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. Use setup_llm_parallel() when you want explicit control over workers.

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), the config list-column (so llm_par_resume() can re-run failures), provider, model, all model parameters, response_text, raw_response_json, success, error_message, and the other diagnostics documented in call_llm_par().

Parallel Workflow

Recommended workflow:

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. If the active future plan is sequential, call_llm_par() temporarily switches to multisession for the duration of the call.

See also

setup_llm_parallel, reset_llm_parallel, call_llm_par, llm_fn, llm_mutate

Examples

if (FALSE) { # \dontrun{
  # Broadcast different questions
  config <- llm_config(provider = "groq", model = "openai/gpt-oss-20b")

  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)
} # }