com.google.cloud.aiplatform.v1

Interface IndexDatapointOrBuilder

All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

IndexDatapoint, IndexDatapoint.Builder

public interface IndexDatapointOrBuilder
extends com.google.protobuf.MessageOrBuilder

Method Summary

Modifier and Type	Method and Description
IndexDatapoint.CrowdingTag	getCrowdingTag() Optional.
IndexDatapoint.CrowdingTagOrBuilder	getCrowdingTagOrBuilder() Optional.
String	getDatapointId() Required.
com.google.protobuf.ByteString	getDatapointIdBytes() Required.
float	getFeatureVector(int index) Required.
int	getFeatureVectorCount() Required.
List<Float>	getFeatureVectorList() Required.
IndexDatapoint.NumericRestriction	getNumericRestricts(int index) Optional.
int	getNumericRestrictsCount() Optional.
List<IndexDatapoint.NumericRestriction>	getNumericRestrictsList() Optional.
IndexDatapoint.NumericRestrictionOrBuilder	getNumericRestrictsOrBuilder(int index) Optional.
List<? extends IndexDatapoint.NumericRestrictionOrBuilder>	getNumericRestrictsOrBuilderList() Optional.
IndexDatapoint.Restriction	getRestricts(int index) Optional.
int	getRestrictsCount() Optional.
List<IndexDatapoint.Restriction>	getRestrictsList() Optional.
IndexDatapoint.RestrictionOrBuilder	getRestrictsOrBuilder(int index) Optional.
List<? extends IndexDatapoint.RestrictionOrBuilder>	getRestrictsOrBuilderList() Optional.
IndexDatapoint.SparseEmbedding	getSparseEmbedding() Optional.
IndexDatapoint.SparseEmbeddingOrBuilder	getSparseEmbeddingOrBuilder() Optional.
boolean	hasCrowdingTag() Optional.
boolean	hasSparseEmbedding() Optional.

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

isInitialized

Method Detail

getDatapointId

String getDatapointId()

 Required. Unique identifier of the datapoint.
 

string datapoint_id = 1 [(.google.api.field_behavior) = REQUIRED];

Returns:

The datapointId.

getDatapointIdBytes

com.google.protobuf.ByteString getDatapointIdBytes()

 Required. Unique identifier of the datapoint.
 

string datapoint_id = 1 [(.google.api.field_behavior) = REQUIRED];

Returns:

The bytes for datapointId.

getFeatureVectorList

List<Float> getFeatureVectorList()

 Required. Feature embedding vector for dense index. An array of numbers
 with the length of [NearestNeighborSearchConfig.dimensions].
 

repeated float feature_vector = 2 [(.google.api.field_behavior) = REQUIRED];

Returns:

A list containing the featureVector.

getFeatureVectorCount

int getFeatureVectorCount()

 Required. Feature embedding vector for dense index. An array of numbers
 with the length of [NearestNeighborSearchConfig.dimensions].
 

repeated float feature_vector = 2 [(.google.api.field_behavior) = REQUIRED];

Returns:

The count of featureVector.

getFeatureVector

float getFeatureVector(int index)

 Required. Feature embedding vector for dense index. An array of numbers
 with the length of [NearestNeighborSearchConfig.dimensions].
 

repeated float feature_vector = 2 [(.google.api.field_behavior) = REQUIRED];

Parameters:

index - The index of the element to return.

Returns:

The featureVector at the given index.

hasSparseEmbedding

boolean hasSparseEmbedding()

 Optional. Feature embedding vector for sparse index.
 

.google.cloud.aiplatform.v1.IndexDatapoint.SparseEmbedding sparse_embedding = 7 [(.google.api.field_behavior) = OPTIONAL];

Returns:

Whether the sparseEmbedding field is set.

getSparseEmbedding

IndexDatapoint.SparseEmbedding getSparseEmbedding()

 Optional. Feature embedding vector for sparse index.
 

.google.cloud.aiplatform.v1.IndexDatapoint.SparseEmbedding sparse_embedding = 7 [(.google.api.field_behavior) = OPTIONAL];

Returns:

The sparseEmbedding.

getSparseEmbeddingOrBuilder

IndexDatapoint.SparseEmbeddingOrBuilder getSparseEmbeddingOrBuilder()

 Optional. Feature embedding vector for sparse index.
 

.google.cloud.aiplatform.v1.IndexDatapoint.SparseEmbedding sparse_embedding = 7 [(.google.api.field_behavior) = OPTIONAL];

getRestrictsList

List<IndexDatapoint.Restriction> getRestrictsList()

 Optional. List of Restrict of the datapoint, used to perform "restricted
 searches" where boolean rule are used to filter the subset of the database
 eligible for matching. This uses categorical tokens. See:
 https://cloud.google.com/vertex-ai/docs/matching-engine/filtering
 

repeated .google.cloud.aiplatform.v1.IndexDatapoint.Restriction restricts = 4 [(.google.api.field_behavior) = OPTIONAL];

getRestricts

IndexDatapoint.Restriction getRestricts(int index)

