Class VectorSearchConfig (1.48.0)

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VectorSearchConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Deprecated. Use IndexConfig instead.

This message has oneof_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Attributes
Name	Description
tree_ah_config	google.cloud.aiplatform_v1beta1.types.FeatureView.VectorSearchConfig.TreeAHConfig Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396 This field is a member of oneof_ algorithm_config.
brute_force_config	google.cloud.aiplatform_v1beta1.types.FeatureView.VectorSearchConfig.BruteForceConfig Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search. This field is a member of oneof_ algorithm_config.
embedding_column	str Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
filter_columns	MutableSequence[str] Optional. Columns of features that're used to filter vector search results.
crowding_column	str Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by FeatureOnlineStoreService.SearchNearestEntities to diversify search results. If NearestNeighborQuery.per_crowding_attribute_neighbor_count is set to K in SearchNearestEntitiesRequest, it's guaranteed that no more than K entities of the same crowding attribute are returned in the response.
embedding_dimension	int Optional. The number of dimensions of the input embedding. This field is a member of oneof_ _embedding_dimension.
distance_measure_type	google.cloud.aiplatform_v1beta1.types.FeatureView.VectorSearchConfig.DistanceMeasureType Optional. The distance measure used in nearest neighbor search.

Classes

DistanceMeasureType

DistanceMeasureType(value)

Values: DISTANCE_MEASURE_TYPE_UNSPECIFIED (0): Should not be set. SQUARED_L2_DISTANCE (1): Euclidean (L_2) Distance. COSINE_DISTANCE (2): Cosine Distance. Defined as 1 - cosine similarity.

    We strongly suggest using DOT_PRODUCT_DISTANCE +
    UNIT_L2_NORM instead of COSINE distance. Our algorithms have
    been more optimized for DOT_PRODUCT distance which, when
    combined with UNIT_L2_NORM, is mathematically equivalent to
    COSINE distance and results in the same ranking.
DOT_PRODUCT_DISTANCE (3):
    Dot Product Distance. Defined as a negative
    of the dot product.

TreeAHConfig

TreeAHConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Methods

VectorSearchConfig

VectorSearchConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Deprecated. Use IndexConfig instead.

This message has oneof_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields