## ----------------------------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", eval = identical(tolower(Sys.getenv("LLMR_RUN_VIGNETTES", "false")), "true") ) ## ----------------------------------------------------------------------------- # library(LLMR) # # cfg_openai <- llm_config( # provider = "openai", # model = "gpt-5-nano", # # ) # # chat_oai <- chat_session(cfg_openai, system = "Be concise.") # chat_oai$send("Say a warm hello in one short sentence.") # chat_oai$send("Now say it in Esperanto.") ## ----------------------------------------------------------------------------- # cfg_anthropic <- llm_config( # provider = "anthropic", # model = "claude-sonnet-4-20250514", # max_tokens = 512 # avoid warnings; Anthropic requires max_tokens # ) # # chat_claude <- chat_session(cfg_anthropic, system = "Be concise.") # chat_claude$send("Name one interesting fact about honey bees.") ## ----------------------------------------------------------------------------- # cfg_gemini <- llm_config( # provider = "gemini", # model = "gemini-2.5-flash-lite", # # ) # # chat_gem <- chat_session(cfg_gemini, system = "Be concise.") # chat_gem$send("Give me a single-sentence fun fact about volcanoes.") ## ----------------------------------------------------------------------------- # cfg_groq <- llm_config( # provider = "groq", # model = "openai/gpt-oss-20b", # # ) # # chat_groq <- chat_session(cfg_groq, system = "Be concise.") # chat_groq$send("Share a short fun fact about octopuses.") ## ----------------------------------------------------------------------------- # chat_oai$send("What did I ask you to do in my first message?") # # The model can reference the earlier "Say a warm hello" request ## ----------------------------------------------------------------------------- # # View all messages # as.data.frame(chat_oai) # # # Get summary statistics # summary(chat_oai) ## ----------------------------------------------------------------------------- # schema <- list( # type = "object", # properties = list( # answer = list(type = "string"), # confidence = list(type = "number") # ), # required = list("answer", "confidence"), # additionalProperties = FALSE # ) # # chat_oai$send_structured( # "Return an answer and a confidence score (0-1) about: Why is the sky blue?", # schema # )