Class Data

Methods

flush

  • Milvus temporarily buffers the newly inserted vectors in the cache. Call flush() to persist them to the object storage.

    Parameters

    • data: FlushReq
      Property Type Description
      collection_names String[] Array of collection names

    Returns Promise<FlushResult>

    Property Description
    status { error_code: number, reason: string }

    Example

     new milvusClient(MILUVS_ADDRESS).dataManager.flush({
        collection_names: ['my_collection'],
     });
    

insert

  • Insert data into Milvus.

    Parameters

    • data: InsertReq
      Property Type Description
      collection_name String Collection name
      partition_name(optional) String Partition name
      fields_data { [x: string]: any }[] If the field type is binary, the vector data length needs to be dimension / 8
      hash_keys(optional) Number[] The hash value depends on the primarykey value

    Returns Promise<MutationResult>

    Property Description
    status { error_code: number, reason: string }
    succ_index Index array of the successfully inserted data
    err_index Index array of the unsuccessfully inserted data
    IDs ID array of the successfully inserted data

    Example

     new milvusClient(MILUVS_ADDRESS).dataManager.insert({
       collection_name: COLLECTION_NAME,
       fields_data: [{
         vector_field: [1,2,2,4],
         scalar_field: 1
       }]
     });
    

query

  • Query vector data in Milvus. Current release of Milvus only supports expression as fieldname in [id1,id2,id3]

    Parameters

    • data: QueryReq
      Property Type Description
      collection_name String Collection name
      expr String Scalar field filter expression
      partitions_names(optional) String[] Array of partition names
      output_fields String[] Vector or scalar field to be returned

    Returns Promise<QueryResults>

    Property Description
    status { error_code: number,reason:string }
    data Data of all fields that you defined in output_fields, {field_name: value}[]

    Example

     new milvusClient(MILUVS_ADDRESS).dataManager.query({
       collection_name: 'my_collection',
       expr: "age in [1,2,3,4,5,6,7,8]",
       output_fields: ["age"],
     });
    

search

  • Perform vector similarity search.

    Parameters

    • data: SearchReq
      Property Type Description
      collection_name String Collection name
      partition_names(optional) String[] Array of partition names
      expr(optional) String Scalar field filter expression
      search_params Object anns_field: vector field name
      topk: search result counts
      metric_type
      params: search params
      vectors Number[][] Original vector to search with
      output_fields(optional) String[] Support scalar field
      vector_type enum Binary field -> 100, Float field -> 101

    Returns Promise<SearchResults>

    Property Description
    status { error_code: number, reason: string }
    succ_index Insert successful index array
    err_index Insert failed index array
    IDs Insert successful id array

    Example

     new milvusClient(MILUVS_ADDRESS).dataManager.search({
      collection_name: COLLECTION_NAME,
      expr: "",
      vectors: [[1, 2, 3, 4]],
      search_params: {
        anns_field: VECTOR_FIELD_NAME,
        topk: "4",
        metric_type: "L2",
        params: JSON.stringify({ nprobe: 1024 }),
      },
      output_fields: ["age", "time"],
      vector_type: 100,
     });