tidyllm (version 0.6.0)

send_chat: Start a chat without waiting for it

Description

Sends one request and returns immediately with a job handle. The session stays usable while the model works; use check_job() for its status, get_partial() for the text so far, and fetch_job() for the finished LLMMessage, which is exactly what chat() would have returned.

Usage

send_chat(
  .llm,
  .provider = getOption("tidyllm_chat_default"),
  .on_chunk = NULL,
  .stream = TRUE,
  .dry_run = NULL,
  .temperature = NULL,
  .timeout = NULL,
  .top_p = NULL,
  .max_tries = NULL,
  .model = NULL,
  .verbose = NULL,
  .json_schema = NULL,
  .tools = NULL,
  .max_tool_rounds = NULL,
  .seed = NULL,
  .stop = NULL,
  .frequency_penalty = NULL,
  .presence_penalty = NULL
)

Value

A tidyllm_chat_job.

Arguments

.llm

An LLMMessage object.

.provider

A provider function call, as in chat().

.on_chunk

Optional function of one argument, called with each text delta as it arrives. This is the push form of a stream; \(d) cat(d) echoes to the console, and writing to a reactiveVal is all a Shiny app needs. Only meaningful with .stream = TRUE.

.stream

Logical; whether the provider streams the reply. Streaming is what makes .on_chunk and get_partial() show progress. A non-streaming job still runs without blocking; it simply has nothing to report until it finishes.

.dry_run

Logical; if TRUE, simulates the request without sending it to the provider. Useful for testing.

.temperature

Numeric; controls the randomness of the model's output (0 = deterministic).

.timeout

Numeric; the maximum time (in seconds) to wait for a response.

.top_p

Numeric; nucleus sampling parameter, which limits the sampling to the top cumulative probability p.

.max_tries

Integer; the maximum number of retries for failed requests.

.model

Character; the model identifier to use (e.g., "gpt-4").

.verbose

Logical; if TRUE, prints additional information about the request and response.

.json_schema

List; A JSON schema object as R list to enforce the output structure

.tools

Either a single TOOL object or a list of TOOL objects representing the available functions for tool calls.

.max_tool_rounds

Integer; the maximum number of tool use iterations for multi-turn tool calling (default varies by provider).

.seed

Integer; sets a random seed for reproducibility.

.stop

Character vector; specifies sequences where the model should stop generating further tokens.

.frequency_penalty

Numeric; adjusts the likelihood of repeating tokens (positive values decrease repetition).

.presence_penalty

Numeric; adjusts the likelihood of introducing new tokens (positive values encourage novelty).

Details

The request is driven from R's event loop rather than from a thread or a second process, so progress happens whenever the session yields, which the accessors do on your behalf. The consequence worth knowing is the other side of that: a blocking call of your own, a long Sys.sleep() or another chat(), pauses the job for its duration.

A streamed job is not retried after a transient 429 or 503 the way chat() is, because it is read from an open connection; a non-streamed one keeps the usual retries.

Requires the later package, and promises as well when .stream = FALSE.

Examples

if (FALSE) {
job <- llm_message("Summarise the history of R in 500 words") |>
  send_chat(claude(), .stream = TRUE)

while (check_job(job) == "running") {
  cat("\r", nchar(get_partial(job)), "characters so far")
}

reply <- fetch_job(job)
}