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MultiModalEmbeddingModel(model_id: str, endpoint_name: typing.Optional[str] = None)
Generates embedding vectors from images.
Examples::
model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding@001")
image = Image.load_from_file("image.png")
embeddings = model.get_embeddings(
image=image,
contextual_text="Hello world",
)
image_embedding = embeddings.image_embedding
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.
from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.T
Loads a _ModelGardenModel.
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,
contextual_text: typing.Optional[str] = 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 |
contextual_text |
str
Optional. Contextual text for your input image. 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 |
Returns | |
---|---|
Type | Description |
ImageEmbeddingResponse | The image and text embedding vectors. |