batch: Batch result class for managing chat processing results

In hellmer: Batch Processing for Chat Models

Batch result class for managing chat processing results

Description

Batch result class for managing chat processing results

Usage

batch(
  prompts = list(),
  responses = list(),
  completed = integer(0),
  state_path = character(0),
  type_spec = NULL,
  judgements = integer(0),
  progress = logical(0),
  input_type = character(0),
  max_retries = integer(0),
  initial_delay = integer(0),
  max_delay = integer(0),
  backoff_factor = integer(0),
  chunk_size = integer(0),
  workers = integer(0),
  plan = character(0),
  beep = logical(0),
  echo = logical(0),
  state = list()
)

Arguments

prompts	List of prompts to process
responses	List to store responses
completed	Integer indicating number of completed prompts
state_path	Path to save state file
type_spec	Type specification for structured data extraction
judgements	Number of judgements in a batch_judge() workflow (1 = initial extract + 1 judgement, 2 = initial extract + 2 judgements, etc.)
progress	Whether to show progress bars (default: TRUE)
input_type	Type of input ("vector" or "list")
max_retries	Maximum number of retry attempts
initial_delay	Initial delay before first retry
max_delay	Maximum delay between retries
backoff_factor	Factor to multiply delay by after each retry
chunk_size	Size of chunks for parallel processing
workers	Number of parallel workers
plan	Parallel backend plan
beep	Play sound on completion (default: TRUE)
echo	Whether to echo messages during processing (default: FALSE)
state	Internal state tracking

Value

Returns an S7 class object of class "batch" that represents a collection of prompts and their responses from chat models. The object contains all input parameters as properties and provides methods for:

• Extracting text responses via texts() (includes structured data when a type specification is provided)

• Accessing full chat objects via chats()

• Tracking processing progress via progress()

The batch object manages prompt processing, tracks completion status, and handles retries for failed requests.

Examples

# Create a chat processor
chat <- chat_sequential(chat_openai())

# Process a batch of prompts
batch <- chat$batch(list(
  "What is R?",
  "Explain base R versus tidyverse",
  "Explain vectors, lists, and data frames"
))

# Check the progress if interrupted
batch$progress()

# Return the responses as a vector or list
batch$texts()

# Return the chat objects
batch$chats()

hellmer documentation built on June 8, 2025, 11:09 a.m.