ben-aaron188 / rgpt3 Goto Github PK

dt_prompts = data.table::data.table('prompts_content' = chatPrompts$ChatGPT_Prompt, 'prompts_role' = chatPrompts$prompts_role,'prompt_id' = chatPrompts$PromptId)
  gptResponse <- chatgpt(prompt_role_var = dt_prompts$prompts_role
          , prompt_content_var = dt_prompts$prompts_content
          , id_var = dt_prompts$prompt_id, param_max_tokens=10000, param_n=5)

Error in core_output$chatgpt_role[i] <- request_content$choices[[i]]$message$role : 
replacement has length zero

example_1 = gpt3_single_completion(prompt_input = "Write a cynical text about human nature"
                                   , temperature = 0.7
                                   , max_tokens = 25,
                                   , n = 1)

> example_1
[[1]]
   n                                  prompt
1: 1 Write a cynical text about human nature

[[2]]
                              param_prompt      param_model param_max_tokens param_temperature param_top_p
1: Write a cynical text about human nature text-davinci-003               25               0.7           1
   param_n param_presence_penalty param_frequency_penalty param_best_of
1:       1                      0                       0             1

To avoid an `invalid_request_error`, `best_of` was set to equal `n`
Error in core_output$gpt3[i] <- request_content$choices[[i]]$text : 
  replacement has length zero

[1] "Will use --> sk-xxxxxx for authentication."
[2] "Will use -->  for authentication."   

tweets <- c(
  "Just spilled coffee on myself, not off to a great start",
  "Feeling productive and motivated today!",
  "Got stuck in traffic this morning, already stressed out",
  "Excited to try that new lunch spot everyone's been talking about",
  "Woke up feeling refreshed and ready to take on the day",
  "Feeling overwhelmed with work and deadlines",
  "Finally finished that big project I've been working on for weeks!",
  "Forgot my lunch at home, having a hungry day",
  "Had a great meeting with my boss this morning, feeling appreciated",
  "Feeling under the weather, hoping to get some rest later",
  "Just got a promotion, feeling on top of the world!",
  "Feeling discouraged after getting some negative feedback at work",
  "Met up with an old friend for lunch, feeling grateful for good company",
  "Feeling tired and unmotivated today, need some coffee ASAP",
  "Spent the morning in back-to-back meetings, feeling drained",
  "Received some good news today, feeling optimistic",
  "Feeling stressed about upcoming deadlines, but trying to stay focused",
  "Having a great day so far, feeling productive and energized",
  "Feeling disappointed after not getting that job I applied for",
  "Celebrating a coworker's birthday today, feeling festive"
)

# create a data frame with one column called "tweets"
tweet_data <- data.frame(tweets)

# add a column called "promptID" that contains the row number

tweet_data <- tweet_data %>%
  mutate(promptID = paste0(row_number()))

# Create the root prompt for GPT to apply to each data point
the_prompt_prefix <- "Categorise this tweet whether the tweeter would be happy or not. 
                      Respond with just [positive/negative/neutral] ["
the_prompt_postfix <- "]"

# Make the API request, using the prompts from the data frame
# Note that we are using a different model here, text-ada-001
# Different models cost different amounts of API credits!!!
GPT_tweet_sentiment <- gpt3_completions(prompt_var = paste0(the_prompt_prefix, our_date_data$Date, the_prompt_postfix),
                              id_var = our_date_data$promptID,
                              param_model = 'text-davinci-003',
                              param_max_tokens = 100,
                              param_n = 1,
                              param_temperature = 0.4,
                              )

# use dplyr mutate to add the GPT-3 responses to the data frame
tweet_data <- tweet_data %>%
  mutate(GPT_tweet_sentiment = GPT_tweet_sentiment[[1]]$gpt3)

# Print the data frame
tweet_data```