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Manage Collections

This guide walks you through creating and managing collections using the SDK of your choice.

Before you start

Overview

In Milvus, you store your vector embeddings in collections. All vector embeddings within a collection share the same dimensionality and distance metric for measuring similarity.

Milvus collections support dynamic fields (i.e., fields not pre-defined in the schema) and automatic incrementation of primary keys.

To accommodate different preferences, Milvus offers two methods for creating a collection. One provides a quick setup, while the other allows for detailed customization of the collection schema and index parameters.

Additionally, you can view, load, release, and drop a collection when necessary.

Create Collection

You can create a collection in either of the following manners:

  • Quick setup

    In this manner, you can create a collection by simply giving it a name and specifying the number of dimensions of the vector embeddings to be stored in this collection. For details, refer to Quick setup.

  • Customized setup

    Instead of letting In Milvus decide almost everything for your collection, you can determine the schema and index parameters of the collection on your own. For details, refer to Customized setup.

Quick setup

Against the backdrop of the great leap in the AI industry, most developers just need a simple yet dynamic collection to start with. Milvus allows a quick setup of such a collection with just three arguments:

  • Name of the collection to create,

  • Dimension of the vector embeddings to insert, and

  • Metric type used to measure similarities between vector embeddings.

For quick setup, use the create_collection() method of the MilvusClient class to create a collection with the specified name and dimension.

For quick setup, use the createCollection() method of the MilvusClientV2 class to create a collection with the specified name and dimension.

For quick setup, use the createCollection() method of the MilvusClient class to create a collection with the specified name and dimension.

For quick setup, use the POST /v2/vectordb/collections/create API endpoint to create a collection with the specified name and dimension.

Python Java Node.js cURL 
from pymilvus import MilvusClient, DataType

# 1. Set up a Milvus client
client = MilvusClient(
    uri="http://localhost:19530"
)

# 2. Create a collection in quick setup mode
client.create_collection(
    collection_name="quick_setup",
    dimension=5
)

res = client.get_load_state(
    collection_name="quick_setup"
)

print(res)

# Output
#
# {
#     "state": "<LoadState: Loaded>"
# }

import io.milvus.v2.client.ConnectConfig;
import io.milvus.v2.client.MilvusClientV2;
import io.milvus.v2.service.collection.request.GetLoadStateReq;
import io.milvus.v2.service.collection.request.CreateCollectionReq;

String CLUSTER_ENDPOINT = "http://localhost:19530";

// 1. Connect to Milvus server
ConnectConfig connectConfig = ConnectConfig.builder()
    .uri(CLUSTER_ENDPOINT)
    .build();

MilvusClientV2 client = new MilvusClientV2(connectConfig);

// 2. Create a collection in quick setup mode
CreateCollectionReq quickSetupReq = CreateCollectionReq.builder()
    .collectionName("quick_setup")
    .dimension(5)
    .build();

client.createCollection(quickSetupReq);

// Thread.sleep(5000);

GetLoadStateReq quickSetupLoadStateReq = GetLoadStateReq.builder()
    .collectionName("quick_setup")
    .build();

Boolean res = client.getLoadState(quickSetupLoadStateReq);

System.out.println(res);

// Output:
// true

address = "http://localhost:19530"

// 1. Set up a Milvus Client
client = new MilvusClient({address});

// 2. Create a collection in quick setup mode
let res = await client.createCollection({
    collection_name: "quick_setup",
    dimension: 5,
});  

console.log(res.error_code)

// Output
// 
// Success
// 

res = await client.getLoadState({
    collection_name: "quick_setup"
})

console.log(res.state)

// Output
// 
// LoadStateLoaded
// 

$ export MILVUS_URI="localhost:19530"

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/create" \
-H "Content-Type: application/json" \
-d '{
  "collectionName": "quick_setup",
  "dimension": 5
}'

# Output
#
# {
#     "code": 0,
#     "data": {},
# }

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/get_load_state" \
-H "Content-Type: application/json" \
-d '{
  "collectionName": "quick_setup"
}'

# {
#     "code": 0,
#     "data": {
#         "loadProgress": 100,
#         "loadState": "LoadStateLoaded"
#     }
# }

The collection generated in the above code contains only two fields: id (as the primary key) and vector (as the vector field), with auto_id and enable_dynamic_field settings enabled by default.

  • auto_id

    Enabling this setting ensures that the primary key increments automatically. There's no need for manual provision of primary keys during data insertion.

  • enable_dynamic_field

    When enabled, all fields, excluding id and vector in the data to be inserted, are treated as dynamic fields. These additional fields are saved as key-value pairs within a special field named $meta. This feature allows the inclusion of extra fields during data insertion.

The automatically indexed and loaded collection from the provided code is ready for immediate data insertions.

Customized setup

Instead of letting Milvus decide almost everything for your collection, you can determine the schema and index parameters of the collection on your own.

Step 1: Set up schema

A schema defines the structure of a collection. Within the schema, you have the option to enable or disable enable_dynamic_field, add pre-defined fields, and set attributes for each field. For a detailed explanation of the concept and available data types, refer to Schema Explained.

To set up a schema, use create_schema() to create a schema object and add_field() to add fields to the schema.

To set up a schema, use createSchema() to create a schema object and addField() to add fields to the schema.

To set up a schema, use createCollection().

To set up a schema, you need to define a JSON object that follows the schema format as displayed on the POST /v2/vectordb/collections/create API endpoint reference page.