 Optional. List of Restrict of the datapoint, used to perform "restricted
 searches" where boolean rule are used to filter the subset of the database
 eligible for matching. This uses categorical tokens. See:
 https://cloud.google.com/vertex-ai/docs/matching-engine/filtering
 

repeated .google.cloud.aiplatform.v1.IndexDatapoint.Restriction restricts = 4 [(.google.api.field_behavior) = OPTIONAL];

getRestrictsCount

int getRestrictsCount()

 Optional. List of Restrict of the datapoint, used to perform "restricted
 searches" where boolean rule are used to filter the subset of the database
 eligible for matching. This uses categorical tokens. See:
 https://cloud.google.com/vertex-ai/docs/matching-engine/filtering
 

repeated .google.cloud.aiplatform.v1.IndexDatapoint.Restriction restricts = 4 [(.google.api.field_behavior) = OPTIONAL];

getRestrictsOrBuilderList

List<? extends IndexDatapoint.RestrictionOrBuilder> getRestrictsOrBuilderList()

 Optional. List of Restrict of the datapoint, used to perform "restricted
 searches" where boolean rule are used to filter the subset of the database
 eligible for matching. This uses categorical tokens. See:
 https://cloud.google.com/vertex-ai/docs/matching-engine/filtering
 

repeated .google.cloud.aiplatform.v1.IndexDatapoint.Restriction restricts = 4 [(.google.api.field_behavior) = OPTIONAL];

getRestrictsOrBuilder

IndexDatapoint.RestrictionOrBuilder getRestrictsOrBuilder(int index)

 Optional. List of Restrict of the datapoint, used to perform "restricted
 searches" where boolean rule are used to filter the subset of the database
 eligible for matching. This uses categorical tokens. See:
 https://cloud.google.com/vertex-ai/docs/matching-engine/filtering
 

repeated .google.cloud.aiplatform.v1.IndexDatapoint.Restriction restricts = 4 [(.google.api.field_behavior) = OPTIONAL];

getNumericRestrictsList

List<IndexDatapoint.NumericRestriction> getNumericRestrictsList()

 Optional. List of Restrict of the datapoint, used to perform "restricted
 searches" where boolean rule are used to filter the subset of the database
 eligible for matching. This uses numeric comparisons.
 

repeated .google.cloud.aiplatform.v1.IndexDatapoint.NumericRestriction numeric_restricts = 6 [(.google.api.field_behavior) = OPTIONAL];

getNumericRestricts

IndexDatapoint.NumericRestriction getNumericRestricts(int index)

 Optional. List of Restrict of the datapoint, used to perform "restricted
 searches" where boolean rule are used to filter the subset of the database
 eligible for matching. This uses numeric comparisons.
 

repeated .google.cloud.aiplatform.v1.IndexDatapoint.NumericRestriction numeric_restricts = 6 [(.google.api.field_behavior) = OPTIONAL];

getNumericRestrictsCount

int getNumericRestrictsCount()

 Optional. List of Restrict of the datapoint, used to perform "restricted
 searches" where boolean rule are used to filter the subset of the database
 eligible for matching. This uses numeric comparisons.
 

repeated .google.cloud.aiplatform.v1.IndexDatapoint.NumericRestriction numeric_restricts = 6 [(.google.api.field_behavior) = OPTIONAL];

getNumericRestrictsOrBuilderList

List<? extends IndexDatapoint.NumericRestrictionOrBuilder> getNumericRestrictsOrBuilderList()

 Optional. List of Restrict of the datapoint, used to perform "restricted
 searches" where boolean rule are used to filter the subset of the database
 eligible for matching. This uses numeric comparisons.
 

repeated .google.cloud.aiplatform.v1.IndexDatapoint.NumericRestriction numeric_restricts = 6 [(.google.api.field_behavior) = OPTIONAL];

getNumericRestrictsOrBuilder

IndexDatapoint.NumericRestrictionOrBuilder getNumericRestrictsOrBuilder(int index)

 Optional. List of Restrict of the datapoint, used to perform "restricted
 searches" where boolean rule are used to filter the subset of the database
 eligible for matching. This uses numeric comparisons.
 

repeated .google.cloud.aiplatform.v1.IndexDatapoint.NumericRestriction numeric_restricts = 6 [(.google.api.field_behavior) = OPTIONAL];

hasCrowdingTag

boolean hasCrowdingTag()

 Optional. CrowdingTag of the datapoint, the number of neighbors to return
 in each crowding can be configured during query.
 

.google.cloud.aiplatform.v1.IndexDatapoint.CrowdingTag crowding_tag = 5 [(.google.api.field_behavior) = OPTIONAL];

Returns:

Whether the crowdingTag field is set.

getCrowdingTag

IndexDatapoint.CrowdingTag getCrowdingTag()

 Optional. CrowdingTag of the datapoint, the number of neighbors to return
 in each crowding can be configured during query.
 

.google.cloud.aiplatform.v1.IndexDatapoint.CrowdingTag crowding_tag = 5 [(.google.api.field_behavior) = OPTIONAL];

Returns:

The crowdingTag.

getCrowdingTagOrBuilder

IndexDatapoint.CrowdingTagOrBuilder getCrowdingTagOrBuilder()

 Optional. CrowdingTag of the datapoint, the number of neighbors to return
 in each crowding can be configured during query.
 

.google.cloud.aiplatform.v1.IndexDatapoint.CrowdingTag crowding_tag = 5 [(.google.api.field_behavior) = OPTIONAL];