The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

hellmer

hellmer enables sequential or parallel batch processing for chat models from ellmer.

Overview

Process multiple chat interactions with:

Tooling and structured data extraction
State persistence and recovery
Progress tracking
Configurable output verbosity
Automatic retry with backoff
Timeout handling
Sound notifications

Installation

You can install hellmer from CRAN with:

install.packages("hellmer")

Setup API Keys

ellmer will look for API keys in your environmental variables. I recommend the usethis package to setup API keys in your .Renviron such as OPENAI_API_KEY=your-key.

usethis::edit_r_environ(scope = c("user", "project"))

Basic Usage

Sequential Processing

library(hellmer)

chat <- chat_sequential(chat_openai, 
                        system_prompt = "Reply concisely, one sentence")

prompts <- list(
  "What is R?",
  "Explain base R versus tidyverse",
  "Explain vectors, lists, and data frames",
  "How do environments work in R?",
  "Compare R and Python for data analysis",
  "Explain lazy evaluation in R",
  "What are R's apply functions?",
  "How do R packages work?",
  "Explain R's object-oriented programming systems.",
  "What are closures in R?",
  "Describe R memory management",
  "How does R handle missing values?",
  "Explain R's integration with C++",
  "Compare dplyr and data.table approaches",
  "What are R formulas and when to use them?"
)
result <- chat$batch(prompts)

result$progress()
result$texts()
result$chats()

Parallel Processing

chat <- chat_future(chat_openai, 
                    system_prompt = "Reply concisely, one sentence")

Performance vs Safety Trade-Off

When using parallel processing with chat_future, there’s a trade-off between performance and safety:

Maximum Performance: Setting chunk_size equal to the number of prompts results in a 4-5x faster processing speed

chat$batch(prompts, chunk_size = length(prompts))

Maximum Safety: Using a smaller chunk_size ensures state is saved more frequently, allowing recovery if something goes wrong (default: number of prompts / 10)

Naming Note

chat_future isn’t named chat_parallel because the latter will be included in ellmer (#143).

Features

Tooling

square_number <- function(num) num^2

chat$register_tool(tool(
  square_number,
  "Calculates the square of a given number",
  num = type_integer("The number to square")
))

prompts <- list(
  "What is the square of 3?",
  "Calculate the square of 5."
)

Structured Data Extraction

Extract structured data using type specifications:

type_sentiment <- type_object(
  "Extract sentiment scores",
  positive_score = type_number("Positive sentiment score, 0.0 to 1.0"),
  negative_score = type_number("Negative sentiment score, 0.0 to 1.0"),
  neutral_score = type_number("Neutral sentiment score, 0.0 to 1.0")
)

prompts <- list(
  "I love this product! It's amazing!",
  "This is okay, nothing special.",
  "Terrible experience, very disappointed."
)

result <- chat$batch(prompts, type_spec = type_sentiment)
structured_data <- result$structured_data()

State Management

Batch processing automatically saves state and can resume interrupted operations:

result <- chat$batch(prompts, state_path = "chat_state.rds")

If state_path is not defined, a temporary file will be created by default.

Output Control

Control verbosity with the echo parameter (sequential only):

"none": Silent operation with progress bar
"text": Show chat responses only
"all": Show both prompts and responses

chat <- chat_sequential(
  chat_openai, 
  echo = "none"
)

Automatic Retry

Automatically retry failed requests with backoff, which serves as a wide guardrail against errors while ellmer and httr2 serve as a narrow guardrail against specific API limits:

chat <- chat_sequential(
  chat_openai,         # Ellmer chat model
  max_retries = 3,     # Maximum retry attempts
  initial_delay = 20,  # Initial delay in seconds
  max_delay = 60,      # Maximum delay between retries
  backoff_factor = 2   # Multiply delay by this factor after each retry
)

If a request fails, the code will:

Wait for the initial_delay
Retry the request
If it fails again, wait for (delay × backoff_factor)
Continue until success or max_retries is reached

If the code detects an authorization or API key issue, it will stop immediately.

Timeout Handling

The timeout parameter specifies the maximum time to wait for a response from the chat model for each prompt. However, this parameter is still limited by the timeouts propagated up from the chat model functions.

chat <- chat_future(
  chat_openai,
  system_prompt = "Reply concisely, one sentence"
  timeout = 60
)

Sound Notifications

Toggle sound notifications on batch completion, interruption, and error:

chat <- chat_sequential(
  chat_openai,
  beep = TRUE
)

Results Methods

texts(): Returns response texts in the same format as the input prompts (i.e., a list if prompts were provided as a list, or a character vector if prompts were provided as a vector)
chats(): Returns a list of chat objects
progress(): Returns processing statistics
structured_data(): Returns extracted structured data (if type_spec is provided)