpt3_completions}
\alias{gpt3_completions}
\title{Makes bunch completion requests to the GPT-3 API}
\usage{
gpt3_completions(
  prompt_var,
  id_var,
  param_output_type = "complete",
  param_model = "text-davinci-003",
  param_suffix = NULL,
  param_max_tokens = 100,
  param_temperature = 0.9,
  param_top_p = 1,
  param_n = 1,
  param_logprobs = NULL,
  param_stop = NULL,
  param_presence_penalty = 0,
  param_frequency_penalty = 0,
  param_best_of = 1
)
}
\arguments{
\item{prompt_var}{character vector that contains the prompts to the GPT-3 request}

\item{id_var}{(optional) character vector that contains the user-defined ids of the prompts. See details.}

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

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

\item{param_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{param_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{param_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{param_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{param_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{param_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{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: \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{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: \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{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: \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{param_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), \code{gpt3} (= the completion as returned from the GPT-3 model) and \code{id} (= the provided \code{id_var} or its default alternative). [\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}), the total usage (\code{tok_usage_total}), and the \code{id} (= the provided \code{id_var} or its default alternative).

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_completions()} is the package's main function for rquests and takes as input a vector of prompts and processes each prompt as per the defined parameters. It extends the \code{gpt3_single_completion()} function to allow for bunch processing of requests to the Open AI GPT-3 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 GPT-3 and a prompt id (see examples below).
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: The \code{gpt3_single_completion()} (which is used here in a vectorised manner) handles the issue that best_of must be greater than n by setting \code{if(best_of <= n){ best_of = n}}.

If \code{id_var} is not provided, the function will use \code{prompt_1} ... \code{prompt_n} as id variable.

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:
# Assuming you have a data.table with 3 different prompts:
dt_prompts = data.table::data.table('prompts' = c('What is the meaning if life?', 'Write a tweet about London:', 'Write a research proposal for using AI to fight fake news:'), 'prompt_id' = c(LETTERS[1:3]))
gpt3_completions(prompt_var = dt_prompts$prompts
   , id_var = dt_prompts$prompt_id)

## With more controls
gpt3_completions(prompt_var = dt_prompts$prompts
   , id_var = dt_prompts$prompt_id
   , param_max_tokens = 50
   , param_temperature = 0.5
   , param_n = 5)

## Reproducible example (deterministic approach)
gpt3_completions(prompt_var = dt_prompts$prompts
   , id_var = dt_prompts$prompt_id
   , param_max_tokens = 50
   , param_temperature = 0.0)

## Changing the GPT-3 model
gpt3_completions(prompt_var = dt_prompts$prompts
   , id_var = dt_prompts$prompt_id
   , param_model = 'text-babbage-001'
   , param_max_tokens = 50
   , param_temperature = 0.4)
}