Python Java Node.js cURL 
# 3. Create a collection in customized setup mode

# 3.1. Create schema
schema = MilvusClient.create_schema(
    auto_id=False,
    enable_dynamic_field=True,
)

# 3.2. Add fields to schema
schema.add_field(field_name="my_id", datatype=DataType.INT64, is_primary=True)
schema.add_field(field_name="my_vector", datatype=DataType.FLOAT_VECTOR, dim=5)

import io.milvus.v2.common.DataType;
import io.milvus.v2.service.collection.request.CreateCollectionReq;

// 3. Create a collection in customized setup mode

// 3.1 Create schema
CreateCollectionReq.CollectionSchema schema = client.createSchema();

// 3.2 Add fields to schema
schema.addField(AddFieldReq.builder()
    .fieldName("my_id")
    .dataType(DataType.Int64)
    .isPrimaryKey(true)
    .autoID(false)
    .build());

schema.addField(AddFieldReq.builder()
    .fieldName("my_vector")
    .dataType(DataType.FloatVector)
    .dimension(5)
    .build());

// 3. Create a collection in customized setup mode
// 3.1 Define fields
const fields = [
    {
        name: "my_id",
        data_type: DataType.Int64,
        is_primary_key: true,
        auto_id: false
    },
    {
        name: "my_vector",
        data_type: DataType.FloatVector,
        dim: 5
    },
]

export fields='[{ \
    "fieldName": "my_id", \
    "dataType": "Int64", \
    "isPrimary": true \
}, \
{ \
    "fieldName": "my_vector", \
    "dataType": "FloatVector", \
    "elementTypeParams": { \
        "dim": 5 \
    } \
}]'
Parameter Description
auto_id Whether allows the primary field to automatically increment.
Setting this to True makes the primary field automatically increment. In this case, the primary field should not be included in the data to insert to avoid errors.
enable_dynamic_field Whether allows Milvus saves the values of undefined fields in a dynamic field if the data being inserted into the target collection includes fields that are not defined in the collection's schema.
When you set this to True, Milvus will create a field called $meta to store any undefined fields and their values from the data that is inserted.
field_name The name of the field.
datatype The data type of the field. For a list of available data types, refer to DataType.
is_primary Whether the current field is the primary field in a collection.
Each collection has only one primary field. A primary field should be of either the DataType.INT64 type or the DataType.VARCHAR type.
dim The dimension of the vector embeddings.
This is mandatory for a field of the DataType.FLOAT_VECTOR, DataType.BINARY_VECTOR, DataType.FLOAT16_VECTOR, or DataType.BFLOAT16_VECTOR type. If you use DataType.SPARSE_FLOAT_VECTOR, omit this parameter.
Parameter Description
fieldName The name of the field.
dataType The data type of the field. For a list of available data types, refer to DataType.
isPrimaryKey Whether the current field is the primary field in a collection.
Each collection has only one primary field. A primary field should be of either the DataType.Int64 type or the DataType.VarChar type.
autoID Whether allows the primary field to automatically increment.
Setting this to true makes the primary field automatically increment. In this case, the primary field should not be included in the data to insert to avoid errors.
dimension The dimension of the vector embeddings.
This is mandatory for a field of the DataType.FloatVector, DataType.BinaryVector, DataType.Float16Vector, or DataType.BFloat16Vector type.
Parameter Description
name The name of the field.
data_type The data type of the field. For an enumeration of all available data types, refer to DataType.
is_primary_key Whether the current field is the primary field in a collection.
Each collection has only one primary field. A primary field should be of either the DataType.INT64 type or the DataType.VARCHAR type.
auto_id 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 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.
Parameter Description
fieldName The name of the field to create in the target collection.
dataType The data type of the field values.
isPrimary Whether the current field is the primary field. Setting this to True makes the current field the primary field.
elementTypeParams Extra field parameters.
dim An optional parameter for FloatVector or BinaryVector fields that determines the vector dimension.

Step 2: Set up index parameters

Index parameters dictate how Milvus organizes your data within a collection. You can tailor the indexing process for specific fields by adjusting their metric_type and index_type. For the vector field, you have the flexibility to select COSINE, L2, IP, HAMMING, or JACCARD as the metric_type, depending on the type of vectors you are working with. For more information, refer to Similarity Metrics.

To set up index parameters, use prepare_index_params() to prepare index parameters and add_index() to add the index.

To set up index parameters, use IndexParam.

To set up index parameters, use createIndex().

To set up index parameters, you need to define a JSON object that follows the index parameters format as displayed on the POST /v2/vectordb/collections/create API endpoint reference page.

Python Java Node.js cURL 
# 3.3. Prepare index parameters
index_params = client.prepare_index_params()

# 3.4. Add indexes
index_params.add_index(
    field_name="my_id",
    index_type="STL_SORT"
)

index_params.add_index(
    field_name="my_vector", 
    index_type="IVF_FLAT",
    metric_type="IP",
    params={ "nlist": 128 }
)

import io.milvus.v2.common.IndexParam;

// 3.3 Prepare index parameters
IndexParam indexParamForIdField = IndexParam.builder()
    .fieldName("my_id")
    .indexType(IndexParam.IndexType.STL_SORT)
    .build();

IndexParam indexParamForVectorField = IndexParam.builder()
    .fieldName("my_vector")
    .indexType(IndexParam.IndexType.IVF_FLAT)
    .metricType(IndexParam.MetricType.L2)
    .extraParams(Map.of("nlist", 1024))
    .build();

List<IndexParam> indexParams = new ArrayList<>();
indexParams.add(indexParamForIdField);
indexParams.add(indexParamForVectorField);

// 3.2 Prepare index parameters
const index_params = [{
    field_name: "my_id",
    index_type: "STL_SORT"
},{
    field_name: "my_vector",
    index_type: "IVF_FLAT",
    metric_type: "IP",
    params: { nlist: 1024}
}]

