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Version: User Guides (Cloud)

Array Field

An ARRAY field stores an ordered set of elements of the same data type. Here's an example of how ARRAY fields store data:

{
  "tags": ["pop", "rock", "classic"],
  "ratings": [5, 4, 3]
}

Limits​

  • Default Values: ARRAY fields do not support default values. However, you can set the nullable attribute to True to allow null values. For details, refer to Nullable & Default.

  • Data Type: All elements in an Array field must have the same data type, as specified by the element_type.

  • Array Capacity: The number of elements in an Array field must be less than or equal to the maximum capacity defined when the Array was created, as specified by max_capacity.

  • String Handling: String values in Array fields are stored as-is, without semantic escaping or conversion. For example, 'a"b', "a'b", 'a\'b', and "a\"b" are stored as entered, while 'a'b' and "a"b" are considered invalid values.

Add ARRAY field​

To use ARRAY fields Zilliz Cloud clusters, define the relevant field type when creating the collection schema. This process includes:

  1. Setting datatype to the supported Array data type, ARRAY.

  2. Using the element_type parameter to specify the data type of elements in the array. This can be any scalar data type supported by Zilliz Cloud clusters, such as VARCHAR or INT64. All elements in the same Array must be of the same data type.

  3. Using the max_capacity parameter to define the maximum capacity of the array, i.e., the maximum number of elements it can contain.

Here’s how to define a collection schema that includes ARRAY fields:

📘Notes

If you set enable_dynamic_fields=True when defining the schema, Zilliz Cloud allows you to insert scalar fields that were not defined in advance. However, this may increase the complexity of queries and management, potentially impacting performance. For more information, refer to Dynamic Field.

# Import necessary libraries
from pymilvus import MilvusClient, DataType

# Define server address
SERVER_ADDR = "YOUR_CLUSTER_ENDPOINT"

# Create a MilvusClient instance
client = MilvusClient(uri=SERVER_ADDR)

# Define the collection schema
schema = client.create_schema(
    auto_id=False,
    enable_dynamic_fields=True,
)

#  Add `tags` and `ratings` ARRAY fields with nullable=True
schema.add_field(field_name="tags", datatype=DataType.ARRAY, element_type=DataType.VARCHAR, max_capacity=10, max_length=65535, nullable=True)
schema.add_field(field_name="ratings", datatype=DataType.ARRAY, element_type=DataType.INT64, max_capacity=5, nullable=True)
schema.add_field(field_name="pk", datatype=DataType.INT64, is_primary=True)
schema.add_field(field_name="embedding", datatype=DataType.FLOAT_VECTOR, dim=3)
export arrayField1='{
    "fieldName": "tags",
    "dataType": "Array",
    "elementDataType": "VarChar",
    "elementTypeParams": {
        "max_capacity": 10,
        "max_length": 65535
    }
}'

export arrayField2='{
    "fieldName": "ratings",
    "dataType": "Array",
    "elementDataType": "Int64",
    "elementTypeParams": {
        "max_capacity": 5
    }
}'

export pkField='{
    "fieldName": "pk",
    "dataType": "Int64",
    "isPrimary": true
}'

export vectorField='{
    "fieldName": "embedding",
    "dataType": "FloatVector",
    "elementTypeParams": {
        "dim": 3
    }
}'

export schema="{
    \"autoID\": false,
    \"fields\": [
        $arrayField1,
        $arrayField2,
        $pkField,
        $vectorField
    ]
}"

Set index params​

Indexing helps improve search and query performance. In Zilliz Cloud clusters, indexing is mandatory for vector fields but optional for scalar fields.

The following example creates indexes on the vector field embedding and the ARRAY field tags, both using the AUTOINDEX index type. With this type, Milvus automatically selects the most suitable index based on the data type.

# Set index params

index_params = client.prepare_index_params()

# Index `age` with AUTOINDEX
index_params.add_index(
    field_name="tags",
    index_type="AUTOINDEX",
    index_name="tags_index"
)

# Index `embedding` with AUTOINDEX and specify similarity metric type
index_params.add_index(
    field_name="embedding",
    index_type="AUTOINDEX",  # Use automatic indexing to simplify complex index settings
    metric_type="COSINE"  # Specify similarity metric type, options include L2, COSINE, or IP
)

Create collection​

Once the schema and index are defined, create a collection that includes ARRAY fields.

client.create_collection(
    collection_name="my_array_collection",
    schema=schema,
    index_params=index_params
)

Insert data​

After creating the collection, you can insert data that includes ARRAY fields.

# Sample data
data = [
  {
      "tags": ["pop", "rock", "classic"],
      "ratings": [5, 4, 3],
      "pk": 1,
      "embedding": [0.12, 0.34, 0.56]
  },
  {
      "tags": None,  # Entire ARRAY is null
      "ratings": [4, 5],
      "pk": 2,
      "embedding": [0.78, 0.91, 0.23]
  },
  {  # The tags field is completely missing
      "ratings": [9, 5],
      "pk": 3,
      "embedding": [0.18, 0.11, 0.23]
  }
]

client.insert(
    collection_name="my_array_collection",
    data=data
)

Query with filter expressions​

After inserting entities, use the query method to retrieve entities that match the specified filter expressions.

To retrieve entities where the tags is not null:

# Query to exclude entities where `tags` is not null

filter = 'tags IS NOT NULL'

res = client.query(
    collection_name="my_array_collection",
    filter=filter,
    output_fields=["tags", "ratings", "pk"]
)

print(res)

# Example output:
# data: [
#     "{'tags': ['pop', 'rock', 'classic'], 'ratings': [5, 4, 3], 'pk': 1}"
# ]

To retrieve entities where the value of the first element of ratings is greater than 4:

filter = 'ratings[0] > 4'

res = client.query(
    collection_name="my_array_collection",
    filter=filter,
    output_fields=["tags", "ratings", "embedding"]
)

print(res)

# Example output:
# data: [
#     "{'tags': ['pop', 'rock', 'classic'], 'ratings': [5, 4, 3], 'embedding': [0.12, 0.34, 0.56], 'pk': 1}",
#     "{'tags': None, 'ratings': [9, 5], 'embedding': [0.18, 0.11, 0.23], 'pk': 3}"
# ]

Vector search with filter expressions​

In addition to basic scalar field filtering, you can combine vector similarity searches with scalar field filters. For example, the following code shows how to add a scalar field filter to a vector search:

filter = 'tags[0] == "pop"'

res = client.search(
    collection_name="my_array_collection",
    data=[[0.3, -0.6, 0.1]],
    limit=5,
    search_params={"params": {"nprobe": 10}},
    output_fields=["tags", "ratings", "embedding"],
    filter=filter
)

print(res)

# Example output:
# data: [
#     "[{'id': 1, 'distance': -0.2479381263256073, 'entity': {'tags': ['pop', 'rock', 'classic'], 'ratings': [5, 4, 3], 'embedding': [0.11999999731779099, 0.3400000035762787, 0.5600000023841858]}}]"
# ]

Additionally, Zilliz Cloud supports advanced Array filtering operators like ARRAY_CONTAINS, ARRAY_CONTAINS_ALL, ARRAY_CONTAINS_ANY, and ARRAY_LENGTH to further enhance query capabilities. For more details, refer to ARRAY Operators.