Summarize the narrative output with ChatGPT

Usage

summarize_narrative(
  narrative,
  prompt = "Summarize the following narrative to make it shorter:",
  openai_api_key = Sys.getenv("OPENAI_API_KEY"),
  max_tokens = 1024,
  temperature = 0,
  top_p = 1,
  frequency_penalty = 0,
  presence_penalty = 0
)

Arguments

narrative: List of narratives returned by narrate_* function, character vector or string that will be enhanced by ChatGPT
prompt: Prompt to send to OpenAI API
openai_api_key: Your OpenAI API key, you can set it up in .Renviron file as "OPENAI_API_KEY", function will look for it with Sys.getenv("OPENAI_API_KEY")
max_tokens: The maximum number of tokens to generate in the chat completion.
temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
top_p: 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.
frequency_penalty: 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.
presence_penalty: 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.

Value

character() of narratives enhanced by ChatGPT

Examples

if (FALSE) {
narrative <- sales %>%
dplyr::filter(Product %in% c("Tools", "Clothing", "Home")) %>%
  dplyr::group_by(Product, Region)  %>%
  dplyr::summarise(Sales = sum(Sales)) %>%
  narrate_descriptive()

narrative <- enhance_narrative(narrative)
summarize_narrative(narrative)
}