LLMR Response Object

A lightweight S3 container for generative model calls. It standardizes finish reasons and token usage across providers and keeps the raw response for advanced users.

Returns the standardized finish reason for an llmr_response.

Returns a list with token counts for an llmr_response.

Convenience check for truncation due to token limits.

Usage

finish_reason(x)

tokens(x)

is_truncated(x)

# S3 method for class 'llmr_response'
as.character(x, ...)

# S3 method for class 'llmr_response'
print(x, ...)

Arguments

x

An llmr_response object.

...

Ignored.

Value

A length-1 character vector or NA_character_.

A list list(sent, rec, total, reasoning, cached). Missing values are NA. cached counts prompt tokens the provider read from its cache (cheaper than fresh input tokens); it is NA for providers that do not report cache usage.

TRUE if truncated, otherwise FALSE.

Details

Fields

• text: character scalar. Assistant reply.

• provider: character. Provider id (e.g., "openai", "gemini").

• model: character. Model id as requested in the config.

• model_version: character. The model identifier the server reports having served (e.g., a dated snapshot). Useful for reproducibility records; NA when the provider does not echo it.

• finish_reason: one of "stop", "length", "filter", "tool", "other".

• usage: list with integers sent, rec, total, reasoning, and cached (tokens read from the provider's prompt cache; NA when not reported).

• thinking: character. Reasoning text when the provider returns it separately (e.g., Anthropic thinking blocks, Gemini thought parts, DeepSeek reasoning_content); NA otherwise.

• response_id: provider's response identifier if present.

• duration_s: numeric seconds from request to parse.

• raw: parsed provider JSON (list).

• raw_json: raw JSON string.

Printing

print() shows the text, then a compact status line with model, finish reason, token counts, and a terse hint if truncated or filtered.

Coercion

as.character() extracts text so the object remains drop-in for code that expects a character return.

See also

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

Examples

# Minimal fabricated example (no network):
r <- structure(
  list(
    text = "Hello!",
    provider = "openai",
    model = "demo",
    finish_reason = "stop",
    usage = list(sent = 12L, rec = 5L, total = 17L, reasoning = NA_integer_),
    response_id = "resp_123",
    duration_s = 0.012,
    raw = list(choices = list(list(message = list(content = "Hello!")))),
    raw_json = "{}"
  ),
  class = "llmr_response"
)
as.character(r)
finish_reason(r)
tokens(r)
print(r)
r <- structure(list(text="hi", model="demo", finish_reason="stop",
  usage=list(sent=1L, rec=1L, total=2L, reasoning=NA_integer_, cached=NA_integer_),
  duration_s=0.01), class="llmr_response")
finish_reason(r)
r <- structure(list(text="hi", model="demo", finish_reason="stop",
  usage=list(sent=1L, rec=1L, total=2L, reasoning=NA_integer_, cached=NA_integer_),
  duration_s=0.01), class="llmr_response")
tokens(r)
r <- structure(list(text="hi", model="demo", finish_reason="stop",
  usage=list(sent=1L, rec=1L, total=2L, reasoning=NA_integer_, cached=NA_integer_),
  duration_s=0.01), class="llmr_response")
is_truncated(r)