embed_azure_openai: Uses Azure AI Foundry to create embeddings

In ragnar: Retrieval-Augmented Generation (RAG) Workflows

Uses Azure AI Foundry to create embeddings

Description

Uses Azure AI Foundry to create embeddings

Usage

embed_azure_openai(
  x,
  endpoint = get_envvar("AZURE_OPENAI_ENDPOINT"),
  api_key = get_envvar("AZURE_OPENAI_API_KEY"),
  api_version = "2023-05-15",
  model,
  batch_size = 20L,
  api_args = list()
)

Arguments

x	x can be: A character vector, in which case a matrix of embeddings is returned. A data frame with a column named text, in which case the dataframe is returned with an additional column named embedding. Missing or NULL, in which case a function is returned that can be called to get embeddings. This is a convenient way to partial in additional arguments like model, and is the most convenient way to produce a function that can be passed to the embed argument of ragnar_store_create().
endpoint	The Azure AI Foundry endpoint URL. A URI in the form of ⁠https://<project>.cognitiveservices.azure.com/⁠. Defaults to the value of the AZURE_OPENAI_ENDPOINT environment variable. This URL is appended with ⁠/openai/deployments/{model}/embeddings⁠. Where model is the deployment name of the model.
api_key	resolved using env var OPENAI_API_KEY
api_version	The API version to use. Defaults to 2023-05-15.
model	The deployment name of the model to use for generating embeddings.
batch_size	split x into batches when embedding. Integer, limit of strings to include in a single request.
api_args	A list of additional arguments to pass to the API request body.

Value

If x is a character vector, then a numeric matrix is returned, where nrow = length(x) and ⁠ncol = <model-embedding-size>⁠. If x is a data.frame, then a new embedding matrix "column" is added, containing the matrix described in the previous sentence.

A matrix of embeddings with 1 row per input string, or a dataframe with an 'embedding' column.

ragnar documentation built on Sept. 11, 2026, 1:06 a.m.