updated function names
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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gpt3_single_embedding.R
+\name{gpt_single_embedding}
+\alias{gpt_single_embedding}
+\title{Obtains text embeddings for a single character (string) from the GPT-3 API}
+\usage{
+gpt_single_embedding(input, model = "text-similarity-ada-001")
+}
+\arguments{
+\item{input}{character that contains the text for which you want to obtain text embeddings from the GPT-3 model}
+
+\item{model}{a character vector that indicates the \href{https://beta.openai.com/docs/guides/embeddings/similarity-embeddings}{similarity embedding model}; one of "text-similarity-ada-001" (default), "text-similarity-curie-001", "text-similarity-babbage-001", "text-similarity-davinci-001"}
+}
+\value{
+A numeric vector (= the embedding vector)
+}
+\description{
+\code{gpt_single_embedding()} sends a single \href{https://beta.openai.com/docs/guides/embeddings}{embedding request} to the Open AI GPT-3 API.
+}
+\details{
+The function supports the text similarity embeddings for the four GPT-3 models as specified in the parameter list. The main difference between the four models is the sophistication of the embedding representation as indicated by the vector embedding size.
+\itemize{
+\item Ada (1024 dimensions)
+\item Babbage (2048 dimensions)
+\item Curie (4096 dimensions)
+\item Davinci (12288 dimensions)
+}
+
+Note that the dimension size (= vector length), speed and \href{https://openai.com/api/pricing/}{associated costs} differ considerably.
+
+These vectors can be used for downstream tasks such as (vector) similarity calculations.
+}
+\examples{
+# First authenticate with your API key via `gpt3_authenticate('pathtokey')`
+
+# Once authenticated:
+
+## Simple request with defaults:
+sample_string = "London is one of the most liveable cities in the world. The city is always full of energy and people. It's always a great place to explore and have fun."
+gpt_single_embedding(input = sample_string)
+
+## Change the model:
+#' gpt_single_embedding(input = sample_string
+ , model = 'text-similarity-curie-001')
+}