Class MultiModalEmbeddingModel (1.55.0)

Version 1.55.0

• 1.71.1 (latest)
• 1.71.0
• 1.70.0
• 1.69.0
• 1.68.0
• 1.67.1
• 1.66.0
• 1.65.0
• 1.63.0
• 1.62.0
• 1.60.0
• 1.59.0
• 1.58.0
• 1.57.0
• 1.56.0
• 1.55.0
• 1.54.1
• 1.53.0
• 1.52.0
• 1.51.0
• 1.50.0
• 1.49.0
• 1.48.0
• 1.47.0
• 1.46.0
• 1.45.0
• 1.44.0
• 1.43.0
• 1.39.0
• 1.38.1
• 1.37.0
• 1.36.4
• 1.35.0
• 1.34.0
• 1.33.1
• 1.32.0
• 1.31.1
• 1.30.1
• 1.29.0
• 1.28.1
• 1.27.1
• 1.26.1
• 1.25.0
• 1.24.1
• 1.23.0
• 1.22.1
• 1.21.0
• 1.20.0
• 1.19.1
• 1.18.3
• 1.17.1
• 1.16.1
• 1.15.1
• 1.14.0
• 1.13.1
• 1.12.1
• 1.11.0
• 1.10.0
• 1.9.0
• 1.8.1
• 1.7.1
• 1.6.2
• 1.5.0
• 1.4.3
• 1.3.0
• 1.2.0
• 1.1.1
• 1.0.1
• 0.9.0
• 0.8.0
• 0.7.1
• 0.6.0
• 0.5.1
• 0.4.0
• 0.3.1

MultiModalEmbeddingModel(model_id: str, endpoint_name: typing.Optional[str] = None)

Generates embedding vectors from images and videos.

Examples::

model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding@001")
image = Image.load_from_file("image.png")
video = Video.load_from_file("video.mp4")

embeddings = model.get_embeddings(
    image=image,
    video=video,
    contextual_text="Hello world",
)
image_embedding = embeddings.image_embedding
video_embeddings = embeddings.video_embeddings
text_embedding = embeddings.text_embedding

Methods

MultiModalEmbeddingModel

MultiModalEmbeddingModel(model_id: str, endpoint_name: typing.Optional[str] = None)

Creates a _ModelGardenModel.

This constructor should not be called directly. Use {model_class}.from_pretrained(model_name=...) instead.

Parameters
Name	Description
model_id	str Identifier of a Model Garden Model. Example: "text-bison@001"
endpoint_name	typing.Optional[str] Vertex Endpoint resource name for the model

from_pretrained

from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.T

Loads a _ModelGardenModel.

Parameter
Name	Description
model_name	str Name of the model.

Exceptions
Type	Description
ValueError	If model_name is unknown.
ValueError	If model does not support this class.

get_embeddings

get_embeddings(
    image: typing.Optional[vertexai.vision_models.Image] = None,
    video: typing.Optional[vertexai.vision_models.Video] = None,
    contextual_text: typing.Optional[str] = None,
    dimension: typing.Optional[int] = None,
    video_segment_config: typing.Optional[
        vertexai.vision_models.VideoSegmentConfig
    ] = None,
) -> vertexai.vision_models.MultiModalEmbeddingResponse

Gets embedding vectors from the provided image.

Parameters
Name	Description
image	Image Optional. The image to generate embeddings for. One of image, video, or contextual_text is required.
video	Video Optional. The video to generate embeddings for. One of image, video or contextual_text is required.
contextual_text	str Optional. Contextual text for your input image or video. If provided, the model will also generate an embedding vector for the provided contextual text. The returned image and text embedding vectors are in the same semantic space with the same dimensionality, and the vectors can be used interchangeably for use cases like searching image by text or searching text by image. One of image, video or contextual_text is required.
dimension	int Optional. The number of embedding dimensions. Lower values offer decreased latency when using these embeddings for subsequent tasks, while higher values offer better accuracy. Available values: 128, 256, 512, and 1408 (default).
video_segment_config	VideoSegmentConfig Optional. The specific video segments (in seconds) the embeddings are generated for.

Returns
Type	Description
MultiModalEmbeddingResponse	The image and text embedding vectors.