diff --git a/R/bunch_embedding.R b/R/bunch_embedding.R
index 29e3dc6..d22a3ac 100644
--- a/R/bunch_embedding.R
+++ b/R/bunch_embedding.R
@@ -1,11 +1,48 @@
-gpt3.bunch_embedding = function(data
-                                , text_var
+#' Retrieves text embeddings for character input from a vector from the GPT-3 API
+#'
+#' @description
+#' `gpt3_bunch_embedding()` extends the single embeddings function `gpt3_make_embedding()` to allow for the processing of a whole vector
+#' @details The returned data.table contains the column `id` which indicates the text id (or its generic alternative if not specified) and the columns `dim_1` ... `dim_{max}`, where `max` is the length of the text embeddings vector that the four different models return. For the default "Ada" model, these are 1024 dimensions (i.e., `dim_1`... `dim_1024`).
+#'
+#' 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_var character vector that contains the texts for which you want to obtain text embeddings from the GPT-3 model
+#' #' @param id_var (optional) character vector that contains the user-defined ids of the prompts. See details.
+#' @param 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 data.table with the embeddings as separate columns; one row represents one input text. See details.
+#' @examples
+#' # First authenticate with your API key via `gpt3_authenticate('pathtokey')`
+#'
+#' # Use example data:
+#' ## The data below were generated with the `gpt3_make_request()` function as follows:
+#' ##### DO NOT RUN #####
+#' # travel_blog_data = gpt3_make_request(prompt_input = "Write a travel blog about a dog's journey through the UK:", temperature = 0.8, n = 10, max_tokens = 200)[[1]]
+#' ##### END DO NOT RUN #####
+#'
+#' # You can load these data with:
+#' data("travel_blog_data") # the dataset contains 10 completions for the above request
+#'
+#' ## Obtain text embeddings for the completion texts:
+#' emb_travelblogs = gpt3_bunch_embedding(input_var = travel_blog_data$gpt3)
+#' dim(emb_travelblogs)
+#' @export
+gpt3_bunch_embedding = function(input_var
                                 , id_var
                                 , param_model = 'text-similarity-ada-001'){
 
-  data_ = data
-
-  data_length = data_[, .N]
+  data_length = length(input_var)
+  if(missing(id_var)){
+    data_id = paste0('prompt_', 1:data_length)
+  } else {
+    data_id = id_var
+  }
 
   empty_list = list()
 
@@ -13,12 +50,12 @@
 
     print(paste0('Embedding: ', i, '/', data_length))
 
-    row_outcome = gpt3.make_embedding(model_ = param_model
-                                      , input_ = as.character(unname(data_[i, ..text_var])))
+    row_outcome = gpt3_make_embedding(model = param_model
+                                      , input = input_var[i])
 
     empty_df = data.frame(t(row_outcome))
     names(empty_df) = paste0('dim_', 1:length(row_outcome))
-    empty_df$id_full = as.character(unname(data_[i, ..id_var]))
+    empty_df$id = data_id[i]
 
     empty_list[[i]] = empty_df