Package com.google.cloud.aiplatform.v1

Interface IndexDatapointOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
IndexDatapoint, IndexDatapoint.Builder
@Generated public interface IndexDatapointOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Details

    • 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];

    • hasEmbeddingMetadata

      boolean hasEmbeddingMetadata()
       Optional. The key-value map of additional metadata for the datapoint.
      .google.protobuf.Struct embedding_metadata = 8 [(.google.api.field_behavior) = OPTIONAL];
      Returns:
      Whether the embeddingMetadata field is set.

    • getEmbeddingMetadata

      com.google.protobuf.Struct getEmbeddingMetadata()
       Optional. The key-value map of additional metadata for the datapoint.
      .google.protobuf.Struct embedding_metadata = 8 [(.google.api.field_behavior) = OPTIONAL];
      Returns:
      The embeddingMetadata.

    • getEmbeddingMetadataOrBuilder

      com.google.protobuf.StructOrBuilder getEmbeddingMetadataOrBuilder()
       Optional. The key-value map of additional metadata for the datapoint.
      .google.protobuf.Struct embedding_metadata = 8 [(.google.api.field_behavior) = OPTIONAL];