LLMR (version 0.6.3)

llm_config: Create an LLM configuration (provider-agnostic)

Description

llm_config() builds a provider-agnostic configuration object that call_llm() (and friends) understand. You can pass provider-specific parameters via ...; LLMR forwards them as-is, with a few safe conveniences.

Usage

llm_config(
  provider,
  model,
  api_key = NULL,
  troubleshooting = FALSE,
  base_url = NULL,
  embedding = NULL,
  no_change = FALSE,
  ...
)

Value

An object of class c("llm_config", provider). Fields: provider, model, api_key, troubleshooting, embedding, no_change, and model_params (a named list of extras).

Arguments

provider

Character scalar. One of: "openai", "anthropic", "gemini", "groq", "together", "voyage" (embeddings only), "deepseek", "xai", "ollama".

model

Character scalar. Model name understood by the chosen provider. (e.g., "gpt-4o-mini", "o4-mini", "claude-3.7", "gemini-2.0-flash", etc.)

api_key

Character scalar. Provider API key.

troubleshooting

Logical. If TRUE, prints the full request payloads (including your API key!) for debugging. Use with extreme caution.

base_url

Optional character. Back-compat alias; if supplied it is stored as api_url in model_params and overrides the default endpoint.

embedding

NULL (default), TRUE, or FALSE. If TRUE, the call is routed to the provider's embeddings API; if FALSE, to the chat API. If NULL, LLMR infers embeddings when model contains "embedding".

no_change

Logical. If TRUE, LLMR never auto-renames/adjusts provider parameters. If FALSE (default), well-known compatibility shims may apply (e.g., renaming OpenAI's max_tokens → max_completion_tokens after a server hint; see call_llm() notes).

...

Additional provider-specific parameters (e.g., temperature, top_p, max_tokens, top_k, repetition_penalty, reasoning_effort, api_url, etc.). Values are forwarded verbatim unless documented shims apply. For Anthropic extended thinking, supply thinking_budget (canonical; mapped to thinking.budget_tokens) together with include_thoughts = TRUE to request the thinking block in the response.

Temperature range clamping

Anthropic temperatures must be in [0, 1]; others in [0, 2]. Out-of-range values are clamped with a warning.

Endpoint overrides

You can pass api_url (or base_url= alias) in ... to point to gateways or compatible proxies.

See Also

call_llm, call_llm_robust, llm_chat_session, call_llm_par, get_batched_embeddings

Examples

if (FALSE) {
# Basic OpenAI config
cfg <- llm_config("openai", "gpt-4o-mini",
temperature = 0.7, max_tokens = 300)

# Generative call returns an llmr_response object
r <- call_llm(cfg, "Say hello in Greek.")
print(r)
as.character(r)

# Embeddings (inferred from the model name)
e_cfg <- llm_config("gemini", "text-embedding-004")

# Force embeddings even if model name does not contain "embedding"
e_cfg2 <- llm_config("voyage", "voyage-large-2", embedding = TRUE)
}