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View source on Github Arize class to map up to 3 columns (vector, data, and link_to_data) to a single embedding feature. 
class EmbeddingColumnNames(
    vector_column_name: str
    data_column_name: Optional[str] = None
    link_to_data_column_name: Optional[str] = None
)
ParametersData TypeExpected Type In ColumnDescription
vector_column_namestrThe contents of this column must be List[float] or nd.array[float].(Required)Column name for the vector of a given embedding feature.
data_column_namestrThe contents of this column must be str or List[str].(Optional)Used for Natural Language Processing model type - Column name for the data of a given embedding feature, typically the raw text associated with the embedding vector.
link_to_data_column_namestrThe contents of this column must be str.(Optional) Used for Computer Vision model type -Column name for the link to data of a given embedding feature, typically a link to the data file (image, audio, …) associated with the embedding vector. Host data in a cloud storage provider (GCS, AWS, Azure), local server, or public URL. Navigate here to view private AWS S3 image links. Example URL: ” https://link-to-my-image.png ” NOTE: Currently only supports links to image files.

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Code Example

# Declare embedding feature columns
embedding_feature_column_names = {
    # Dictionary keys will be the name of the embedding feature in the app
    "embedding_display_name": EmbeddingColumnNames(
        vector_column_name="vector", # column containing embedding vector (required)
        data_column_name="text", # column containing raw text (optional NLP)
        link_to_data_column_name="image_link" # column containing image URL links (optional CV)
    )
}