Resource: FeatureView
FeatureView is representation of values that the FeatureOnlineStore will serve based on its syncConfig.
name
string
Identifier. name of the FeatureView. Format: projects/{project}/locations/{location}/featureOnlineStores/{featureOnlineStore}/featureViews/{featureView}
Output only. timestamp when this FeatureView was created.
A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: "2014-10-02T15:01:23Z"
and "2014-10-02T15:01:23.045123456Z"
.
Output only. timestamp when this FeatureView was last updated.
A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: "2014-10-02T15:01:23Z"
and "2014-10-02T15:01:23.045123456Z"
.
etag
string
Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels
map (key: string, value: string)
Optional. The labels with user-defined metadata to organize your FeatureViews.
label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed.
See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
Configures when data is to be synced/updated for this FeatureView. At the end of the sync the latest featureValues for each entityId of this FeatureView are made ready for online serving.
Optional. Configuration for index preparation for vector search. It contains the required configurations to create an index from source data, so that approximate nearest neighbor (a.k.a ANN) algorithms search can be performed during online serving.
satisfiesPzs
boolean
Output only. reserved for future use.
satisfiesPzi
boolean
Output only. reserved for future use.
Union field source
.
source
can be only one of the following:
Optional. Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore.
Optional. Configures the features from a feature Registry source that need to be loaded onto the FeatureOnlineStore.
Optional. The Vertex RAG Source that the FeatureView is linked to.
JSON representation |
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{ "name": string, "createTime": string, "updateTime": string, "etag": string, "labels": { string: string, ... }, "syncConfig": { object ( |
BigQuerySource
uri
string
Required. The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.
entityIdColumns[]
string
Required. columns to construct entityId / row keys.
JSON representation |
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{ "uri": string, "entityIdColumns": [ string ] } |
FeatureRegistrySource
A feature Registry source for features that need to be synced to Online Store.
Required. List of features that need to be synced to Online Store.
Optional. The project number of the parent project of the feature Groups.
JSON representation |
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{
"featureGroups": [
{
object ( |
FeatureGroup
Features belonging to a single feature group that will be synced to Online Store.
featureGroupId
string
Required. Identifier of the feature group.
featureIds[]
string
Required. Identifiers of features under the feature group.
JSON representation |
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{ "featureGroupId": string, "featureIds": [ string ] } |
VertexRagSource
A Vertex Rag source for features that need to be synced to Online Store.
uri
string
Required. The BigQuery view/table URI that will be materialized on each manual sync trigger. The table/view is expected to have the following columns and types at least: - corpus_id
(STRING, NULLABLE/REQUIRED) - fileId
(STRING, NULLABLE/REQUIRED) - chunkId
(STRING, NULLABLE/REQUIRED) - chunk_data_type
(STRING, NULLABLE/REQUIRED) - chunk_data
(STRING, NULLABLE/REQUIRED) - embeddings
(FLOAT, REPEATED) - file_original_uri
(STRING, NULLABLE/REQUIRED)
Optional. The RAG corpus id corresponding to this FeatureView.
JSON representation |
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{ "uri": string, "ragCorpusId": string } |
SyncConfig
Configuration for Sync. Only one option is set.
cron
string
Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".
continuous
boolean
Optional. If true, syncs the FeatureView in a continuous manner to Online Store.
JSON representation |
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{ "cron": string, "continuous": boolean } |
IndexConfig
Configuration for vector indexing.
embeddingColumn
string
Optional. column of embedding. This column contains the source data to create index for vector search. embeddingColumn must be set when using vector search.
filterColumns[]
string
Optional. columns of features that're used to filter vector search results.
crowdingColumn
string
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.
Optional. The distance measure used in nearest neighbor search.
algorithm_config
. The configuration with regard to the algorithms used for efficient search. algorithm_config
can be only one of the following: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
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.
embeddingDimension
integer
Optional. The number of dimensions of the input embedding.
JSON representation |
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{ "embeddingColumn": string, "filterColumns": [ string ], "crowdingColumn": string, "distanceMeasureType": enum ( |
TreeAHConfig
Configuration options for the tree-AH algorithm.
Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.
JSON representation |
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{ "leafNodeEmbeddingCount": string } |
BruteForceConfig
This type has no fields.
Configuration options for using brute force search.
DistanceMeasureType
The distance measure used in nearest neighbor search.
Enums | |
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DISTANCE_MEASURE_TYPE_UNSPECIFIED |
Should not be set. |
SQUARED_L2_DISTANCE |
Euclidean (L_2) Distance. |
COSINE_DISTANCE |
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 |
Dot Product Distance. Defined as a negative of the dot product. |
Methods |
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Creates a new FeatureView in a given FeatureOnlineStore. |
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Deletes a single FeatureView. |
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Fetch feature values under a FeatureView. |
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Gets details of a single FeatureView. |
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Lists FeatureViews in a given FeatureOnlineStore. |
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Updates the parameters of a single FeatureView. |
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Search the nearest entities under a FeatureView. |
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Triggers on-demand sync for the FeatureView. |