foundry_embed_batch: Generate Text Embeddings in Parallel Batches

In foundryR: Data Frame Workflows for 'Microsoft Foundry'

Generate Text Embeddings in Parallel Batches

Description

Generate embedding vectors for a large collection of texts using parallel batch processing. This function is optimized for high-throughput embedding generation, using httr2::req_perform_parallel() to process multiple batches concurrently while tracking errors gracefully.

Usage

foundry_embed_batch(
  text,
  model = NULL,
  dimensions = NULL,
  batch_size = 100L,
  max_active = 2L,
  progress = TRUE,
  api = c("v1", "deployment"),
  api_key = NULL,
  api_version = NULL
)

Arguments

text	Character vector. The texts 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).
batch_size	Integer. Number of texts to include in each batch request. Default: 100.
max_active	Integer. Maximum number of concurrent requests. Default: 2.
progress	Logical. Whether to show a progress bar. Default: TRUE.
api	Character. Endpoint style. "v1" (default) sends requests to ⁠/openai/v1/embeddings⁠ with model in the JSON body. "deployment" keeps the legacy deployment-path endpoint.
api_key	Character. Optional API key override.
api_version	Character. Optional API version override.

Value

A tibble with columns:

.input_idx

Integer. The original index of each text in the input vector.

text

Character. The original input text.

embedding

List. A numeric vector containing the embedding, or NULL if failed. May contain multiple embeddings per batch response.

n_dims

Integer. The dimensionality of the embedding, or NA if failed.

.error

Logical. TRUE if the request for this text failed.

.error_msg

Character. Error message if failed, NA otherwise.

raw_response

List. Raw parsed response payload for successful rows, or NULL for failed rows.

Examples

## Not run: 
# Requires a configured Azure endpoint and credentials,
# plus an embedding deployment.
# Embed many texts in parallel
texts <- c("Hello, world!", "Data science is fun", "R is great")
embeddings <- foundry_embed_batch(texts, model = "text-embedding-ada-002")

# With custom batch size and concurrency
large_texts <- rep("Sample text", 1000)
embeddings <- foundry_embed_batch(
  large_texts,
  model = "text-embedding-ada-002",
  batch_size = 50,
  max_active = 2
)

# Filter successful embeddings
successful <- embeddings[!embeddings$.error, ]

# Check for errors
failed <- embeddings[embeddings$.error, ]
if (nrow(failed) > 0) {
  message("Some embeddings failed:")
  print(failed[, c(".input_idx", ".error_msg")])
}

## End(Not run)

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