mall (version 0.1.0)

llm_classify: Categorize data as one of options given

Description

Use a Large Language Model (LLM) to classify the provided text as one of the options provided via the labels argument.

Usage

llm_classify(
  .data,
  col,
  labels,
  pred_name = ".classify",
  additional_prompt = ""
)
llm_vec_classify(x, labels, additional_prompt = "", preview = FALSE)

Value

llm_classify returns a data.frame or tbl object. llm_vec_classify returns a vector that is the same length as x.

Arguments

.data

A data.frame or tbl object that contains the text to be analyzed

col

The name of the field to analyze, supports tidy-eval

labels

A character vector with at least 2 labels to classify the text as

pred_name

A character vector with the name of the new column where the prediction will be placed

additional_prompt

Inserts this text into the prompt sent to the LLM

x

A vector that contains the text to be analyzed

preview

It returns the R call that would have been used to run the prediction. It only returns the first record in x. Defaults to FALSE Applies to vector function only.

Examples

# \donttest{
library(mall)

data("reviews")

llm_use("ollama", "llama3.2", seed = 100, .silent = TRUE)

llm_classify(reviews, review, c("appliance", "computer"))

# Use 'pred_name' to customize the new column's name
llm_classify(
  reviews,
  review,
  c("appliance", "computer"),
  pred_name = "prod_type"
)

# Pass custom values for each classification
llm_classify(reviews, review, c("appliance" ~ 1, "computer" ~ 2))

# For character vectors, instead of a data frame, use this function
llm_vec_classify(
  c("this is important!", "just whenever"),
  c("urgent", "not urgent")
)

# To preview the first call that will be made to the downstream R function
llm_vec_classify(
  c("this is important!", "just whenever"),
  c("urgent", "not urgent"),
  preview = TRUE
)
# }