EmbeddingIndex#

EmbeddingIndex #

Represents an index for managing embedding features.

PARAMETER	DESCRIPTION
`index_name`	The name of the embedding index. The name of the project index is used if not provided. TYPE: `str \| None` DEFAULT: `None`
`features`	A list of `EmbeddingFeature` objects for the features that contain embeddings that should be indexed for similarity search. TYPE: `list[EmbeddingFeature] \| None` DEFAULT: `None`
`col_prefix`	The prefix to be added to column names when using project index. It is managed by Hopsworks and should not be provided. TYPE: `str \| None` DEFAULT: `None`

Example

embedding_index = EmbeddingIndex()
embedding_index.add_embedding(name="user_vector", dimension=256)
embeddings = embedding_index.get_embeddings()

col_prefix `property` #

col_prefix

str: The prefix to be added to column names.

feature_group `property` `writable` #

feature_group

FeatureGroup: The feature group object that contains the embedding feature.

index_name `property` #

index_name

str: The name of the embedding index.

add_embedding #

add_embedding(
    name: str,
    dimension: int,
    similarity_function_type: SimilarityFunctionType
    | None = SimilarityFunctionType.L2,
    model=None,
)

Adds a new embedding feature to the index.

Example

embedding_index = EmbeddingIndex()
embedding_index.add_embedding(name="user_vector", dimension=256)

# Attach a hsml model to the embedding feature
embedding_index = EmbeddingIndex()
embedding_index.add_embedding(name="user_vector", dimension=256, model=hsml_model)

PARAMETER	DESCRIPTION
`name`	The name of the embedding feature. TYPE: `str`
`dimension`	The dimensionality of the embedding feature. TYPE: `int`
`similarity_function_type`	The type of similarity function to be used. TYPE: `SimilarityFunctionType \| None` DEFAULT: `SimilarityFunctionType.L2`
`model`	`hsml.model.Model \| None` The hsml model used to generate the embedding. DEFAULT: `None`

count #

count(options: map = None) -> int

Count the number of records in the feature group.

PARAMETER	DESCRIPTION
`options`	The options used for the request to the vector database. The keys are attribute values of `OpensearchRequestOption`. TYPE: `map` DEFAULT: `None`

RETURNS	DESCRIPTION
`int`	The number of records in the feature group.

RAISES	DESCRIPTION
`ValueError`	If the feature group is not initialized.
`hopsworks.client.exceptions.FeatureStoreException`	If an error occurs during the count operation.

get_embedding #

get_embedding(name: str) -> EmbeddingFeature

Get EmbeddingFeature associated with the feature name.

PARAMETER	DESCRIPTION
`name`	The name of the embedding feature. TYPE: `str`

RETURNS	DESCRIPTION
`EmbeddingFeature`	The `EmbeddingFeature` associated with the name.

get_embeddings #

get_embeddings() -> list[EmbeddingFeature]

Returns the list of EmbeddingFeature objects associated with the index.

RETURNS	DESCRIPTION
`list[EmbeddingFeature]`	All embedding features in the index.

json #

json()

Serialize the EmbeddingIndex object to a JSON string.

to_dict #

to_dict() -> dict

Convert the EmbeddingIndex object to a dictionary.

RETURNS	DESCRIPTION
`dict`	A dictionary representation of the EmbeddingIndex object.