lares (version 5.2.13)

gpt_ask: ChatGPT API Interaction with R

Description

This function lets the user ask ChatGPT via its API, and returns the rendered reply. There are a couple of specific verbs (functions) with a preset prompt to help fetch the data in specific formats. We also store the prompts and replies in current session with their respective time-stamps so user can gather historical results.

Usage

gpt_ask(
  ask,
  secret_key = get_credentials()$openai$secret_key,
  url = Sys.getenv("LARES_GPT_URL"),
  model = Sys.getenv("LARES_GPT_MODEL"),
  num_retries = 3,
  temperature = 0.5,
  max_tokens = NULL,
  pause_base = 1,
  quiet = FALSE,
  ...
)
gpt_history(quiet = TRUE, ...)
gpt_table(x, cols = NULL, quiet = TRUE, ...)
gpt_classify(x, categories, quiet = TRUE, ...)
gpt_tag(x, tags, quiet = TRUE, ...)
gpt_extract(x, extract, quiet = TRUE, ...)
gpt_format(x, format, quiet = TRUE, ...)
gpt_convert(x, unit, quiet = TRUE, ...)
gpt_translate(x, language, quiet = TRUE, ...)

Value

(Invisible) list. Content returned from API POST and processed.

Arguments

ask

Character. Redacted prompt to ask. If multiple asks are requested, they will be concatenated with "+" into a single request.

secret_key

Character. Secret Key. Get yours in: platform.openai.com for OpenAI or makersuite.google.com/app/apikey for Gemini.

url

Character. Base API URL.

model

Character. OpenAI model to use. This can be adjusted according to the available models in the OpenAI API (such as "gpt-4").

num_retries

Integer. Number of times to retry the request in case of failure. Default is 3.

temperature

Numeric. The temperature to use for generating the response. Default is 0.5. The lower the temperature, the more deterministic the results in the sense that the highest probable next token is always picked. Increasing temperature could lead to more randomness, which encourages more diverse or creative outputs. You are essentially increasing the weights of the other possible tokens. In terms of application, you might want to use a lower temperature value for tasks like fact-based QA to encourage more factual and concise responses. For poem generation or other creative tasks, it might be beneficial to increase the temperature value.

max_tokens

Integer. The maximum number of tokens in the response.

pause_base

Numeric. The number of seconds to wait between retries. Default is 1.

quiet

Boolean. Keep quiet? If not, message will be shown.

...

Additional parameters.

x

Vector. List items you wish to process in your instruction

cols

Vector. Force column names for your table results.

categories, tags

Vector. List of possible categories/tags to consider.

extract, format, unit

Character. Length 1 or same as x to extract/format/unit information from x. For example: email, country of phone number, country, amount as number, currency ISO code, ISO, Fahrenheit, etc.

language

Character. Language to translate to

See Also

Other API: bring_api(), fb_accounts(), fb_ads(), fb_creatives(), fb_insights(), fb_process(), fb_report_check(), fb_rf(), fb_token(), gemini_ask(), li_auth(), li_profile(), queryGA(), slackSend()

Other ChatGPT: gpt_prompter()

Other LLM: gemini_ask(), gpt_prompter()

Examples

if (FALSE) {
api_key <- get_credentials()$openai$secret_key
# Open question:
gpt_ask("Can you write an R function to plot a dummy histogram?", api_key)

##### The following examples return dataframes:
# Classify each element based on categories:
gpt_classify(1:10, c("odd", "even"))

# Add all tags that apply to each element based on tags:
gpt_tag(
  c("I love chocolate", "I hate chocolate", "I like Coke"),
  c("food", "positive", "negative", "beverage")
)

# Extract specific information:
gpt_extract(
  c("My mail is [e-mail]", "30 Main Street, Brooklyn, NY, USA", "[phone]", "$1.5M"),
  c("email", "full state name", "country of phone number", "amount as number")
)

# Format values
gpt_format(
  c("March 27th, 2021", "12-25-2023 3:45PM", "01.01.2000", "29 Feb 92"),
  format = "ISO Date getting rid of time stamps"
)

# Convert temperature units
gpt_convert(c("50C", "300K"), "Fahrenheit")

# Create a table with data
gpt_table("5 random people's address in South America, email, phone, age between 18-30")
gpt_table(
  ask = "5 largest cities, their countries, and population",
  cols = c("city_name", "where", "POP")
)

# Translate text to any language
gpt_translate(
  rep("I love you with all my heart", 5),
  language = c("spanish", "chinese", "japanese", "russian", "german")
)

# Now let's read the historical prompts, replies, ano more from current session
gpt_history()
}