## -----------------------------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE, comment = "#>",
  eval = identical(tolower(Sys.getenv("LLMR_RUN_VIGNETTES", "false")), "true")
)

## -----------------------------------------------------------------------------
# library(LLMR)
# library(dplyr)
# 
# cfg_openai <- llm_config("openai",   "gpt-5-nano")
# cfg_cld    <- llm_config("anthropic","claude-sonnet-4-20250514", max_tokens = 512)  # avoid warnings; Anthropic requires max_tokens
# cfg_gemini <- llm_config("gemini",   "gemini-2.5-flash")
# cfg_groq   <- llm_config("groq",     "openai/gpt-oss-20b")

## -----------------------------------------------------------------------------
# words <- c("excellent", "awful", "fine")
# out <- llm_fn(
#   words,
#   prompt  = "Classify '{x}' as Positive, Negative, or Neutral.",
#   .config = cfg_openai,
#   .return = "columns"
# )
# out

## -----------------------------------------------------------------------------
# out_groq <- llm_fn(
#   words,
#   prompt  = "Classify '{x}' as Positive, Negative, or Neutral.",
#   .config = cfg_groq,
#   .return = "columns"
# )
# out_groq

## -----------------------------------------------------------------------------
# schema <- list(
#   type = "object",
#   properties = list(
#     label = list(type = "string", description = "Sentiment label"),
#     score = list(type = "number", description = "Confidence 0..1")
#   ),
#   required = list("label", "score"),
#   additionalProperties = FALSE
# )
# 
# out_s <- llm_fn_structured(
#   x = words,
#   prompt  = "Classify '{x}' as Positive, Negative, or Neutral with confidence.",
#   .config = cfg_openai,
#   .schema = schema,
#   .fields = c("label", "score")
# )
# out_s

## -----------------------------------------------------------------------------
# df <- tibble::tibble(
#   id   = 1:3,
#   text = c("Cats are great pets", "The weather is bad", "I like tea")
# )
# 
# df_u <- df |>
#   llm_mutate(
#     answer  = "Give a short category for: {text}",
#     .config = cfg_cld,
#     .return = "columns"
#   )
# 
# df_u

## -----------------------------------------------------------------------------
# df |>
#   llm_mutate(
#     category = "Give a short category for: {text}",
#     .config = cfg_cld
#   )
# # Equivalent to: llm_mutate(category, prompt = "Give...", .config = cfg_cld)

## -----------------------------------------------------------------------------
# df |>
#   llm_mutate(
#     classified = c(
#       system = "You are a text classifier. One word only.",
#       user = "Category for: {text}"
#     ),
#     .config = cfg_openai
#   )

## -----------------------------------------------------------------------------
# schema <- list(
#   type = "object",
#   properties = list(
#     category = list(type = "string"),
#     confidence = list(type = "number")
#   ),
#   required = list("category", "confidence")
# )
# 
# # Using .structured = TRUE (equivalent to calling llm_mutate_structured)
# df |>
#   llm_mutate(
#     structured_result = "{text}",
#     .config = cfg_openai,
#     .structured = TRUE,
#     .schema = schema
#   )

## -----------------------------------------------------------------------------
# schema2 <- list(
#   type = "object",
#   properties = list(
#     category  = list(type = "string"),
#     rationale = list(type = "string")
#   ),
#   required = list("category", "rationale"),
#   additionalProperties = FALSE
# )
# 
# # Traditional call
# df_s <- df |>
#   llm_mutate_structured(
#     annot,
#     prompt  = "Extract category and a one-sentence rationale for: {text}",
#     .config = cfg_gemini,
#     .schema = schema2
#     # Because a schema is present, fields auto-hoist; you can also pass:
#     # .fields = c("category", "rationale")
#   )
# 
# df_s
# 
# # Or use shorthand (NEW in v0.6.2)
# df |>
#   llm_mutate_structured(
#     annot = "Extract category and rationale for: {text}",
#     .config = cfg_gemini,
#     .schema = schema2
#   )