export indexParams='[{ \
    "fieldName": "my_id", \
    "indexName": "my_id", \
    "params": { \
        "index_type": "SLT_SORT" \
  } \
}, { \
    "fieldName": "my_vector", \
    "metricType": "COSINE", \
    "indexName": "my_vector", \
    "params": { \
        "index_type": "IVF_FLAT", \
        "nlist": 1024 \
  } \
}]'
Parameter Description
field_name The name of the target file to apply this object applies.
index_type The name of the algorithm used to arrange data in the specific field. For applicable algorithms, refer to In-memory Index and On-disk Index.
metric_type The algorithm that is used to measure similarity between vectors. Possible values are IP, L2, COSINE, JACCARD, HAMMING. This is available only when the specified field is a vector field. For more information, refer to Indexes supported in Milvus.
params The fine-tuning parameters for the specified index type. For details on possible keys and value ranges, refer to In-memory Index.
Parameter Description
fieldName The name of the target field to apply this IndexParam object applies.
indexType The name of the algorithm used to arrange data in the specific field. For applicable algorithms, refer to In-memory Index and On-disk Index.
metricType The distance metric to use for the index. Possible values are IP, L2, COSINE, JACCARD, HAMMING.
extraParams Extra index parameters. For details, refer to In-memory Index and On-disk Index.
Parameter Description
field_name The name of the target field on which an index is to be created.
index_type The name of the algorithm used to arrange data in the specific field. For applicable algorithms, refer to In-memory Index and On-disk Index.
metric_type The algorithm that is used to measure similarity between vectors. Possible values are IP, L2, COSINE, JACCARD, HAMMING. This is available only when the specified field is a vector field. For more information, refer to Indexes supported in Milvus.
params The fine-tuning parameters for the specified index type. For details on possible keys and value ranges, refer to In-memory Index.
Parameter Description
fieldName The name of the target field on which an index is to be created.
indexName The name of the index to create. The value defaults to the target field name.
metricType The algorithm that is used to measure similarity between vectors. Possible values are IP, L2, COSINE, JACCARD, HAMMING. This is available only when the specified field is a vector field. For more information, refer to Indexes supported in Milvus.
params The index type and related settings. For details, refer to In-memory Index.
params.index_type The type of the index to create.
params.nlist The number of cluster units. This applies to IVF-related index types.

The code snippet above demonstrates how to set up index parameters for the vector field and a scalar field, respectively. For the vector field, set both the metric type and the index type. For a scalar field, set only the index type. It is recommended to create an index for the vector field and any scalar fields that are frequently used for filtering.

Step 3: Create the collection

You have the option to create a collection and an index file separately or to create a collection with the index loaded simultaneously upon creation.

Use create_collection() to create a collection with the specified schema and index parameters and get_load_state() to check the load state of the collection.

Use createCollection() to create a collection with the specified schema and index parameters and getLoadState() to check the load state of the collection.

Use createCollection() to create a collection with the specified schema and index parameters and getLoadState() to check the load state of the collection.

  • Create a collection with the index loaded simultaneously upon creation.

    Python Java Node.js cURL 

    # 3.5. Create a collection with the index loaded simultaneously
client.create_collection(
    collection_name="customized_setup_1",
    schema=schema,
    index_params=index_params
)

time.sleep(5)

res = client.get_load_state(
    collection_name="customized_setup_1"
)

print(res)

# Output
#
# {
#     "state": "<LoadState: Loaded>"
# }

    import io.milvus.v2.service.collection.request.CreateCollectionReq;
import io.milvus.v2.service.collection.request.GetLoadStateReq;

// 3.4 Create a collection with schema and index parameters
CreateCollectionReq customizedSetupReq1 = CreateCollectionReq.builder()
    .collectionName("customized_setup_1")
    .collectionSchema(schema)
    .indexParams(indexParams)
    .build();

client.createCollection(customizedSetupReq1);

// Thread.sleep(5000);

// 3.5 Get load state of the collection
GetLoadStateReq customSetupLoadStateReq1 = GetLoadStateReq.builder()
    .collectionName("customized_setup_1")
    .build();

res = client.getLoadState(customSetupLoadStateReq1);

System.out.println(res);

// Output:
// true

    // 3.3 Create a collection with fields and index parameters
res = await client.createCollection({
    collection_name: "customized_setup_1",
    fields: fields,
    index_params: index_params,
})

console.log(res.error_code)  

// Output
// 
// Success
// 

res = await client.getLoadState({
    collection_name: "customized_setup_1"
})

console.log(res.state)

// Output
// 
// LoadStateLoaded
//   

    $ curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/create" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "customized_setup_1",
    "schema": {
        "autoId": false,
        "enabledDynamicField": false,
        "fields": [
            {
                "fieldName": "my_id",
                "dataType": "Int64",
                "isPrimary": true
            },
            {
                "fieldName": "my_vector",
                "dataType": "FloatVector",
                "elementTypeParams": {
                    "dim": "5"
                }
            }
        ]
    },
    "indexParams": [
        {
            "fieldName": "my_vector",
            "metricType": "COSINE",
            "indexName": "my_vector",
            "params": {
                "index_type": "IVF_FLAT",
                "nlist": "1024"
            }
        },
        {
            "fieldName": "my_id",
            "indexName": "my_id",
            "params": {
                "index_type": "STL_SORT"
            }            
        }
    ]
}'

# Output
#
# {
#     "code": 0,
#     "data": {},
# }

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/get_load_state" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "customized_setup_1"
}'

# {
#     "code": 0,
#     "data": {
#         "loadProgress": 100,
#         "loadState": "LoadStateLoaded"
#     }
# }

    The collection created above is loaded automatically. To learn more about loading and releasing a collection, refer to Load & Release Collection.

  • Create a collection and an index file separately.

