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 toTrue
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:
-
Setting
datatype
to the supported Array data type,ARRAY
. -
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 asVARCHAR
orINT64
. All elements in the same Array must be of the same data type. -
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:
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.
- Python
- Java
- Go
- NodeJS
- HTTP
# 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)
import io.milvus.v2.client.ConnectConfig;
import io.milvus.v2.client.MilvusClientV2;
import io.milvus.v2.common.DataType;
import io.milvus.v2.service.collection.request.AddFieldReq;
import io.milvus.v2.service.collection.request.CreateCollectionReq;
MilvusClientV2 client = new MilvusClientV2(ConnectConfig.builder()
.uri("YOUR_CLUSTER_ENDPOINT")
.build());
CreateCollectionReq.CollectionSchema schema = client.createSchema();
schema.setEnableDynamicField(true);
schema.addField(AddFieldReq.builder()
.fieldName("tags")
.dataType(DataType.Array)
.elementType(DataType.VarChar)
.maxCapacity(10)
.maxLength(65535)
.isNullable(true)
.build());
schema.addField(AddFieldReq.builder()
.fieldName("ratings")
.dataType(DataType.Array)
.elementType(DataType.Int64)
.maxCapacity(5)
.isNullable(true)
.build());
schema.addField(AddFieldReq.builder()
.fieldName("pk")
.dataType(DataType.Int64)
.isPrimaryKey(true)
.build());
schema.addField(AddFieldReq.builder()
.fieldName("embedding")
.dataType(DataType.FloatVector)
.dimension(3)
.build());
// go
import { MilvusClient, DataType } from "@zilliz/milvus2-sdk-node";
const schema = [
{
name: "tags",
data_type: DataType.Array,
element_type: DataType.VarChar,
max_capacity: 10,
max_length: 65535
},
{
name: "rating",
data_type: DataType.Array,
element_type: DataType.Int64,
max_capacity: 5,
},
{
name: "pk",
data_type: DataType.Int64,
is_primary_key: true,
},
{
name: "embedding",
data_type: DataType.FloatVector,
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.
- Python
- Java
- Go
- NodeJS
- cURL
# 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
)
import io.milvus.v2.common.IndexParam;
import java.util.*;
List<IndexParam> indexes = new ArrayList<>();
indexes.add(IndexParam.builder()
.fieldName("tags")
.indexName("tags_index")
.indexType(IndexParam.IndexType.AUTOINDEX)
.build());
indexes.add(IndexParam.builder()
.fieldName("embedding")
.indexType(IndexParam.IndexType.AUTOINDEX)
.metricType(IndexParam.MetricType.COSINE)
.build());
// go
const indexParams = [{
index_name: 'inverted_index',
field_name: 'tags',
index_type: IndexType.AUTOINDEX,
)];
indexParams.push({
index_name: 'embedding_index',
field_name: 'embedding',
index_type: IndexType.AUTOINDEX,
});
export indexParams='[
{
"fieldName": "tags",
"indexName": "inverted_index",
"indexType": "AUTOINDEX"
},
{
"fieldName": "embedding",
"metricType": "COSINE",
"indexType": "AUTOINDEX"
}
]'
Create collection
Once the schema and index are defined, create a collection that includes ARRAY fields.
- Python
- Java
- Go
- NodeJS
- cURL
client.create_collection(
collection_name="my_array_collection",
schema=schema,
index_params=index_params
)
CreateCollectionReq requestCreate = CreateCollectionReq.builder()
.collectionName("my_array_collection")
.collectionSchema(schema)
.indexParams(indexes)
.build();
client.createCollection(requestCreate);
// go
client.create_collection({
collection_name: "my_array_collection",
schema: schema,
index_params: indexParams
})
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/collections/create" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d "{
\"collectionName\": \"my_array_collection\",
\"schema\": $schema,
\"indexParams\": $indexParams
}"
Insert data
After creating the collection, you can insert data that includes ARRAY fields.
- Python
- Java
- Go
- NodeJS
- cURL
# Sample data
data = [
{
"tags": ["pop", "rock", "classic"],
"ratings": [5, 4, 3],
"pk": 1,
"embedding": [0.12, 0.34, 0.56]
},
{
"tags": None, # Entire ARRAY is null
"ratings": [4, 5],
"pk": 2,
"embedding": [0.78, 0.91, 0.23]
},
{ # The tags field is completely missing
"ratings": [9, 5],
"pk": 3,
"embedding": [0.18, 0.11, 0.23]
}
]
client.insert(
collection_name="my_array_collection",
data=data
)
import com.google.gson.Gson;
import com.google.gson.JsonObject;
import io.milvus.v2.service.vector.request.InsertReq;
import io.milvus.v2.service.vector.response.InsertResp;
List<JsonObject> rows = new ArrayList<>();
Gson gson = new Gson();
rows.add(gson.fromJson("{\"tags\": [\"pop\", \"rock\", \"classic\"], \"ratings\": [5, 4, 3], \"pk\": 1, \"embedding\": [0.12, 0.34, 0.56]}", JsonObject.class));
rows.add(gson.fromJson("{\"tags\": null, \"ratings\": [4, 5], \"pk\": 2, \"embedding\": [0.78, 0.91, 0.23]}", JsonObject.class));
rows.add(gson.fromJson("{\"ratings\": [9, 5], \"pk\": 3, \"embedding\": [0.18, 0.11, 0.23]}", JsonObject.class));
InsertResp insertR = client.insert(InsertReq.builder()
.collectionName("my_array_collection")
.data(rows)
.build());
// go
const data = [
{
"tags": ["pop", "rock", "classic"],
"ratings": [5, 4, 3],
"pk": 1,
"embedding": [0.12, 0.34, 0.56]
},
{
"tags": ["jazz", "blues"],
"ratings": [4, 5],
"pk": 2,
"embedding": [0.78, 0.91, 0.23]
},
{
"tags": ["electronic", "dance"],
"ratings": [3, 3, 4],
"pk": 3,
"embedding": [0.67, 0.45, 0.89]
}
];
client.insert({
collection_name: "my_array_collection",
data: data,
});
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/insert" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
"data": [
{
"tags": ["pop", "rock", "classic"],
"ratings": [5, 4, 3],
"pk": 1,
"embedding": [0.12, 0.34, 0.56]
},
{
"tags": ["jazz", "blues"],
"ratings": [4, 5],
"pk": 2,
"embedding": [0.78, 0.91, 0.23]
},
{
"tags": ["electronic", "dance"],
"ratings": [3, 3, 4],
"pk": 3,
"embedding": [0.67, 0.45, 0.89]
}
],
"collectionName": "my_array_collection"
}'
Query with filter expressions
After inserting entities, use the query
method to retrieve entities that match the specified filter expressions.
