addCollectionFields()

This operation adds a list of new scalar fields to an existing collection without recreating it. These fields become available almost immediately with minimal delay due to internal schema synchronization.

addCollectionFields(data: AddCollectionFieldReq): Promise<ResStatus>

Request Syntax

milvusClient.addCollectionFields({
    collection_name: string,
    db_name?: string,
    field: FieldType,
    timeout?: number
})

PARAMETERS:

• collection_name (string) -

  The name of the target collection.

• db_name (string) -

  The name of the target database.

• fields (FieldType[]) -

  The configurations of the fields to add, each of which is a FieldType object that has the following fields:

  • name (string) -

    The name of the field.

  • data_type (string) -

    The data type of the field. For an enumeration of all available data types, please see DataType.

  • description (string) -

    The description of the field.

  • is_clustering_key (boolean) -

    A boolean value indicating whether this field will work as the clustering key.

  • is_partition_key (boolean) -

    A boolean value indicating whether this field will work as the partition key field.

  • is_primary_key (boolean) -

    Whether the field will work as the primary key.

    The value defaults to False. Setting this to True makes the field a primary key field which is unique throughout the collection.

  • type_params (string | number) -

    Other parameters for the field.

    • auto_id (boolean) -

      Whether the primary field automatically increments upon data insertions into this collection.

      The value defaults to False. Setting this to True makes the primary field automatically increment. Skip this parameter if you need to set up a collection with a customized schema.

    • dim (string | number) -

      The dimensionality of the collection field that holds vector embeddings.

      The value should be an integer greater than 1 and is usually determined by the model you use to generate vector embeddings.

    • element_type (string) -

      The data type of the elements in an array.

      This parameter applies if the current field is an array field.

    • max_capacity (string | number) -

      The number of elements in an array.

      This parameter applies if the current field is an array field.

    • max_length (string) -

      The maximum lengths of a string in this field.

      This is required when the data_type of this field is VarChar.

    • type_params (object) -

      Extra parameters for the current field in key-value pairs.

  • nullable (boolean) -

    A Boolean parameter that specifies whether the field can accept null values. Valid values:

    • true: The field can contain null values, indicating that the field is optional, and missing data is permitted for entries.

    • false (default): The field must contain a valid value for each entity; missing data is not allowed, making the field mandatory.

    For more information, refer to Nullable & Default.

  • default_value (object)

    Sets a default value for a specific field in a collection schema when creating it. This is particularly useful when you want certain fields to have an initial value even if no value is explicitly provided during data insertion.

  • enable_analyzer (boolean) -

    Whether to enable text analysis for the specified VarChar field. When set to true, it instructs Milvus to use a text analyzer, which tokenizes and filters the text content of the field.

  • enable_match (boolean)

    Whether to enable keyword matching for the specified VarChar field. When set to true, Milvus creates an inverted index for the field, allowing for quick and efficient keyword lookups. enable_match works in conjunction with enable_analyzer to provide structured term-based text search, with enable_analyzer handling tokenization and enable_match handling the search operations on these tokens.

  • analyzer_params (object)

    Configures the analyzer for text processing, specifically for VarChar fields. This parameter configures tokenizer and filter settings, particularly for text fields used in keyword matching or full text search. Depending on the type of analyzer, it can be configured in either of the following methods:

    • Built-in analyzer

      const analyzer_params: { type: 'english' };
      

      • type (string) -

        Pre-configured analyzer type built into Milvus, which can be used out-of-the-box by specifying its name. Possible values: standard, english, chinese. For more information, refer to Standard Analyzer, English Analyzer, and Chinese Analyzer.

    • Custom analyzer

      const analyzer_params: {
          "tokenizer": "standard",
          "filter": ["lowercase"],
      };
      

      • tokenizer (string) -

        Defines the tokenizer type. Possible values: standard (default), whitespace, jieba. For more information, refer to Standard Tokenizer, Whitespace Tokenizer, and Jieba Tokenizer.

      • filter (list) -

        Lists filters to refine tokens produced by the tokenizer, with options for built-in filters and custom filters. For more information, refer to Alphanumonly Filter and others.

• timeout (number) -

  The timeout duration for this operation. Setting this to None indicates that this operation timeouts when any response arrives or any error occurs.

RETURNS Promise

This method returns a promise that resolves to a ResStatus object.

{
    code: number,
    error_code: string | number,
    reason: string
}

PARAMETERS:

• code (number) -

  A code that indicates the operation result. It remains 0 if this operation succeeds.

• error_code (string | number) -

  An error code that indicates an occurred error. It remains Success if this operation succeeds.

• reason (string) -

  The reason that indicates the reason for the reported error. It remains an empty string if this operation succeeds.

Example

const milvusClient = new MilvusClient(MILVUS_ADDRESS);
const resStatus = await milvusClient.addCollectionFields({
  collection_name: 'my_collection',
  fields: [
    {
      name: 'new_field_1',
      data_type: 'Int64',
      is_primary_key: false,
      description: 'First new field'
    },
    {
      name: 'new_field_2',
      data_type: 'FloatVector',
      dim: 128,
      description: 'Second new field'
    }
  ]
});