    Python Java Node.js cURL 

    # 3.6. Create a collection and index it separately
client.create_collection(
    collection_name="customized_setup_2",
    schema=schema,
)

res = client.get_load_state(
    collection_name="customized_setup_2"
)

print(res)

# Output
#
# {
#     "state": "<LoadState: NotLoad>"
# }

    // 3.6 Create a collection and index it separately
CreateCollectionReq customizedSetupReq2 = CreateCollectionReq.builder()
    .collectionName("customized_setup_2")
    .collectionSchema(schema)
    .build();

client.createCollection(customizedSetupReq2);

    // 3.4 Create a collection and index it seperately
res = await client.createCollection({
    collection_name: "customized_setup_2",
    fields: fields,
})

console.log(res.error_code)

// Output
// 
// Success
// 

res = await client.getLoadState({
    collection_name: "customized_setup_2"
})

console.log(res.state)

// Output
// 
// LoadStateNotLoad
// 

    $ curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/create" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "customized_setup_2",
    "schema": {
        "autoId": false,
        "enabledDynamicField": false,
        "fields": [
            {
                "fieldName": "my_id",
                "dataType": "Int64",
                "isPrimary": true
            },
            {
                "fieldName": "my_vector",
                "dataType": "FloatVector",
                "elementTypeParams": {
                    "dim": "5"
                }
            }
        ]
        
    }
}'

# Output
#
# {
#     "code": 0,
#     "data": {},
# }

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/get_load_state" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "customized_setup_2"
}'

# {
#     "code": 0,
#     "data": {
#         "loadState": "LoadStateNotLoaded"
#     }
# }

    The collection created above is not loaded automatically. You can create an index for the collection as follows. Creating an index for the collection in a separate manner does not automatically load the collection. For details, refer to Load & Release Collection.

    Parameter Description
    collection_name The name of the collection.
    schema The schema of this collection.
    Setting this to None indicates this collection will be created with default settings.
    To set up a collection with a customized schema, you need to create a CollectionSchema object and reference it here. In this case, Milvus ignores all other schema-related settings carried in the request.
    index_params The parameters for building the index on the vector field in this collection. To set up a collection with a customized schema and automatically load the collection to memory, you need to create an IndexParams object and reference it here.
    You should at least add an index for the vector field in this collection. You can also skip this parameter if you prefer to set up the index parameters later on.

    Parameter Description
    collectionName The name of the collection.
    collectionSchema The schema of this collection.
    Leaving it empty indicates this collection will be created with default settings. To set up a collection with a customized schema, you need to create a CollectionSchema object and reference it here.
    indexParams The parameters for building the index on the vector field in this collection. To set up a collection with a customized schema and automatically load the collection to memory, create an IndexParams object with a list of IndexParam objects and reference it here.

    Parameter Description
    collection_name The name of the collection.
    fields The fields in the collection.
    index_params The index parameters for the collection to create.

    Parameter Description
    collectionName The name of the collection.
    schema The schema is responsible for organizing data in the target collection. A valid schema should have multiple fields, which must include a primary key, a vector field, and several scalar fields.
    schema.autoID Whether allows the primary field to automatically increment. Setting this to True makes the primary field automatically increment. In this case, the primary field should not be included in the data to insert to avoid errors. Set this parameter in the field with is_primary set to True.
    schema.enableDynamicField Whether allows to use the reserved $meta field to hold non-schema-defined fields in key-value pairs.
    fields A list of field objects.
    fields.fieldName The name of the field to create in the target collection.
    fields.dataType The data type of the field values.
    fields.isPrimary Whether the current field is the primary field. Setting this to True makes the current field the primary field.
    fields.elementTypeParams Extra field parameters.
    fields.elementTypeParams.dim An optional parameter for FloatVector or BinaryVector fields that determines the vector dimension.

    The collection created above is not loaded automatically. You can create an index for the collection as follows. Creating an index for the collection in a separate manner does not automatically load the collection. For details, refer to Load & Release Collection.

    Python Java Node.js cURL 

    # 3.6 Create index
client.create_index(
    collection_name="customized_setup_2",
    index_params=index_params
)

res = client.get_load_state(
    collection_name="customized_setup_2"
)

print(res)

# Output
#
# {
#     "state": "<LoadState: NotLoad>"
# }

    CreateIndexReq  createIndexReq = CreateIndexReq.builder()
    .collectionName("customized_setup_2")
    .indexParams(indexParams)
    .build();

client.createIndex(createIndexReq);

// Thread.sleep(1000);

// 3.7 Get load state of the collection
GetLoadStateReq customSetupLoadStateReq2 = GetLoadStateReq.builder()
    .collectionName("customized_setup_2")
    .build();

res = client.getLoadState(customSetupLoadStateReq2);

System.out.println(res);

// Output:
// false

    // 3.5 Create index
res = await client.createIndex({
    collection_name: "customized_setup_2",
    field_name: "my_vector",
    index_type: "IVF_FLAT",
    metric_type: "IP",
    params: { nlist: 1024}
})

res = await client.getLoadState({
    collection_name: "customized_setup_2"
})

console.log(res.state)

// Output
// 
// LoadStateNotLoad
//

    $ curl -X POST "http://${MILVUS_URI}/v2/vectordb/indexes/create" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "customized_setup_2",
    "indexParams": [
        {
            "metricType": "L2",
            "fieldName": "my_vector",
            "indexName": "my_vector",
            "indexConfig": {
                "index_type": "IVF_FLAT",
                "nlist": "1024"
            }
        }
    ]
}'

# Output
#
# {
#     "code": 0,
#     "data": {},
# }

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/get_load_state" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "customized_setup_2"
}'

# {
#     "code": 0,
#     "data": {
#         "loadState": "LoadStateNotLoaded"
#     }
# }
    Parameter Description
    collection_name The name of the collection.
    index_params An IndexParams object containing a list of IndexParam objects.
Parameter Description
collectionName The name of the collection.
indexParams A list of IndexParam objects.
Parameter Description
collection_name The name of the collection.
field_name The name of the field in which to create an index.
index_type The name of the algorithm used to arrange data in the specific field. For applicable algorithms, refer to In-memory Index and On-disk Index.
metric_type The algorithm that is used to measure similarity between vectors. Possible values are IP, L2, COSINE, JACCARD, HAMMING. This is available only when the specified field is a vector field. For more information, refer to Indexes supported in Milvus.
params The fine-tuning parameters for the specified index type. For details on possible keys and value ranges, refer to In-memory Index.
Parameter Description
collectionName The name of the collection.
indexParams The index parameters for the collection to create.
indexParams.metricType The similarity metric type used to build the index. The value defaults to COSINE.
indexParams.fieldName The name of the target field on which an index is to be created.
indexParams.indexName The name of the index to create, the value defaults to the target field name.
indexParams.indexConfig.index_type The type of the index to create.
indexParams.indexConfig.nlist The number of cluster units. This applies to IVF-related index types.

