## ----setup, include = FALSE---------------------------------------------------
fixture_dir <- "responses-api"
recording <- nzchar(Sys.getenv("FOUNDRY_RECORD_DOCS"))
have_fixtures <- dir.exists(fixture_dir) && length(list.files(fixture_dir)) > 0
run_api <- requireNamespace("httptest2", quietly = TRUE) &&
  (recording || have_fixtures)
library(foundryR)
if (run_api) {
  httptest2::start_vignette(fixture_dir)
}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = run_api,
  fig.width = 7, fig.height = 4.5, out.width = "100%")

## ----project-route, eval = FALSE----------------------------------------------
# foundry_set_project_endpoint(Sys.getenv("AZURE_FOUNDRY_PROJECT_ENDPOINT"))
# 
# foundry_response(
#   "Summarize the project route in one sentence.",
#   project_endpoint = Sys.getenv("AZURE_FOUNDRY_PROJECT_ENDPOINT")
# )

## ----basic-response-----------------------------------------------------------
basic <- foundry_response(
  "Answer in one sentence: what is retrieval-augmented generation?"
)

basic$output_text
basic[, c(
  "input_tokens", "output_tokens", "reasoning_tokens",
  "cached_input_tokens", "total_tokens"
)]

## ----stateful-turns-----------------------------------------------------------
first <- foundry_response(
  "Define catastrophic forgetting in one sentence."
)

second <- foundry_response(
  "Explain it for a college freshman in one sentence.",
  previous_response_id = first$response_id
)

second$output_text
second[, c("response_id", "input_tokens", "output_tokens", "total_tokens")]

## ----structured-extraction----------------------------------------------------
comment_schema <- foundry_schema(
  sentiment = schema_enum(c("positive", "negative", "neutral")),
  entities = schema_array(schema_string()),
  summary = schema_string()
)

comments <- c(
  "The new data pipeline reduced manual coding time by half.",
  "Participants reported confusion about the consent form."
)

comment_codes <- foundry_extract(
  comments,
  schema = comment_schema,
  schema_name = "CommentCode"
)

comment_codes[, c("sentiment", "entities", "summary", ".status")]

## ----ellmer-schema, eval = requireNamespace("ellmer", quietly = TRUE)---------
sentiment_spec <- ellmer::type_object(
  sentiment = ellmer::type_enum(
    c("positive", "negative", "neutral"),
    description = "Overall sentiment of the response."
  ),
  theme = ellmer::type_string("A short theme label for the response.")
)

sentiment_schema <- as_foundry_schema(sentiment_spec)
jsonlite::toJSON(sentiment_schema, auto_unbox = TRUE, pretty = TRUE)

## ----function-tools-----------------------------------------------------------
get_weather <- function(location) {
  list(location = location, temperature = "70 F")
}

weather_tool <- foundry_tool(
  get_weather,
  description = "Get weather for a location",
  parameters = foundry_schema(
    location = schema_string("City and state.")
  )
)

tool_turns <- foundry_agent(
  "What is the weather in San Francisco?",
  tools = list(weather_tool),
  max_iterations = 4
)

tool_turns[, c("iteration", "final", "output_text")]
tool_turns$tool_calls[[1]][, c("type", "name", "call_id", "arguments")]
tool_turns$tool_results[[1]]

## ----mcp-tool, eval = FALSE---------------------------------------------------
# mcp_tool <- list(
#   type = "mcp",
#   server_label = "approved_server",
#   server_url = Sys.getenv("MY_MCP_SERVER_URL"),
#   require_approval = "never"
# )
# 
# foundry_response(
#   "Use the MCP server if it helps answer the question.",
#   tools = list(mcp_tool)
# )

## ----web-search---------------------------------------------------------------
options(foundryR.web_search_warning = TRUE)

web_answer <- foundry_web_search(
  "Which version of R does the R Project website list as the latest release, and when was it released?",
  search_context_size = "medium"
)

web_answer$output_text
web_answer$citations[[1]][, c("title", "url")]
web_answer$tool_calls[[1]][, c("type", "status", "action_type", "query")]

## ----web-search-location, eval = FALSE----------------------------------------
# foundry_web_search(
#   "Find a recent AI research event near me.",
#   country = "US",
#   region = "Washington",
#   city = "Seattle",
#   timezone = "America/Los_Angeles"
# )

## ----reasoning----------------------------------------------------------------
reasoned <- foundry_response(
  "Compare the two arguments and identify the weaker premise: A says the survey item is valid because it is short. B says it is valid because respondents interpret it consistently.",
  reasoning_effort = "medium"
)

reasoned$output_text
reasoned[, c(
  "input_tokens", "output_tokens", "reasoning_tokens",
  "cached_input_tokens", "total_tokens"
)]

## ----cleanup, include = FALSE-------------------------------------------------
if (run_api) {
  httptest2::end_vignette()
}