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rtemis.llm (version 0.8.1)

generate: Generate Method

Description

Generic method for generating text or structured output from LLMs and Agents.

Usage

generate(
  x,
  prompt,
  temperature = NULL,
  top_p = NULL,
  max_tokens = NULL,
  stop = NULL,
  think = NULL,
  output_schema = NULL,
  verbosity = 1L,
  ...
)

Value

Message object or list of Message objects (for Agent).

Arguments

x

An object of class LLM or Agent.

prompt

Character: The prompt to pass to the model or agent.

temperature

Optional numeric [0, 2]: Per-call sampling temperature.

top_p

Optional numeric [0, 1]: Nucleus sampling cutoff.

max_tokens

Optional integer [1, Inf): Maximum tokens to generate. For Anthropic, this overrides the config-level value (which is required); for Ollama this maps to options.num_predict; for OpenAI-compatible backends this maps to max_tokens.

stop

Optional character: Stop sequence(s). Mapped to stop_sequences on Anthropic and options.stop on Ollama.

think

Optional logical or character: Whether to enable model thinking (reasoning trace) for this call. Character values target gpt-oss-style local models.

output_schema

Optional Schema: Output schema to enforce on this call's response. If omitted, the object's default schema (if any) is used.

verbosity

Integer: Verbosity level.

...

Additional backend-specific per-call arguments. See Details.

Author

EDG

Details

The system prompt is set once at agent (or LLM) construction time and is not overridable per call. Construct a new agent if you need a different system prompt.

Backend-specific extra arguments accepted via ...:

  • Ollama: top_k (integer), seed (integer)

  • OpenAI: seed (integer)

  • Anthropic: top_k (integer)

Any argument set to NULL (the default) falls back to the value baked into the underlying LLMConfig at construction time.

Examples

Run this code
# Requires running Ollama server and gemma4:e4b model
if (FALSE) {
  agent <- create_agent(
    config_Ollama(
      model_name = "gemma4:e4b",
      temperature = 0.2
    )
  )
  generate(agent, "What is your name?", temperature = 0.7)
}

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