View Collections

To check the details of an existing collection, use describe_collection().

To check the details of an existing collection, use describeCollection().

To check the details of an existing collection, use describeCollection().

To view the definition of a collection, you can use the POST /v2/vectordb/collections/describe and the POST /v2/vectordb/collections/list API endpoints.

Python Java Node.js cURL 
# 5. View Collections
res = client.describe_collection(
    collection_name="customized_setup_2"
)

print(res)

# Output
#
# {
#     "collection_name": "customized_setup_2",
#     "auto_id": false,
#     "num_shards": 1,
#     "description": "",
#     "fields": [
#         {
#             "field_id": 100,
#             "name": "my_id",
#             "description": "",
#             "type": 5,
#             "params": {},
#             "element_type": 0,
#             "is_primary": true
#         },
#         {
#             "field_id": 101,
#             "name": "my_vector",
#             "description": "",
#             "type": 101,
#             "params": {
#                 "dim": 5
#             },
#             "element_type": 0
#         }
#     ],
#     "aliases": [],
#     "collection_id": 448143479230158446,
#     "consistency_level": 2,
#     "properties": {},
#     "num_partitions": 1,
#     "enable_dynamic_field": true
# }


import io.milvus.v2.service.collection.request.DescribeCollectionReq;
import io.milvus.v2.service.collection.response.DescribeCollectionResp;

// 4. View collections
DescribeCollectionReq describeCollectionReq = DescribeCollectionReq.builder()
    .collectionName("customized_setup_2")
    .build();

DescribeCollectionResp describeCollectionRes = client.describeCollection(describeCollectionReq);

System.out.println(JSONObject.toJSON(describeCollectionRes));

// Output:
// {
//     "createTime": 449005822816026627,
//     "collectionSchema": {"fieldSchemaList": [
//         {
//             "autoID": false,
//             "dataType": "Int64",
//             "name": "my_id",
//             "description": "",
//             "isPrimaryKey": true,
//             "maxLength": 65535,
//             "isPartitionKey": false
//         },
//         {
//             "autoID": false,
//             "dataType": "FloatVector",
//             "name": "my_vector",
//             "description": "",
//             "isPrimaryKey": false,
//             "dimension": 5,
//             "maxLength": 65535,
//             "isPartitionKey": false
//         }
//     ]},
//     "vectorFieldName": ["my_vector"],
//     "autoID": false,
//     "fieldNames": [
//         "my_id",
//         "my_vector"
//     ],
//     "description": "",
//     "numOfPartitions": 1,
//     "primaryFieldName": "my_id",
//     "enableDynamicField": true,
//     "collectionName": "customized_setup_2"
// }

// 5. View Collections
res = await client.describeCollection({
    collection_name: "customized_setup_2"
})

console.log(res)

// Output
// 
// {
//   virtual_channel_names: [ 'by-dev-rootcoord-dml_13_449007919953017716v0' ],
//   physical_channel_names: [ 'by-dev-rootcoord-dml_13' ],
//   aliases: [],
//   start_positions: [],
//   properties: [],
//   status: {
//     extra_info: {},
//     error_code: 'Success',
//     reason: '',
//     code: 0,
//     retriable: false,
//     detail: ''
//   },
//   schema: {
//     fields: [ [Object], [Object] ],
//     properties: [],
//     name: 'customized_setup_2',
//     description: '',
//     autoID: false,
//     enable_dynamic_field: false
//   },
//   collectionID: '449007919953017716',
//   created_timestamp: '449024569603784707',
//   created_utc_timestamp: '1712892797866',
//   shards_num: 1,
//   consistency_level: 'Bounded',
//   collection_name: 'customized_setup_2',
//   db_name: 'default',
//   num_partitions: '1'
// }
// 

curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/describe" \
-H "Content-Type: application/json" \
-d '{
    "dbName": "default",
    "collectionName": "test_collection"
}'

# {
#     "code": 0,
#     "data": {
#         "aliases": [],
#         "autoId": false,
#         "collectionID": 448707763883002014,
#         "collectionName": "test_collection",
#         "consistencyLevel": "Bounded",
#         "description": "",
#         "enableDynamicField": true,
#         "fields": [
#             {
#                 "autoId": false,
#                 "description": "",
#                 "id": 100,
#                 "name": "id",
#                 "partitionKey": false,
#                 "primaryKey": true,
#                 "type": "Int64"
#             },
#             {
#                 "autoId": false,
#                 "description": "",
#                 "id": 101,
#                 "name": "vector",
#                 "params": [
#                     {
#                         "key": "dim",
#                         "value": "5"
#                     }
#                 ],
#                 "partitionKey": false,
#                 "primaryKey": false,
#                 "type": "FloatVector"
#             }
#         ],
#         "indexes": [
#             {
#                 "fieldName": "vector",
#                 "indexName": "vector",
#                 "metricType": "COSINE"
#             }
#         ],
#         "load": "LoadStateLoaded",
#         "partitionsNum": 1,
#         "properties": [],
#         "shardsNum": 1
#     }
# }

To list all existing collections, you can do as follows:

Python Java Node.js cURL 
# 6. List all collection names
res = client.list_collections()

print(res)

# Output
#
# [
#     "customized_setup_2",
#     "quick_setup",
#     "customized_setup_1"
# ]

import io.milvus.v2.service.collection.response.ListCollectionsResp;

// 5. List all collection names
ListCollectionsResp listCollectionsRes = client.listCollections();

System.out.println(listCollectionsRes.getCollectionNames());

// Output:
// [
//     "customized_setup_2",
//     "quick_setup",
//     "customized_setup_1"
// ]

// 5. List all collection names
ListCollectionsResp listCollectionsRes = client.listCollections();

System.out.println(listCollectionsRes.getCollectionNames());

// Output:
// [
//     "customized_setup_1",
//     "quick_setup",
//     "customized_setup_2"
// ]

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/list" \
-H "Content-Type: application/json" \
-d '{
    "dbName": "default"
}'

# {
#   "code": 0,
#   "data": [
#     "quick_setup",
#     "customized_setup_1",
#     "customized_setup_2"
#   ]
# }

Load & Release Collection

During the loading process of a collection, Milvus loads the collection's index file into memory. Conversely, when releasing a collection, Milvus unloads the index file from memory. Before conducting searches in a collection, ensure that the collection is loaded.

