Convert a chat to a solver function

generate() is the simplest possible solver one might use with vitals; it just passes its inputs to the supplied model and returns its raw responses. The inputs are evaluated in parallel, not in the sense of multiple R sessions, but in the sense of multiple, asynchronous HTTP requests using ellmer::parallel_chat(). generate()'s output can be passed directory to the solver argument of Task's $new() method.

Usage

generate(solver_chat = NULL)

Arguments

solver_chat

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

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_structured() for structured output extraction.

Examples

if (!identical(Sys.getenv("ANTHROPIC_API_KEY"), "")) {
  # set the log directory to a temporary directory
  withr::local_envvar(VITALS_LOG_DIR = withr::local_tempdir())

  library(ellmer)
  library(tibble)

  simple_addition <- tibble(
    input = c("What's 2+2?", "What's 2+3?"),
    target = c("4", "5")
  )

  # create a new Task
  tsk <- Task$new(
    dataset = simple_addition,
    solver = generate(chat_claude(model = "claude-sonnet-4-5-20250929")),
    scorer = model_graded_qa()
  )

  # evaluate the task (runs solver and scorer) and opens
  # the results in the Inspect log viewer (if interactive)
  tsk$eval()

  # $eval() is shorthand for:
  tsk$solve()
  tsk$score()
  tsk$measure()
  tsk$log()
  tsk$view()

  # get the evaluation results as a data frame
  tsk$get_samples()

  # view the task directory with $view() or vitals_view()
  vitals_view()
}
#> ℹ Solving
#> ✔ Solving [1.9s]
#> 
#> ℹ Scoring
#> [working] (0 + 0) -> 1 -> 1 | ■■■■■■■■■■■■■■■■                  50%
#> [working] (0 + 0) -> 0 -> 2 | ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100%
#> ℹ Scoring

#> ✔ Scoring [3s]
#> 
#> [working] (0 + 0) -> 1 -> 1 | ■■■■■■■■■■■■■■■■                  50%
#> [working] (0 + 0) -> 0 -> 2 | ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100%
#> ✔ Inspect Viewer running at: <http://127.0.0.1:26308>
#> ✔ Inspect Viewer running at: <http://127.0.0.1:21035>

# The `input` column can be a list of 1-row tibbles for per-sample metadata.
# Custom solvers can then extract columns from each input:
shapes_data <- tibble::tibble(
  input = list(
    tibble::tibble(shapes = "square, circle, rhombus", pick = "square"),
    tibble::tibble(shapes = "square, circle, rhombus", pick = "circle")
  ),
  target = c("square", "circle")
)

my_solver <- function(solver_chat = NULL) {
  chat <- solver_chat
  function(inputs, ..., solver_chat = chat) {
    ch <- if (is.function(solver_chat)) solver_chat() else solver_chat$clone()
    prompts <- lapply(inputs, function(inp) {
      paste0("Always pick ", inp$pick, ". Return only that shape.\n\n", inp$shapes)
    })
    res <- ellmer::parallel_chat(ch, prompts, ...)
    list(result = purrr::map_chr(res, \(c) c$last_turn()@text), solver_chat = res)
  }
}