Parallel API calls: Fixed Config, Multiple Messages

Broadcasts different messages using the same configuration in parallel. Perfect for batch processing different prompts with consistent settings. This function requires setting up the parallel environment using setup_llm_parallel.

Usage

call_llm_broadcast(config, messages, ...)

Arguments

config

Single llm_config object to use for all calls.

messages

A character vector (each element is a prompt) OR a list where each element is a pre-formatted message list.

...

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

Value

A tibble with columns: message_index (metadata), provider, model, all model parameters, response_text, raw_response_json, success, error_message.

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) { # \dontrun{
  # Broadcast different questions
  config <- llm_config(provider = "openai", model = "gpt-4.1-nano")

  messages <- list(
    list(list(role = "user", content = "What is 2+2?")),
    list(list(role = "user", content = "What is 3*5?")),
    list(list(role = "user", content = "What is 10/2?"))
  )

  setup_llm_parallel(workers = 4, verbose = TRUE)
  results <- call_llm_broadcast(config, messages)
  reset_llm_parallel(verbose = TRUE)
} # }