Load a collection

To load a collection, use the load_collection() method, specifying the collection name. You can also set replica_number to determine how many in-memory replicas of data segments to create on query nodes when the collection is loaded.

  • Milvus Standalone: The maximum allowed value for replica_number is 1.
  • Milvus Cluster: The maximum value should not exceed the queryNode.replicas set in your Milvus configurations. For additional details, refer to Query Node-related Configurations.

To load a collection, use the loadCollection() method, specifying the collection name.

To load a collection, use the loadCollection() method, specifying the collection name.

To load a collection, you can use the POST /v2/vectordb/collections/load and the POST /v2/vectordb/collections/get_load_state API endpoints.

Python Java Node.js cURL 
# 7. Load the collection
client.load_collection(
    collection_name="customized_setup_2",
    replica_number=1 # Number of replicas to create on query nodes. Max value is 1 for Milvus Standalone, and no greater than `queryNode.replicas` for Milvus Cluster.
)

res = client.get_load_state(
    collection_name="customized_setup_2"
)

print(res)

# Output
#
# {
#     "state": "<LoadState: Loaded>"
# }

import io.milvus.v2.service.collection.request.LoadCollectionReq;

// 6. Load the collection
LoadCollectionReq loadCollectionReq = LoadCollectionReq.builder()
    .collectionName("customized_setup_2")
    .build();

client.loadCollection(loadCollectionReq);

// Thread.sleep(5000);

// 7. Get load state of the collection
GetLoadStateReq loadStateReq = GetLoadStateReq.builder()
    .collectionName("customized_setup_2")
    .build();

res = client.getLoadState(loadStateReq);

System.out.println(res);

// Output:
// true

// 7. Load the collection
res = await client.loadCollection({
    collection_name: "customized_setup_2"
})

console.log(res.error_code)

// Output
// 
// Success
// 

await sleep(3000)

res = await client.getLoadState({
    collection_name: "customized_setup_2"
})

console.log(res.state)

// Output
// 
// LoadStateLoaded
// 

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/load" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "customized_setup_2"
}'

# Output
#
# {
#     "code": 0,
#     "data": {},
# }

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/get_load_state" \
-H "Content-Type: application/json" \
-d '{
  "collectionName": "customized_setup_2"
}'

# {
#     "code": 0,
#     "data": {
#         "loadProgress": 100,
#         "loadState": "LoadStateLoaded"
#     }
# }

Release a collection

To release a collection, use the release_collection() method, specifying the collection name.

To release a collection, use the releaseCollection() method, specifying the collection name.

To release a collection, use the releaseCollection() method, specifying the collection name.

To release a collection, you can use the POST /v2/vectordb/collections/release and the POST /v2/vectordb/collections/get_load_state API endpoints.

Python Java Node.js cURL 
# 8. Release the collection
client.release_collection(
    collection_name="customized_setup_2"
)

res = client.get_load_state(
    collection_name="customized_setup_2"
)

print(res)

# Output
#
# {
#     "state": "<LoadState: NotLoad>"
# }

import io.milvus.v2.service.collection.request.ReleaseCollectionReq;

// 8. Release the collection
ReleaseCollectionReq releaseCollectionReq = ReleaseCollectionReq.builder()
    .collectionName("customized_setup_2")
    .build();

client.releaseCollection(releaseCollectionReq);

// Thread.sleep(1000);

res = client.getLoadState(loadStateReq);

System.out.println(res);

// Output:
// false

// 8. Release the collection
res = await client.releaseCollection({
    collection_name: "customized_setup_2"
})

console.log(res.error_code)

// Output
// 
// Success
// 

res = await client.getLoadState({
    collection_name: "customized_setup_2"
})

console.log(res.state)

// Output
// 
// LoadStateNotLoad
// 

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/release" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "customized_setup_2"
}'

# Output
#
# {
#     "code": 0,
#     "data": {},
# }


$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/get_load_state" \
-H "Content-Type: application/json" \
-d '{
  "collectionName": "customized_setup_2"
}'


# {
#     "code": 0,
#     "data": {
#         "loadState": "LoadStateNotLoad"
#     }
# }

Set up aliases

You can assign aliases for collections to make them more meaningful in a specific context. You can assign multiple aliases for a collection, but multiple collections cannot share an alias.

Create aliases

To create aliases, use the create_alias() method, specifying the collection name and the alias.

To create aliases, use the createAlias() method, specifying the collection name and the alias.

To create aliases, use the createAlias() method, specifying the collection name and the alias.

To create aliases for a collection, you can use the POST /v2/vectordb/aliases/create API endpoint.

Python Java Node.js cURL 
# 9.1. Create aliases
client.create_alias(
    collection_name="customized_setup_2",
    alias="bob"
)

client.create_alias(
    collection_name="customized_setup_2",
    alias="alice"
)

import io.milvus.v2.service.utility.request.CreateAliasReq;

// 9. Manage aliases

// 9.1 Create alias
CreateAliasReq createAliasReq = CreateAliasReq.builder()
    .collectionName("customized_setup_2")
    .alias("bob")
    .build();

client.createAlias(createAliasReq);

createAliasReq = CreateAliasReq.builder()
    .collectionName("customized_setup_2")
    .alias("alice")
    .build();

client.createAlias(createAliasReq);

// 9. Manage aliases
// 9.1 Create aliases
res = await client.createAlias({
    collection_name: "customized_setup_2",
    alias: "bob"
})

console.log(res.error_code)

// Output
// 
// Success
// 

res = await client.createAlias({
    collection_name: "customized_setup_2",
    alias: "alice"
})

console.log(res.error_code)

