Talk to a model, with memory of the conversation

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")
}