LLMR (version 0.6.0)

llm_chat_session: Chat Session Object and Methods

Description

Create and interact with a stateful chat session object that retains message history. This documentation page covers the constructor function chat_session() as well as all S3 methods for the llm_chat_session class.

Usage

chat_session(config, system = NULL, ...)
# S3 method for llm_chat_session
as.data.frame(x, ...)
# S3 method for llm_chat_session
summary(object, ...)
# S3 method for llm_chat_session
head(x, n = 6L, width = getOption("width") - 15, ...)
# S3 method for llm_chat_session
tail(x, n = 6L, width = getOption("width") - 15, ...)
# S3 method for llm_chat_session
print(x, width = getOption("width") - 15, ...)

Value

For chat_session(), an object of class llm_chat_session. Other methods return what their titles state.

Arguments

config

An llm_config for a generative model (embedding = FALSE).

system

Optional system prompt inserted once at the beginning.

...

Default arguments forwarded to every call_llm_robust() call (e.g. verbose = TRUE).

x, object

An llm_chat_session object.

n

Number of turns to display.

width

Character width for truncating long messages.

How it works

1. A private environment stores the running list of list(role, content) messages.

2. At each $send() the history is sent in full to the model.

3. Provider-agnostic token counts are extracted from the JSON response.

Public methods

$send(text, ..., role = "user")

Append a message (default role "user"), query the model, print the assistant's reply, and invisibly return it.

$send_structured(text, schema, ..., role = "user", .fields = NULL, .validate_local = TRUE)

Send a message with structured-output enabled using schema, append the assistant's reply, parse JSON (and optionally validate locally when .validate_local = TRUE), returning the parsed result invisibly.

$history()

Raw list of messages.

$history_df()

Two-column data frame (role, content).

$tokens_sent()/$tokens_received()

Running token totals.

$reset()

Clear history (retains the optional system message).

Details

The chat_session object provides a simple way to hold a conversation with a generative model. It wraps call_llm_robust() to benefit from retry logic, caching, and error logging.

See Also

llm_config(), call_llm(), call_llm_robust(), llm_fn(), llm_mutate()

Examples

if (interactive()) {
  cfg  <- llm_config("openai", "gpt-4o-mini")
  chat <- chat_session(cfg, system = "Be concise.")
  chat$send("Who invented the moon?")
  chat$send("Explain why in one short sentence.")
  chat           # print() shows a summary and first 10 turns
  summary(chat)  # stats
  tail(chat, 2)
  as.data.frame(chat)
}