Convert a chat to a solver function with structured output

generate_structured() is a variant of generate() that uses ellmer::parallel_chat_structured() to extract structured data from the model's responses. This allows you to define a schema for the expected output using ellmer's type_*() functions.

Because parallel_chat_structured() returns structured data rather than Chat objects, generate_structured() creates synthetic Chat objects for logging purposes. These "mock" chats contain the input and JSON-serialized output as turns, but won't include actual token usage or timing metadata from the API.

The result field contains JSON-serialized strings for compatibility with existing scorers. The raw structured data is available in $get_samples()$solver_metadata after calling $solve() or $eval().

Usage

generate_structured(solver_chat = NULL, type = NULL)

Arguments

solver_chat

An ellmer chat object, such as from ellmer::chat_claude(), or a zero-argument function that returns one.

type

A type specification for the extracted data, created with ellmer's type_*() functions (e.g., ellmer::type_object(), ellmer::type_string()). This defines the schema for the structured output.

Value

The output of generate() is another function. That function takes in a vector of inputs, as well as a solver chat by the name of solver_chat with the default supplied to generate() itself.

See the documentation for the solver argument in Task for more information on the return type.

See also

generate() for unstructured output, ellmer::type_object() and related functions for defining type specifications.

Examples

if (FALSE) {
  library(ellmer)

  type_answer <- type_object(
    answer = type_string(
      "The author's first name, with no other formatting."
    )
  )

  names <- tibble::tribble(
    ~input,                                  ~target,
    "Name's Josiah, how's it going?",        "Josiah",
    "I'm Lin, what's your name?",            "Lin",
    "My name is Em Fields, how about you?",  "Em"
  )

  tsk <- Task$new(
    dataset = names,
    solver = generate_structured(
      solver_chat = chat_anthropic(model = "claude-sonnet-4-20250514"),
      type = type_answer
    ),
    scorer = detect_match("any")
  )

  tsk$eval()

  # the result is JSON-serialized for compatibility with scorers
  tsk$get_samples()$result

  # raw structured data is available in solver_metadata
  tsk$get_samples()$solver_metadata

  # solver_chat contains synthetic turns for logging
  tsk$get_samples()$solver_chat[[1]]
}