finishCode {perplexR}

Large Language Model: Finish code

Description

Large Language Model: Finish code

Usage

finishCode(
  code = clipr::read_clip(allow_non_interactive = TRUE),
  PERPLEXITY_API_KEY = Sys.getenv("PERPLEXITY_API_KEY"),
  modelSelection = c("mistral-7b-instruct", "mixtral-8x7b-instruct",
    "codellama-70b-instruct", "sonar-small-chat", "sonar-small-online",
    "sonar-medium-chat", "sonar-medium-online"),
  systemRole = "You are a helpful assistant with extensive programming skills.",
  maxTokens = 265,
  temperature = 1,
  top_p = NULL,
  top_k = 100,
  presence_penalty = 0,
  frequency_penalty = NULL,
  proxy = NULL
)

Arguments

code

The code to be completed by Large Language Model. If not provided, it will use what's copied on the clipboard.

PERPLEXITY_API_KEY

PERPLEXITY API key.

modelSelection

model choice. Default is mistral-7b-instruct.

systemRole

Role for model. Default is: "You are a helpful assistant with extensive knowledge of R programming."

maxTokens

The maximum integer of completion tokens returned by API.

temperature

The amount of randomness in the response, valued between 0 inclusive and 2 exclusive. Higher values are more random, and lower values are more deterministic. Set either temperature or top_p.

top_p

Nucleus sampling threshold, valued between 0 and 1 inclusive.

top_k

The number of tokens to keep for highest top-k filtering, specified as an integer between 0 and 2048 inclusive. If set to 0, top-k filtering is disabled.

presence_penalty

A value 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. Incompatible with frequency_penalty.

frequency_penalty

A multiplicative penalty greater than 0. Values greater than 1.0 penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. A value of 1.0 means no penalty.

proxy

Default value is NULL.

Value

A character value with the response generated by Large Language Model.

Examples

## Not run: 
finishCode("# A function to square each element of a vector\nsquare_each <- function(")

## End(Not run)