// Output
// 
// Success
// 

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/aliases/create" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "customized_setup_2",
    "aliasName": "bob"
}'

# Output
#
# {
#     "code": 0,
#     "data": {}
# }

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/aliases/create" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "customized_setup_2",
    "aliasName": "alice"
}'

# Output
#
# {
#     "code": 0,
#     "data": {}
# }
Parameter Description
collection_name The name of the collection to create an alias for.
alias The alias of the collection. Before this operation, ensure that the alias does not already exist. If it does, exceptions will occur.
Parameter Description
collectionName The name of the collection to create an alias for.
alias The alias of the collection. Before this operation, ensure that the alias does not already exist. If it does, exceptions will occur.
Parameter Description
collection_name The name of the collection to create an alias for.
alias The alias of the collection. Before this operation, ensure that the alias does not already exist. If it does, exceptions will occur.
Parameter Description
collectionName The name of the collection to create an alias for.
aliasName The alias of the collection. Before this operation, ensure that the alias does not already exist. If it does, exceptions will occur.

List aliases

To list aliases, use the list_aliases() method, specifying the collection name.

To list aliases, use the listAliases() method, specifying the collection name.

To list aliases, use the listAliases() method, specifying the collection name.

To list aliases for a collection, you can use the POST /v2/vectordb/aliases/list API endpoint.

Python Java Node.js cURL 
# 9.2. List aliases
res = client.list_aliases(
    collection_name="customized_setup_2"
)

print(res)

# Output
#
# {
#     "aliases": [
#         "bob",
#         "alice"
#     ],
#     "collection_name": "customized_setup_2",
#     "db_name": "default"
# }

import io.milvus.v2.service.utility.request.ListAliasesReq;
import io.milvus.v2.service.utility.response.ListAliasResp;

// 9.2 List alises
ListAliasesReq listAliasesReq = ListAliasesReq.builder()
    .collectionName("customized_setup_2")
    .build();

ListAliasResp listAliasRes = client.listAliases(listAliasesReq);

System.out.println(listAliasRes.getAlias());

// Output:
// [
//     "bob",
//     "alice"
// ]

// 9.2 List aliases
res = await client.listAliases({
    collection_name: "customized_setup_2"
})

console.log(res.aliases)

// Output
// 
// [ 'bob', 'alice' ]
// 

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/aliases/list" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "customized_setup_2"
}'

# {
#     "code": 0,
#     "data": [
#         "bob",
#         "alice"
#     ]
# }

Describe aliases

To describe aliases, use the describe_alias() method, specifying the alias.

To describe aliases, use the describeAlias() method, specifying the alias.

To describe aliases, use the describeAlias() method, specifying the alias.

To describe aliases for a collection, you can use the POST /v2/vectordb/aliases/describe API endpoint.

Python Java Node.js cURL 
# 9.3. Describe aliases
res = client.describe_alias(
    alias="bob"
)

print(res)

# Output
#
# {
#     "alias": "bob",
#     "collection_name": "customized_setup_2",
#     "db_name": "default"
# }

import io.milvus.v2.service.utility.request.DescribeAliasReq;
import io.milvus.v2.service.utility.response.DescribeAliasResp;

// 9.3 Describe alias
DescribeAliasReq describeAliasReq = DescribeAliasReq.builder()
    .alias("bob")
    .build();

DescribeAliasResp describeAliasRes = client.describeAlias(describeAliasReq);

System.out.println(JSONObject.toJSON(describeAliasRes));

// Output:
// {
//     "alias": "bob",
//     "collectionName": "customized_setup_2"
// }

// 9.3 Describe aliases
res = await client.describeAlias({
    collection_name: "customized_setup_2",
    alias: "bob"
})

console.log(res)

// Output
// 
// {
//   status: {
//     extra_info: {},
//     error_code: 'Success',
//     reason: '',
//     code: 0,
//     retriable: false,
//     detail: ''
//   },
//   db_name: 'default',
//   alias: 'bob',
//   collection: 'customized_setup_2'
// }
// 

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/aliases/describe" \
-H "Content-Type: application/json" \
-d '{
    "aliasName": "bob"
}'

# {
#     "code": 0,
#     "data": {
#         "aliasName": "bob",
#         "collectionName": "quick_setup",
#         "dbName": "default"
#     }
# }

Reassign aliases

To reassign aliases to other collections, use the alter_alias() method, specifying the collection name and the alias.

To reassign aliases to other collections, use the alterAlias() method, specifying the collection name and the alias.

To reassign aliases to other collections, use the alterAlias() method, specifying the collection name and the alias.

To reassign aliases to other collections, you can use the POST /v2/vectordb/aliases/alter API endpoint.

Python Java Node.js cURL 
# 9.4 Reassign aliases to other collections
client.alter_alias(
    collection_name="customized_setup_1",
    alias="alice"
)

res = client.list_aliases(
    collection_name="customized_setup_1"
)

print(res)

# Output
#
# {
#     "aliases": [
#         "alice"
#     ],
#     "collection_name": "customized_setup_1",
#     "db_name": "default"
# }

res = client.list_aliases(
    collection_name="customized_setup_2"
)

print(res)

# Output
#
# {
#     "aliases": [
#         "bob"
#     ],
#     "collection_name": "customized_setup_2",
#     "db_name": "default"
# }

import io.milvus.v2.service.utility.request.AlterAliasReq;

// 9.4 Reassign alias to other collections
AlterAliasReq alterAliasReq = AlterAliasReq.builder()
    .collectionName("customized_setup_1")
    .alias("alice")
    .build();

client.alterAlias(alterAliasReq);

listAliasesReq = ListAliasesReq.builder()
    .collectionName("customized_setup_1")
    .build();

listAliasRes = client.listAliases(listAliasesReq);

System.out.println(listAliasRes.getAlias());

// Output:
// ["alice"]

listAliasesReq = ListAliasesReq.builder()
    .collectionName("customized_setup_2")
    .build();

listAliasRes = client.listAliases(listAliasesReq);

