type_boolean: Type specifications

In ellmer: Chat with Large Language Models

Type specifications

Description

These functions specify object types in a way that chatbots understand and are used for tool calling and structured data extraction. Their names are based on the JSON schema, which is what the APIs expect behind the scenes. The translation from R concepts to these types is fairly straightforward.

• type_boolean(), type_integer(), type_number(), and type_string() each represent scalars. These are equivalent to length-1 logical, integer, double, and character vectors (respectively).

• type_enum() is equivalent to a length-1 factor; it is a string that can only take the specified values.

• type_array() is equivalent to a vector in R. You can use it to represent an atomic vector: e.g. type_array(items = type_boolean()) is equivalent to a logical vector and type_array(items = type_string()) is equivalent to a character vector). You can also use it to represent a list of more complicated types where every element is the same type (R has no base equivalent to this), e.g. type_array(items = type_array(items = type_string())) represents a list of character vectors.

• type_object() is equivalent to a named list in R, but where every element must have the specified type. For example, type_object(a = type_string(), b = type_array(type_integer())) is equivalent to a list with an element called a that is a string and an element called b that is an integer vector.

Usage

type_boolean(description = NULL, required = TRUE)

type_integer(description = NULL, required = TRUE)

type_number(description = NULL, required = TRUE)

type_string(description = NULL, required = TRUE)

type_enum(description = NULL, values, required = TRUE)

type_array(description = NULL, items, required = TRUE)

type_object(
  .description = NULL,
  ...,
  .required = TRUE,
  .additional_properties = FALSE
)

Arguments

description, .description	The purpose of the component. This is used by the LLM to determine what values to pass to the tool or what values to extract in the structured data, so the more detail that you can provide here, the better.
required, .required	Is the component required? If FALSE, and the component does not exist in the data, the LLM may hallucinate a value. Only applies when the element is nested inside of a type_object().
values	Character vector of permitted values.
items	The type of the array items. Can be created by any of the type_ function.
...	Name-type pairs defineing the components that the object must possess.
.additional_properties	Can the object have arbitrary additional properties that are not explicitly listed? Only supported by Claude.

Examples

# An integer vector
type_array(items = type_integer())

# The closest equivalent to a data frame is an array of objects
type_array(items = type_object(
   x = type_boolean(),
   y = type_string(),
   z = type_number()
))

# There's no specific type for dates, but you use a string with the
# requested format in the description (it's not gauranteed that you'll
# get this format back, but you should most of the time)
type_string("The creation date, in YYYY-MM-DD format.")
type_string("The update date, in dd/mm/yyyy format.")

ellmer documentation built on April 4, 2025, 3:53 a.m.