Build Anthropic Message Batch requests from a tibble of pairs — build_anthropic_batch

This helper converts a tibble of writing pairs into a list of Anthropic Message Batch requests. Each request has a unique custom_id of the form "ANTH_<ID1>_vs_<ID2>" and a params object compatible with the /v1/messages API.

Usage

build_anthropic_batch_requests(
  pairs,
  model,
  trait_name,
  trait_description,
  prompt_template = set_prompt_template(),
  reasoning = c("none", "enabled"),
  custom_id_prefix = "ANTH",
  ...
)

Arguments

pairs: Tibble or data frame with at least columns ID1, text1, ID2, text2. Typically created by make_pairs, sample_pairs, and randomize_pair_order.
model: Anthropic Claude model name, for example "claude-sonnet-4-5", "claude-haiku-4-5", or "claude-opus-4-5".
trait_name: Short label for the trait (for example "Overall Quality").
trait_description: Full-text description of the trait or rubric.
prompt_template: Prompt template string, typically from set_prompt_template. The template should embed your full instructions, rubric text, and <BETTER_SAMPLE> tagging convention.
reasoning: Character scalar indicating whether to allow extended thinking; one of "none" or "enabled". See details above.
custom_id_prefix: Prefix for the custom_id field. Defaults to "ANTH" so that IDs take the form "ANTH_<ID1>_vs_<ID2>".
...: Additional Anthropic parameters such as max_tokens, temperature, top_p, or thinking_budget_tokens, which will be passed through to the Messages API.

Value

A tibble with one row per pair and two main columns:

custom_id: Character ID of the form "<PREFIX>_<ID1>_vs_<ID2>".
params: List-column containing the Anthropic Messages API params object for each request, ready to be used in the requests array of /v1/messages/batches.

Details

The function mirrors the behaviour of build_openai_batch_requests but targets Anthropic's /v1/messages/batches endpoint. It applies the same recommended defaults and reasoning constraints as anthropic_compare_pair_live:

reasoning = "none":
- Default temperature = 0 (deterministic behaviour), unless you explicitly supply a different temperature via ....
- Default max_tokens = 768, unless overridden via max_tokens in ....
reasoning = "enabled" (extended thinking):
- temperature must be 1. If you supply a different value in ..., this function throws an error.
- Defaults to max_tokens = 2048 and thinking_budget_tokens = 1024, with the constraint 1024 <= thinking_budget_tokens < max_tokens. Violations of this constraint produce an error.

As a result, when you build batches without extended thinking (reasoning = "none"), the effective default temperature is 0. When you opt into extended thinking (reasoning = "enabled"), Anthropic's requirement of temperature = 1 is enforced for all batch requests.

Examples

if (FALSE) { # \dontrun{
# Requires ANTHROPIC_API_KEY and network access.
library(pairwiseLLM)

data("example_writing_samples", package = "pairwiseLLM")

pairs <- example_writing_samples |>
  make_pairs() |>
  sample_pairs(n_pairs = 3, seed = 123) |>
  randomize_pair_order(seed = 456)

td <- trait_description("overall_quality")
tmpl <- set_prompt_template()

# Standard batch requests without extended thinking
reqs_none <- build_anthropic_batch_requests(
  pairs             = pairs,
  model             = "claude-sonnet-4-5",
  trait_name        = td$name,
  trait_description = td$description,
  prompt_template   = tmpl,
  reasoning         = "none"
)

reqs_none

# Batch requests with extended thinking
reqs_reason <- build_anthropic_batch_requests(
  pairs             = pairs,
  model             = "claude-sonnet-4-5",
  trait_name        = td$name,
  trait_description = td$description,
  prompt_template   = tmpl,
  reasoning         = "enabled"
)

reqs_reason
} # }