LLMR (version 0.6.0)

call_llm_sweep: Parallel API calls: Parameter Sweep - Vary One Parameter, Fixed Message

Description

Sweeps through different values of a single parameter while keeping the message constant. Perfect for hyperparameter tuning, temperature experiments, etc. This function requires setting up the parallel environment using setup_llm_parallel.

Usage

call_llm_sweep(base_config, param_name, param_values, messages, ...)

Value

A tibble with columns: swept_param_name, the varied parameter column, provider, model, all other model parameters, response_text, raw_response_json, success, error_message.

Arguments

base_config

Base llm_config object to modify.

param_name

Character. Name of the parameter to vary (e.g., "temperature", "max_tokens").

param_values

Vector. Values to test for the parameter.

messages

A character vector or a list of message objects (same for all calls).

...

Additional arguments passed to call_llm_par (e.g., tries, verbose, progress).

Parallel Workflow

All parallel functions require the future backend to be configured. The recommended workflow is:

1. Call setup_llm_parallel() once at the start of your script.

2. Run one or more parallel experiments (e.g., call_llm_broadcast()).

3. Call reset_llm_parallel() at the end to restore sequential processing.

See Also

setup_llm_parallel, reset_llm_parallel

Examples

if (FALSE) {
  # Temperature sweep
  config <- llm_config(provider = "openai", model = "gpt-4.1-nano")

  messages <- "What is 15 * 23?"
  temperatures <- c(0, 0.3, 0.7, 1.0, 1.5)

  setup_llm_parallel(workers = 4, verbose = TRUE)
  results <- call_llm_sweep(config, "temperature", temperatures, messages)
  results |> dplyr::select(temperature, response_text)
  reset_llm_parallel(verbose = TRUE)
}