The send_batch() function allows you to send a list of LLMMessage objects
to an API.
It routes the input to the appropriate provider-specific batch API function.
send_batch(
.llms,
.provider = getOption("tidyllm_sbatch_default"),
.dry_run = NULL,
.temperature = NULL,
.timeout = NULL,
.top_p = NULL,
.max_tries = NULL,
.model = NULL,
.verbose = NULL,
.json_schema = NULL,
.seed = NULL,
.stop = NULL,
.frequency_penalty = NULL,
.presence_penalty = NULL,
.id_prefix = NULL
)An updated and named list of .llms with identifiers that align with batch responses, including a batch_id attribute.
A list of LLMMessage objects containing conversation histories.
A function or function call specifying the language model provider and any additional parameters.
This should be a call to a provider function like openai(), claude(), etc.
You can also set a default provider function via the tidyllm_sbatch_default option.
Logical; if TRUE, simulates the request without sending it to the provider. Useful for testing.
Numeric; controls the randomness of the model's output (0 = deterministic).
Numeric; the maximum time (in seconds) to wait for a response.
Numeric; nucleus sampling parameter, which limits the sampling to the top cumulative probability p.
Integer; the maximum number of retries for failed requests.
Character; the model identifier to use (e.g., "gpt-4").
Logical; if TRUE, prints additional information about the request and response.
List; A JSON schema object as R list to enforce the output structure
Integer; sets a random seed for reproducibility.
Character vector; specifies sequences where the model should stop generating further tokens.
Numeric; adjusts the likelihood of repeating tokens (positive values decrease repetition).
Numeric; adjusts the likelihood of introducing new tokens (positive values encourage novelty).
Character string to specify a prefix for generating custom IDs when names in .llms are missing