To retrieve entities where the tags
is not null:
- Python
- Java
- Go
- NodeJS
- cURL
# Query to exclude entities where `tags` is not null
filter = 'tags IS NOT NULL'
res = client.query(
collection_name="my_array_collection",
filter=filter,
output_fields=["tags", "ratings", "pk"]
)
print(res)
# Example output:
# data: [
# "{'tags': ['pop', 'rock', 'classic'], 'ratings': [5, 4, 3], 'pk': 1}"
# ]
import io.milvus.v2.service.vector.request.QueryReq;
import io.milvus.v2.service.vector.response.QueryResp;
String filter = "tags IS NOT NULL";
QueryResp resp = client.query(QueryReq.builder()
.collectionName("my_array_collection")
.filter(filter)
.outputFields(Arrays.asList("tags", "ratings", "pk"))
.build());
System.out.println(resp.getQueryResults());
// Output
//
// [QueryResp.QueryResult(entity={ratings=[5, 4, 3], pk=1, tags=[pop, rock, classic]})]
// go
client.query({
collection_name: 'my_array_collection',
filter: 'ratings[0] < 4',
output_fields: ['tags', 'ratings', 'embedding']
});
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/query" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
"collectionName": "my_array_collection",
"filter": "ratings[0] < 4",
"outputFields": ["tags", "ratings", "embedding"]
}'
# {"code":0,"cost":0,"data":[{"embedding":[0.67,0.45,0.89],"pk":3,"ratings":{"Data":{"LongData":{"data":[3,3,4]}}},"tags":{"Data":{"StringData":{"data":["electronic","dance"]}}}}]}
To retrieve entities where the value of the first element of ratings
is greater than 4:
- Python
- Java
- Go
- NodeJS
- cURL
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}"
# ]
String filter = "ratings[0] > 4"
QueryResp resp = client.query(QueryReq.builder()
.collectionName("my_array_collection")
.filter(filter)
.outputFields(Arrays.asList("tags", "ratings", "pk"))
.build());
System.out.println(resp.getQueryResults());
// Output
// [
// QueryResp.QueryResult(entity={ratings=[5, 4, 3], pk=1, tags=[pop, rock, classic]}),
// QueryResp.QueryResult(entity={ratings=[9, 5], pk=3, tags=[]})
// ]
// go
// node
const filter = 'ratings[0] > 4';
const res = await client.query({
collection_name:"my_array_collection",
filter:filter,
output_fields: ["tags", "ratings", "embedding"]
});
console.log(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}"
// ]
# restful
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:
- Python
- Java
- Go
- NodeJS
- cURL
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]}}]"
# ]
import io.milvus.v2.service.vector.request.SearchReq;
import io.milvus.v2.service.vector.response.SearchResp;
String filter = "tags[0] == \"pop\"";
SearchResp resp = client.search(SearchReq.builder()
.collectionName("my_array_collection")
.annsField("embedding")
.data(Collections.singletonList(new FloatVec(new float[]{0.3f, -0.6f, 0.1f})))
.topK(5)
.outputFields(Arrays.asList("tags", "ratings", "embedding"))
.filter(filter)
.build());
System.out.println(resp.getSearchResults());
// Output
//
// [[SearchResp.SearchResult(entity={ratings=[5, 4, 3], embedding=[0.12, 0.34, 0.56], tags=[pop, rock, classic]}, score=-0.24793813, id=1)]]
// go
client.search({
collection_name: 'my_array_collection',
data: [0.3, -0.6, 0.1],
limit: 5,
output_fields: ['tags', 'ratings', 'embdding'],
filter: 'tags[0] == "pop"'
});
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/search" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
"collectionName": "my_array_collection",
"data": [
[0.3, -0.6, 0.1]
],
"annsField": "embedding",
"limit": 5,
"filter": "tags[0] == \"pop\"",
"outputFields": ["tags", "ratings", "embedding"]
}'
# {"code":0,"cost":0,"data":[{"distance":-0.24793813,"embedding":[0.12,0.34,0.56],"id":1,"ratings":{"Data":{"LongData":{"data":[5,4,3]}}},"tags":{"Data":{"StringData":{"data":["pop","rock","classic"]}}}}]}
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.