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+#' Obtains text embeddings for a single character (string) from the GPT-3 API
+#'
+#' @description
+#' `gpt_single_embedding()` sends a single [embedding request](https://beta.openai.com/docs/guides/embeddings) 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.
+#'   - Ada (1024 dimensions)
+#'   - Babbage (2048 dimensions)
+#'   - Curie (4096 dimensions)
+#'   - Davinci (12288 dimensions)
+#'
+#' Note that the dimension size (= vector length), speed and [associated costs](https://openai.com/api/pricing/) differ considerably.
+#'
+#' These vectors can be used for downstream tasks such as (vector) similarity calculations.
+#' @param input character that contains the text for which you want to obtain text embeddings from the GPT-3 model
+#' @param model a character vector that indicates the [similarity embedding model](https://beta.openai.com/docs/guides/embeddings/similarity-embeddings); one of "text-similarity-ada-001" (default), "text-similarity-curie-001", "text-similarity-babbage-001", "text-similarity-davinci-001"
+#' @return A numeric vector (= the embedding vector)
+#' @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')
+#' @export
+gpt_single_embedding = function(input
+                               , model = 'text-similarity-ada-001'
+                               ){
+
+  parameter_list = list(model = model
+                        , input = input)
+
+  request_base = httr::POST(url = url.embeddings
+                            , body = parameter_list
+                            , httr::add_headers(Authorization = paste("Bearer", api_key))
+                            , encode = "json")
+
+
+  output_base = httr::content(request_base)
+
+  embedding_raw = to_numeric(unlist(output_base$data[[1]]$embedding))
+
+  return(embedding_raw)
+
+}