bigframes.ml.llm.MultimodalEmbeddingGenerator#
- class bigframes.ml.llm.MultimodalEmbeddingGenerator(*, model_name: Literal['multimodalembedding@001'] | None = None, session: Session | None = None, connection_name: str | None = None)[source]#
Multimodal embedding generator LLM model.
Note
BigFrames Blob is subject to the “Pre-GA Offerings Terms” in the General Service Terms section of the Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available “as is” and might have limited support. For more information, see the launch stage descriptions (https://cloud.google.com/products#product-launch-stages).
- Parameters:
model_name (str, Default to “multimodalembedding@001”) – The model for multimodal embedding. Can set to “multimodalembedding@001”. Multimodal-embedding models returns model embeddings for text, image and video inputs. If no setting is provided, “multimodalembedding@001” will be used by default and a warning will be issued.
session (bigframes.Session or None) – BQ session to create the model. If None, use the global default session.
connection_name (str or None) – Connection to connect with remote service. str of the format <PROJECT_NUMBER/PROJECT_ID>.<LOCATION>.<CONNECTION_ID>. If None, use default connection in session context.
Methods
__init__(*[, model_name, session, ...])get_params([deep])Get parameters for this estimator.
predict(X, *[, max_retries])Predict the result from input DataFrame.
to_gbq(model_name[, replace])Save the model to BigQuery.