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Uses Azure AI Foundry to create embeddings

Source: R/embed-azure.R
embed_azure_openai.Rd

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.