Submit multiple chats in parallel

If you have multiple prompts, you can submit them in parallel. This is typically considerably faster than submitting them in sequence, especially with Gemini and OpenAI.

If you're using chat_openai() or chat_anthropic() and you're willing to wait longer, you might want to use batch_chat() instead, as it comes with a 50% discount in return for taking up to 24 hours.

Usage

parallel_chat(chat, prompts, max_active = 10, rpm = 500)

parallel_chat_structured(
  chat,
  prompts,
  type,
  convert = TRUE,
  include_tokens = FALSE,
  include_cost = FALSE,
  max_active = 10,
  rpm = 500
)

Arguments

chat

A base chat object.

prompts

A vector created by interpolate() or a list of character vectors.

max_active

The maximum number of simultaneous requests to send.

For chat_anthropic(), note that the number of active connections is limited primarily by the output tokens per minute limit (OTPM) which is estimated from the max_tokens parameter, which defaults to 4096. That means if your usage tier limits you to 16,000 OTPM, you should either set max_active = 4 (16,000 / 4096) to decrease the number of active connections or use params() in chat_anthropic() to decrease max_tokens.

rpm

Maximum number of requests per minute.

type

A type specification for the extracted data. Should be created with a type_() function.

convert

If TRUE, automatically convert from JSON lists to R data types using the schema. This typically works best when type is type_object() as this will give you a data frame with one column for each property. If FALSE, returns a list.

include_tokens

If TRUE, and the result is a data frame, will add input_tokens and output_tokens columns giving the total input and output tokens for each prompt.

include_cost

If TRUE, and the result is a data frame, will add cost column giving the cost of each prompt.

Value

For parallel_chat(), a list of Chat objects, one for each prompt. For parallel_chat_structured(), a single structured data object with one element for each prompt. Typically, when type is an object, this will will be a data frame with one row for each prompt, and one column for each property.

Examples

chat <- chat_openai()
#> Using model = "gpt-4.1".

# Chat ----------------------------------------------------------------------
country <- c("Canada", "New Zealand", "Jamaica", "United States")
prompts <- interpolate("What's the capital of {{country}}?")
parallel_chat(chat, prompts)
#> [[1]]
#> <Chat OpenAI/gpt-4.1 turns=2 tokens=13/10 $0.00>
#> ── user [13] ──────────────────────────────────────────────────────────
#> What's the capital of Canada?
#> ── assistant [10] ─────────────────────────────────────────────────────
#> The capital of Canada is **Ottawa**.
#> 
#> [[2]]
#> <Chat OpenAI/gpt-4.1 turns=2 tokens=14/11 $0.00>
#> ── user [14] ──────────────────────────────────────────────────────────
#> What's the capital of New Zealand?
#> ── assistant [11] ─────────────────────────────────────────────────────
#> The capital of New Zealand is **Wellington**.
#> 
#> [[3]]
#> <Chat OpenAI/gpt-4.1 turns=2 tokens=13/10 $0.00>
#> ── user [13] ──────────────────────────────────────────────────────────
#> What's the capital of Jamaica?
#> ── assistant [10] ─────────────────────────────────────────────────────
#> The capital of Jamaica is **Kingston**.
#> 
#> [[4]]
#> <Chat OpenAI/gpt-4.1 turns=2 tokens=14/14 $0.00>
#> ── user [14] ──────────────────────────────────────────────────────────
#> What's the capital of United States?
#> ── assistant [14] ─────────────────────────────────────────────────────
#> The capital of the United States is **Washington, D.C.**
#> 

# Structured data -----------------------------------------------------------
prompts <- list(
  "I go by Alex. 42 years on this planet and counting.",
  "Pleased to meet you! I'm Jamal, age 27.",
  "They call me Li Wei. Nineteen years young.",
  "Fatima here. Just celebrated my 35th birthday last week.",
  "The name's Robert - 51 years old and proud of it.",
  "Kwame here - just hit the big 5-0 this year."
)
type_person <- type_object(name = type_string(), age = type_number())
parallel_chat_structured(chat, prompts, type_person)
#>     name age
#> 1   Alex  42
#> 2  Jamal  27
#> 3 Li Wei  19
#> 4 Fatima  35
#> 5 Robert  51
#> 6  Kwame  50