embed_bedrock: Embed text using a Bedrock model

In ragnar: Retrieval-Augmented Generation (RAG) Workflows

Embed text using a Bedrock model

Description

Embed text using a Bedrock model

Usage

embed_bedrock(x, model, profile, 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().
model	Currently only Cohere.ai and Amazon Titan models are supported. There are no guardarails for the kind of model that is used, but the model must be available in the AWS region specified by the profile. You may look for available models in the Bedrock Model Catalog
profile	AWS profile to use.
api_args	Additional arguments to pass to the Bedrock API. Depending on the model, you might be able to provide different parameters. Check the documentation for the model you are using in the Bedrock user guide.

Value

If x is missing returns a function that can be called to get embeddings. If x is not missing, a matrix of embeddings with 1 row per input string, or a dataframe with an 'embedding' column.

See Also

embed_ollama()

ragnar documentation built on Aug. 8, 2025, 7:07 p.m.