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public interface IndexDatapointOrBuilder extends MessageOrBuilderImplements
MessageOrBuilderMethods
getCrowdingTag()
public abstract 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.v1beta1.IndexDatapoint.CrowdingTag crowding_tag = 5 [(.google.api.field_behavior) = OPTIONAL];
| Returns | |
|---|---|
| Type | Description |
IndexDatapoint.CrowdingTag |
The crowdingTag. |
getCrowdingTagOrBuilder()
public abstract 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.v1beta1.IndexDatapoint.CrowdingTag crowding_tag = 5 [(.google.api.field_behavior) = OPTIONAL];
| Returns | |
|---|---|
| Type | Description |
IndexDatapoint.CrowdingTagOrBuilder |
|
getDatapointId()
public abstract String getDatapointId()Required. Unique identifier of the datapoint.
string datapoint_id = 1 [(.google.api.field_behavior) = REQUIRED];
| Returns | |
|---|---|
| Type | Description |
String |
The datapointId. |
getDatapointIdBytes()
public abstract 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. |
getFeatureVector(int index)
public abstract 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 |
intThe index of the element to return. |
| Returns | |
|---|---|
| Type | Description |
float |
The featureVector at the given index. |
getFeatureVectorCount()
public abstract 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. |
getFeatureVectorList()
public abstract 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. |
getNumericRestricts(int index)
public abstract 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.v1beta1.IndexDatapoint.NumericRestriction numeric_restricts = 6 [(.google.api.field_behavior) = OPTIONAL];
| Parameter | |
|---|---|
| Name | Description |
index |
int |
| Returns | |
|---|---|
| Type | Description |
IndexDatapoint.NumericRestriction |
|
getNumericRestrictsCount()
public abstract 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.v1beta1.IndexDatapoint.NumericRestriction numeric_restricts = 6 [(.google.api.field_behavior) = OPTIONAL];
| Returns | |
|---|---|
| Type | Description |
int |
|
getNumericRestrictsList()
public abstract 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.v1beta1.IndexDatapoint.NumericRestriction numeric_restricts = 6 [(.google.api.field_behavior) = OPTIONAL];
| Returns | |
|---|---|
| Type | Description |
List<NumericRestriction> |
|
getNumericRestrictsOrBuilder(int index)
public abstract 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.v1beta1.IndexDatapoint.NumericRestriction numeric_restricts = 6 [(.google.api.field_behavior) = OPTIONAL];
| Parameter | |
|---|---|
| Name | Description |
index |
int |
| Returns | |
|---|---|
| Type | Description |
IndexDatapoint.NumericRestrictionOrBuilder |
|
getNumericRestrictsOrBuilderList()
public abstract 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.v1beta1.IndexDatapoint.NumericRestriction numeric_restricts = 6 [(.google.api.field_behavior) = OPTIONAL];
| Returns | |
|---|---|
| Type | Description |
List<? extends com.google.cloud.aiplatform.v1beta1.IndexDatapoint.NumericRestrictionOrBuilder> |
|
getRestricts(int index)
public abstract 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.v1beta1.IndexDatapoint.Restriction restricts = 4 [(.google.api.field_behavior) = OPTIONAL];
| Parameter | |
|---|---|
| Name | Description |
index |
int |
| Returns | |
|---|---|
| Type | Description |
IndexDatapoint.Restriction |
|
getRestrictsCount()
public abstract 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.v1beta1.IndexDatapoint.Restriction restricts = 4 [(.google.api.field_behavior) = OPTIONAL];
| Returns | |
|---|---|
| Type | Description |
int |
|
getRestrictsList()
public abstract 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.v1beta1.IndexDatapoint.Restriction restricts = 4 [(.google.api.field_behavior) = OPTIONAL];
| Returns | |
|---|---|
| Type | Description |
List<Restriction> |
|
getRestrictsOrBuilder(int index)
public abstract 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.v1beta1.IndexDatapoint.Restriction restricts = 4 [(.google.api.field_behavior) = OPTIONAL];
| Parameter | |
|---|---|
| Name | Description |
index |
int |
| Returns | |
|---|---|
| Type | Description |
IndexDatapoint.RestrictionOrBuilder |
|
getRestrictsOrBuilderList()
public abstract 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.v1beta1.IndexDatapoint.Restriction restricts = 4 [(.google.api.field_behavior) = OPTIONAL];
| Returns | |
|---|---|
| Type | Description |
List<? extends com.google.cloud.aiplatform.v1beta1.IndexDatapoint.RestrictionOrBuilder> |
|
getSparseEmbedding()
public abstract IndexDatapoint.SparseEmbedding getSparseEmbedding()Optional. Feature embedding vector for sparse index.
.google.cloud.aiplatform.v1beta1.IndexDatapoint.SparseEmbedding sparse_embedding = 7 [(.google.api.field_behavior) = OPTIONAL];
| Returns | |
|---|---|
| Type | Description |
IndexDatapoint.SparseEmbedding |
The sparseEmbedding. |
getSparseEmbeddingOrBuilder()
public abstract IndexDatapoint.SparseEmbeddingOrBuilder getSparseEmbeddingOrBuilder()Optional. Feature embedding vector for sparse index.
.google.cloud.aiplatform.v1beta1.IndexDatapoint.SparseEmbedding sparse_embedding = 7 [(.google.api.field_behavior) = OPTIONAL];
| Returns | |
|---|---|
| Type | Description |
IndexDatapoint.SparseEmbeddingOrBuilder |
|
hasCrowdingTag()
public abstract boolean hasCrowdingTag()Optional. CrowdingTag of the datapoint, the number of neighbors to return in each crowding can be configured during query.
.google.cloud.aiplatform.v1beta1.IndexDatapoint.CrowdingTag crowding_tag = 5 [(.google.api.field_behavior) = OPTIONAL];
| Returns | |
|---|---|
| Type | Description |
boolean |
Whether the crowdingTag field is set. |
hasSparseEmbedding()
public abstract boolean hasSparseEmbedding()Optional. Feature embedding vector for sparse index.
.google.cloud.aiplatform.v1beta1.IndexDatapoint.SparseEmbedding sparse_embedding = 7 [(.google.api.field_behavior) = OPTIONAL];
| Returns | |
|---|---|
| Type | Description |
boolean |
Whether the sparseEmbedding field is set. |