Generate Text Embeddings — foundry

Generate embedding vectors for one or more text inputs using an Azure AI Foundry deployed embedding model. Returns a tibble with the input text and corresponding embedding vectors stored as a list-column.

Usage

foundry_embed(
  text,
  model = NULL,
  dimensions = NULL,
  api_key = NULL,
  api_version = NULL
)

Arguments

text: Character vector. The text(s) to embed.
model: Character. The deployment name of an embedding model. Defaults to the environment variable AZURE_FOUNDRY_EMBED_MODEL.
dimensions: Integer. Optional. The number of dimensions for the output embeddings. Only supported by some models (e.g., text-embedding-3).
api_key: Character. Optional API key override.
api_version: Character. Optional API version override.

Value

A tibble with columns:

text: Character. The original input text.
embedding: List. A numeric vector containing the embedding.
n_dims: Integer. The dimensionality of the embedding.

Details

Important: The model parameter must be a deployment of an embedding model, not a chat model. Common embedding models include:

text-embedding-ada-002
text-embedding-3-small
text-embedding-3-large

Chat models (GPT-4, Claude, Llama, etc.) cannot generate embeddings. If you only have chat models deployed, you'll need to deploy an embedding model in Azure AI Foundry first.

Examples

if (FALSE) { # \dontrun{
# Single text
foundry_embed("Hello, world!", model = "text-embedding-ada-002")

# Multiple texts
texts <- c("Data science is fun", "R is great for statistics")
foundry_embed(texts, model = "text-embedding-ada-002")

# With reduced dimensions (model-dependent)
foundry_embed("Hello", model = "text-embedding-3-small", dimensions = 256)
} # }