README

LLMR offers a unified interface for Large Language Models in R. It supports multiple providers, robust retries, structured output, and embeddings.

Installation

install.packages("LLMR") # CRAN
# Development:
# remotes::install_github("asanaei/LLMR")

Quick start

Configure a model

library(LLMR)

cfg <- llm_config(
provider = "openai",
model = "gpt-4o-mini",
temperature = 0.2,
max_tokens = 256
)

Store keys in environment variables such as OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY.

One-shot generation

r <- call_llm(
config = cfg,
messages = c(
system = "You are a branding expert.",
user = "Six-word catch-phrase for eco-friendly balloons.")
)

print(r) # text + status line
as.character(r) # just the text
finish_reason(r)
tokens(r)
is_truncated(r)

Structured output (JSON with schema)

schema <- list(
type = "object",
properties = list(
label = list(type = "string"),
score = list(type = "number")
),
required = list("label","score"),
additionalProperties = FALSE
)

cfg_s <- enable_structured_output(cfg, schema = schema)

resp <- call_llm(cfg_s, c(system="Reply JSON only.", user="Label and score for 'MNIST'."))
parsed <- llm_parse_structured(resp)
str(parsed)

words <- c("excellent","awful","fine")

out <- llm_fn_structured(
x = words,
prompt = "Classify '{x}' and output {label, score in [0,1]} as JSON.",
.config = cfg,
.schema = schema,
.fields = c("label","score")
)
out

Embeddings

sentences <- c(
one="Quiet rivers mirror bright skies.",
two="Thunder shakes the mountain path."
)

emb_cfg <- llm_config(
provider = "voyage",
model = "voyage-large-2",
embedding = TRUE
)

emb <- call_llm(emb_cfg, sentences) |> parse_embeddings()
dim(emb)

emb <- get_batched_embeddings(
texts = sentences,
embed_config = emb_cfg,
batch_size = 8
)

Conversation with history

chat <- chat_session(cfg, system = "You teach statistics tersely.")
chat$send("Explain p-values in 12 words.")
chat$send("Now give a three-word analogy.")
print(chat)

Parallel runs

setup_llm_parallel(workers = 4)

experiments <- build_factorial_experiments(
configs = list(cfg),
user_prompts = c("Summarize in one sentence: The Apollo program."),
system_prompts= "Be concise."
)

res <- call_llm_par(experiments, progress = TRUE)
reset_llm_parallel()

LLMR