mall (version 0.1.0)

llm_extract: Extract entities from text

Description

Use a Large Language Model (LLM) to extract specific entity, or entities, from the provided text

Usage

llm_extract(
  .data,
  col,
  labels,
  expand_cols = FALSE,
  additional_prompt = "",
  pred_name = ".extract"
)
llm_vec_extract(x, labels = c(), additional_prompt = "", preview = FALSE)

Value

llm_extract returns a data.frame or tbl object. llm_vec_extract 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 vector with the entities to extract from the text

expand_cols

If multiple labels are passed, this is a flag that tells the function to create a new column per item in labels. If labels is a named vector, this function will use those names as the new column names, if not, the function will use a sanitized version of the content as the name.

additional_prompt

Inserts this text into the prompt sent to the LLM

pred_name

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

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)

# Use 'labels' to let the function know what to extract
llm_extract(reviews, review, labels = "product")

# Use 'pred_name' to customize the new column's name
llm_extract(reviews, review, "product", pred_name = "prod")

# Pass a vector to request multiple things, the results will be pipe delimeted
# in a single column
llm_extract(reviews, review, c("product", "feelings"))

# To get multiple columns, use 'expand_cols'
llm_extract(reviews, review, c("product", "feelings"), expand_cols = TRUE)

# Pass a named vector to set the resulting column names
llm_extract(
  .data = reviews,
  col = review,
  labels = c(prod = "product", feels = "feelings"),
  expand_cols = TRUE
)

# For character vectors, instead of a data frame, use this function
llm_vec_extract("bob smith, 123 3rd street", c("name", "address"))

# To preview the first call that will be made to the downstream R function
llm_vec_extract(
  "bob smith, 123 3rd street",
  c("name", "address"),
  preview = TRUE
)
# }