public final class IndexDatapoint extends GeneratedMessageV3 implements IndexDatapointOrBuilder
A datapoint of Index.
Protobuf type google.cloud.aiplatform.v1.IndexDatapoint
Inherited Members
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT)
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT,int)
com.google.protobuf.GeneratedMessageV3.<T>emptyList(java.lang.Class<T>)
com.google.protobuf.GeneratedMessageV3.internalGetMapFieldReflection(int)
Static Fields
public static final int CROWDING_TAG_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int DATAPOINT_ID_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int FEATURE_VECTOR_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int NUMERIC_RESTRICTS_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int RESTRICTS_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int SPARSE_EMBEDDING_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
Static Methods
public static IndexDatapoint getDefaultInstance()
public static final Descriptors.Descriptor getDescriptor()
public static IndexDatapoint.Builder newBuilder()
public static IndexDatapoint.Builder newBuilder(IndexDatapoint prototype)
public static IndexDatapoint parseDelimitedFrom(InputStream input)
public static IndexDatapoint parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
public static IndexDatapoint parseFrom(byte[] data)
| Parameter |
| Name |
Description |
data |
byte[]
|
public static IndexDatapoint parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
public static IndexDatapoint parseFrom(ByteString data)
public static IndexDatapoint parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
public static IndexDatapoint parseFrom(CodedInputStream input)
public static IndexDatapoint parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public static IndexDatapoint parseFrom(InputStream input)
public static IndexDatapoint parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
public static IndexDatapoint parseFrom(ByteBuffer data)
public static IndexDatapoint parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
public static Parser<IndexDatapoint> parser()
Methods
public boolean equals(Object obj)
| Parameter |
| Name |
Description |
obj |
Object
|
Overrides
public 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];
public 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];
public String getDatapointId()
Required. Unique identifier of the datapoint.
string datapoint_id = 1 [(.google.api.field_behavior) = REQUIRED];
| Returns |
| Type |
Description |
String |
The datapointId.
|
public ByteString getDatapointIdBytes()
Required. Unique identifier of the datapoint.
string datapoint_id = 1 [(.google.api.field_behavior) = REQUIRED];
| Returns |
| Type |
Description |
ByteString |
The bytes for datapointId.
|
public IndexDatapoint getDefaultInstanceForType()
public 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];
| Parameter |
| Name |
Description |
index |
int
The index of the element to return.
|
| Returns |
| Type |
Description |
float |
The featureVector at the given index.
|
public 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 |
| Type |
Description |
int |
The count of featureVector.
|
public 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 |
| Type |
Description |
List<Float> |
A list containing the featureVector.
|
public 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];
| Parameter |
| Name |
Description |
index |
int
|
public 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];
| Returns |
| Type |
Description |
int |
|
public 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];
public 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];
| Parameter |
| Name |
Description |
index |
int
|
public 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];
| Returns |
| Type |
Description |
List<? extends com.google.cloud.aiplatform.v1.IndexDatapoint.NumericRestrictionOrBuilder> |
|
public Parser<IndexDatapoint> getParserForType()
Overrides
public 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];
| Parameter |
| Name |
Description |
index |
int
|
public 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];
| Returns |
| Type |
Description |
int |
|
public 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];
public 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];
| Parameter |
| Name |
Description |
index |
int
|
public 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];
| Returns |
| Type |
Description |
List<? extends com.google.cloud.aiplatform.v1.IndexDatapoint.RestrictionOrBuilder> |
|
public int getSerializedSize()
| Returns |
| Type |
Description |
int |
|
Overrides
public IndexDatapoint.SparseEmbedding getSparseEmbedding()
Optional. Feature embedding vector for sparse index.
.google.cloud.aiplatform.v1.IndexDatapoint.SparseEmbedding sparse_embedding = 7 [(.google.api.field_behavior) = OPTIONAL];
public IndexDatapoint.SparseEmbeddingOrBuilder getSparseEmbeddingOrBuilder()
Optional. Feature embedding vector for sparse index.
.google.cloud.aiplatform.v1.IndexDatapoint.SparseEmbedding sparse_embedding = 7 [(.google.api.field_behavior) = OPTIONAL];
public 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 |
| Type |
Description |
boolean |
Whether the crowdingTag field is set.
|
public boolean hasSparseEmbedding()
Optional. Feature embedding vector for sparse index.
.google.cloud.aiplatform.v1.IndexDatapoint.SparseEmbedding sparse_embedding = 7 [(.google.api.field_behavior) = OPTIONAL];
| Returns |
| Type |
Description |
boolean |
Whether the sparseEmbedding field is set.
|
| Returns |
| Type |
Description |
int |
|
Overrides
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Overrides
public final boolean isInitialized()
Overrides
public IndexDatapoint.Builder newBuilderForType()
protected IndexDatapoint.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Overrides
protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
| Returns |
| Type |
Description |
Object |
|
Overrides
public IndexDatapoint.Builder toBuilder()
public void writeTo(CodedOutputStream output)
Overrides
