foundry_extract: Extract structured data from text using JSON Schema

In foundryR: Data Frame Workflows for 'Microsoft Foundry'

Extract structured data from text using JSON Schema

Description

Apply a JSON Schema to one or more text inputs and return model-extracted fields as a tidy tibble. This is useful for research coding tasks such as sentiment annotation, entity extraction, study abstraction, and converting free-text records into analyzable variables.

Usage

foundry_extract(
  text,
  schema = NULL,
  text_col = NULL,
  instructions = NULL,
  schema_name = "ExtractedData",
  strict = TRUE,
  model = NULL,
  flatten = TRUE,
  store = FALSE,
  max_active = 2L,
  progress = TRUE,
  api_key = NULL,
  endpoint = NULL,
  ...
)

Arguments

text	Character vector or data frame. Texts to extract from, or a data frame containing a text column.
schema	List. JSON Schema object describing the fields to extract.
text_col	Character. Column name containing text when text is a data frame.
instructions	Character. Optional extraction instructions. If omitted, a concise default extraction instruction is used.
schema_name	Character. Name for the JSON Schema format.
strict	Logical. Whether the model must strictly follow the schema.
model	Character. The model deployment name. Defaults to AZURE_FOUNDRY_MODEL.
flatten	Logical. If TRUE, top-level schema fields are returned as tibble columns. Nested objects and arrays become list-columns. If FALSE, parsed data is returned in a .data list-column.
store	Logical. Whether to store Responses API objects. Defaults to FALSE because bulk extraction often processes sensitive research data.
max_active	Integer. Maximum number of concurrent requests.
progress	Logical. Whether to show a progress bar for parallel extraction.
api_key	Character. Optional API key override.
endpoint	Character. Optional endpoint override.
...	Additional parameters passed to foundry_response().

Value

A tibble with one row per input text. Metadata columns are prefixed with ., followed by extracted schema fields when flatten = TRUE.

References

• Structured outputs: https://learn.microsoft.com/azure/foundry/openai/how-to/structured-outputs

Examples

## Not run: 
# Requires a configured Azure endpoint, credentials, and AZURE_FOUNDRY_MODEL
# naming a deployment that supports structured outputs.
schema <- list(
  type = "object",
  properties = list(
    sentiment = list(type = "string", enum = c("positive", "negative", "neutral")),
    entities = list(type = "array", items = list(type = "string"))
  ),
  required = c("sentiment", "entities"),
  additionalProperties = FALSE
)

foundry_extract(
  c("I love using R with Azure.", "The workflow was slow and confusing."),
  schema = schema
)

## End(Not run)

foundryR documentation built on Sept. 25, 2026, 1:10 a.m.