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VectorSearchConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Configuration for vector search.
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 nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute. |
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)
Configuration for vector search.
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