You can create a remote model based on the Vertex AI gemini-embedding-001 model, or a

## Feature Feature You can create a [remote model](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-remote-model-embedding-maas)based on the Vertex AI `gemini-embedding-001` model, or a[remote model](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-remote-model-open)based on an open embedding model from Vertex Model Garden or Hugging Face that is deployed to Vertex AI. You can then use the[AI.GENERATE\_EMBEDDING function](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-generate-embedding)with these remote models to generate embeddings. You can also use the[AI.EMBED function](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-embed)directly with the `gemini-embedding-001` model endpoint. These features are[generally available](https://cloud.google.com/products/#product-launch-stages)(GA). ## Feature Feature You can now use the [Pipelines & Connections page](https://cloud.google.com/bigquery/docs/pipeline-connection-page)to streamline your data integration tasks by using guided, BigQuery-specific configuration workflows for services like BigQuery Data Transfer Service, Datastream, and Pub/Sub. This feature is in [Preview](https://cloud.google.com/products/#product-launch-stages).