Parallel API calls: Multiple Configs, Fixed Message

Compares different configurations (models, providers, settings) using the same message. Perfect for benchmarking across different models or providers. This function requires setting up the parallel environment using setup_llm_parallel.

Usage

call_llm_compare(configs_list, messages, ...)

Arguments

configs_list

A list of llm_config objects to compare.

messages

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

...

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

Value

A tibble with columns: config_index (metadata), provider, model, all varying 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{
  # Compare different models
  config1 <- llm_config(provider = "openai", model = "gpt-4o-mini")
  config2 <- llm_config(provider = "openai", model = "gpt-4.1-nano")

  configs_list <- list(config1, config2)
  messages <- "Explain quantum computing"

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