dragon_chat: Talk to a model, with memory of the conversation

In dragonfarm: Fine-Tune Small Language Models with LoRA from R

Talk to a model, with memory of the conversation

Description

A conversation object that keeps the message history and sends all of it on every turn, so the model has context. Works with any backend: the local worker, Ollama, or a remote server. Transcripts use the same message format as training data, so a good conversation can become an example.

Usage

dragon_chat(
  x = NULL,
  system = NULL,
  backend = dragon_backend(),
  max_new_tokens = 256,
  temperature = 0.7,
  top_p = 0.9,
  base = FALSE,
  runs_dir = dragon_runs_dir(),
  context_window = NULL
)

dragon_chat_load(path, x = NULL, backend = dragon_backend())

Arguments

x	What answers: a dragon_run, adapter or model directory, or model id. Ignored by server backends, which serve a fixed model.
system	Optional system prompt, kept at the top of every turn.
backend	Where to run inference. See dragon_backend.
max_new_tokens, temperature, top_p	Generation settings.
base	Talk to what the run started from instead of the run.
runs_dir	Where feedback from ⁠$rate()⁠ and ⁠$edit()⁠ is recorded (under ⁠feedback/⁠). See dragon_feedback().
context_window	Approximate token budget for the conversation (system prompt plus history), such as a small model's 2K to 8K context. When the next turn would go over it, ⁠$say()⁠ drops the oldest user/assistant pair and tries again until it fits, keeping the system prompt and the most recent turns. NULL (the default) never drops turns. The token count is a rough estimate (about 4 characters per token), not the model's own tokenizer.
path	A transcript written by ⁠$save()⁠.

Value

A dragon_chat object with methods: ⁠$say(text, on_token = NULL)⁠ sends a user turn and returns the reply (streaming pieces to on_token when the backend supports it); ⁠$history()⁠ returns the messages; ⁠$reset()⁠ clears them; ⁠$undo()⁠ drops the last exchange; ⁠$regenerate()⁠ asks again for the last reply; ⁠$rate("up")⁠ or ⁠$rate("down")⁠ records a verdict on the last reply and ⁠$edit(text)⁠ replaces it with a better one, both saved as feedback that dragon_feedback() turns into training data; ⁠$context_usage()⁠ reports the estimated tokens used, the window, and how many turns have been dropped to stay under it (NULL when context_window is not set); ⁠$save(path)⁠ and dragon_chat_load(path) write and read a transcript; ⁠$as_example()⁠ returns the conversation as one training row.

Examples

if (interactive()) {
chat <- dragon_chat(run, system = "You are a concise support agent.")
chat$say("My thermostat keeps dropping off Wi-Fi.")
chat$say("I tried that. What else?")   # the model sees the first exchange
chat$history()
chat$save("good-conversation.json")
}

dragonfarm documentation built on Oct. 12, 2026, 5:07 p.m.