DashVector:DashVector data type definitions

Python

@dataclass(frozen=True)
class Doc(object):
    id: str                                            # The primary key.
    vector: Union[List[int], List[float], np.ndarray]  # The vector.
    sparse_vector: Optional[Dict[int, float]] = None   # The sparse vector.
    fields: Optional[FieldDataDict] = None             # The custom fields in the document.
    score: float = 0.0                                 # The similarity between vectors.

Java

@Data
@Builder
public class Doc {
  // The primary key.
  @NonNull private String id;
  // The vector.
  @NonNull private Vector vector;
  // The sparse vector.
  private TreeMap<Integer, Float> sparseVector;
  // The custom fields in the document.
  @Builder.Default private Map<String, Object> fields = new HashMap<>();
  // The similarity between vectors.
  private float score;

  public void addField(String key, String value) {
    this.fields.put(key, value);
  }

  public void addField(String key, Integer value) {
    this.fields.put(key, value);
  }

  public void addField(String key, Float value) {
    this.fields.put(key, value);
  }

  public void addField(String key, Boolean value) {
    this.fields.put(key, value);
  }
}

Field reference

Supported field data types

Python

@dataclass(frozen=True)
class CollectionMeta(object):
    name: str                      # The name of the collection.
    dimension: int                 # The number of vector dimensions.
    dtype: str                     # The data type of the vector. Valid values: float and int.
    metric: str                    # The distance metric. Valid values: euclidean, dotproduct, and cosine.
    status: Status                 # The status of the collection.
    fields: Dict[str, str]         # The fields in the collection. Supported data types of fields: float, bool, int, and str.
    partitions: Dict[str, Status]  # The information about the partitions in the collection.

Java

@Getter
public class CollectionMeta {
  // The name of the collection.
  private final String name;
  // The number of vector dimensions.
  private final int dimension;
  // The data type of the vector. Valid values: float and int.
  private final CollectionInfo.DataType dataType;
  // The distance metric. Valid values: euclidean, dotproduct, and cosine.
  private final CollectionInfo.Metric metric;
  // The status of the collection.
  private final String status;
  // The fields in the collection. Supported data types of fields: float, bool, int, and str.
  private final Map<String, FieldType> fieldsSchema;
  // The information about the partitions in the collection.
  private final Map<String, Status> partitionStatus;

  public CollectionMeta(CollectionInfo collectionInfo) {
    this.name = collectionInfo.getName();
    this.dimension = collectionInfo.getDimension();
    this.dataType = collectionInfo.getDtype();
    this.metric = collectionInfo.getMetric();
    this.status = collectionInfo.getStatus().name();
    this.fieldsSchema = collectionInfo.getFieldsSchemaMap();
    this.partitionStatus = collectionInfo.getPartitionsMap();
  }
}

Field reference

Distance metrics

Vector data types

Python

@dataclass(frozen=True)
class CollectionStats(object):
    total_doc_count: int                    # The total number of documents inserted into the collection.
    index_completeness: float               # The completeness of data insertion into the collection.
    partitions: Dict[str, PartitionStats]   # The information about the partitions in the collection.

Java

@Getter
public class CollectionStats {
  // The total number of documents inserted into the collection.
  private final long totalDocCount;
  // The completeness of data insertion into the collection.
  private final float indexCompleteness;
  // The information about the partitions in the collection.
  private final Map<String, PartitionStats> partitions;

  public CollectionStats(StatsCollectionResponse.CollectionStats collectionStats) {
    this.totalDocCount = collectionStats.getTotalDocCount();
    this.indexCompleteness = collectionStats.getIndexCompleteness();
    this.partitions = new HashMap<>();
    collectionStats
        .getPartitionsMap()
        .forEach((key, value) -> this.partitions.put(key, new PartitionStats(value)));
  }
}

Field reference

Python

@dataclass(frozen=True)
class PartitionStats(object):
    total_doc_count: int                    # The total number of documents in the partition.

Java

@Getter
public class PartitionStats {
  // The total number of documents in the partition.
  private final long totalDocCount;

  public PartitionStats(com.aliyun.dashvector.proto.PartitionStats partitionStats) {
    this.totalDocCount = partitionStats.getTotalDocCount();
  }
}

Field reference