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EmbeddingColumnNames

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
)
Parameters
Data Type
Expected Type In Column
Description
vector_column_name
str
The 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_name
str
The 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_name
str
The 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.

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)
)
}