System.out.println(listAliasRes.getAlias());

// Output:
// ["bob"]

// 9.4 Reassign aliases to other collections
res = await client.alterAlias({
    collection_name: "customized_setup_1",
    alias: "alice"
})

console.log(res.error_code)

// Output
// 
// Success
// 

res = await client.listAliases({
    collection_name: "customized_setup_1"
})

console.log(res.aliases)

// Output
// 
// [ 'alice' ]
// 

res = await client.listAliases({
    collection_name: "customized_setup_2"
})

console.log(res.aliases)

// Output
// 
// [ 'bob' ]
// 

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/aliases/alter" \
-H "Content-Type: application/json" \
-d '{
     "collectionName": "customized_setup_1",
     "aliasName": "alice"
}'

# {
#     "code": 0,
#     "data": {}
# }


$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/aliases/list" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "customized_setup_1"
}'


# {
#     "code": 0,
#     "data": [
#         "alice"
#     ]
# }


$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/aliases/list" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "customized_setup_2"
}'


# {
#     "code": 0,
#     "data": [
#         "bob"
#     ]
# }

Drop aliases

To drop aliases, use the drop_alias() method, specifying the alias.

To drop aliases, use the dropAlias() method, specifying the alias.

To drop aliases, use the dropAlias() method, specifying the alias.

To drop aliases for a collection, you can use the POST /v2/vectordb/aliases/drop API endpoint.

Python Java Node.js 
# 9.5 Drop aliases
client.drop_alias(
    alias="bob"
)

client.drop_alias(
    alias="alice"
)

import io.milvus.v2.service.utility.request.DropAliasReq;

// 9.5 Drop alias
DropAliasReq dropAliasReq = DropAliasReq.builder()
    .alias("bob")
    .build();

client.dropAlias(dropAliasReq);

dropAliasReq = DropAliasReq.builder()
    .alias("alice")
    .build();

client.dropAlias(dropAliasReq);

// 9.5 Drop aliases
res = await client.dropAlias({
    alias: "bob"
})

console.log(res.error_code)

// Output
// 
// Success
// 

res = await client.dropAlias({
    alias: "alice"
})

console.log(res.error_code)

// Output
// 
// Success
// 

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/aliases/drop" \
-H "Content-Type: application/json" \
-d '{
    "aliasName": "bob"
}'

# {
#     "code": 0,
#     "data": {}
# }


$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/aliases/drop" \
-H "Content-Type: application/json" \
-d '{
    "aliasName": "alice"
}'


# {
#     "code": 0,
#     "data": {}
# }

Set Properties

You can set properties for a collection, such as ttl.seconds and mmap.enabled. For more information, refer to set_properties().

The code snippets in this section use the PyMilvus ORM module to interact with Milvus. Code snippets with the new MilvusClient SDK will be available soon.

Set TTL

Set the Time-To-Live (TTL) for the data in the collection, which specifies how long the data should be retained before it is automatically deleted.

from pymilvus import Collection, connections

# Connect to Milvus server
connections.connect(host="localhost", port="19530") # Change to your Milvus server IP and port

# Get existing collection
collection = Collection("quick_setup")

# Set the TTL for the data in the collection
collection.set_properties(
    properties={
        "collection.ttl.seconds": 60
    }
)

Set MMAP

Configure the memory mapping (MMAP) property for the collection, which determines whether data is mapped into memory to improve query performance. For more information, refer to Configure memory mapping .

Before setting the MMAP property, release the collection first. Otherwise, an error will occur.

from pymilvus import Collection, connections

# Connect to Milvus server
connections.connect(host="localhost", port="19530") # Change to your Milvus server IP and port

# Get existing collection
collection = Collection("quick_setup")

# Before setting memory mapping property, we need to release the collection first.
collection.release()

# Set memory mapping property to True or Flase
collection.set_properties(
    properties={
        "mmap.enabled": True
    }
)

Drop a Collection

If a collection is no longer needed, you can drop the collection.

To drop a collection, use the drop_collection() method, specifying the collection name.

To drop a collection, use the dropCollection() method, specifying the collection name.

To drop a collection, use the dropCollection() method, specifying the collection name.

To drop a collection, you can use the POST /v2/vectordb/collections/drop API endpoint.

Python Java Node.js cURL 
# 10. Drop the collections
client.drop_collection(
    collection_name="quick_setup"
)

client.drop_collection(
    collection_name="customized_setup_1"
)

client.drop_collection(
    collection_name="customized_setup_2"
)

import io.milvus.v2.service.collection.request.DropCollectionReq;

// 10. Drop collections

DropCollectionReq dropQuickSetupParam = DropCollectionReq.builder()
    .collectionName("quick_setup")
    .build();

client.dropCollection(dropQuickSetupParam);

DropCollectionReq dropCustomizedSetupParam = DropCollectionReq.builder()
    .collectionName("customized_setup_1")
    .build();

client.dropCollection(dropCustomizedSetupParam);

dropCustomizedSetupParam = DropCollectionReq.builder()
    .collectionName("customized_setup_2")
    .build();

client.dropCollection(dropCustomizedSetupParam);

// 10. Drop the collection
res = await client.dropCollection({
    collection_name: "customized_setup_2"
})

console.log(res.error_code)

// Output
// 
// Success
// 

res = await client.dropCollection({
    collection_name: "customized_setup_1"
})

console.log(res.error_code)

// Output
// 
// Success
// 

res = await client.dropCollection({
    collection_name: "quick_setup"
})

console.log(res.error_code)

// Output
// 
// Success
// 

$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/drop" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "quick_setup"
}'

# {
#     "code": 0,
#     "data": {}
# }


$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/drop" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "customized_setup_1"
}'


# {
#     "code": 0,
#     "data": {}
# }


$ curl -X POST "http://${MILVUS_URI}/v2/vectordb/collections/drop" \
-H "Content-Type: application/json" \
-d '{
    "collectionName": "customized_setup_2"
}'


# {
#     "code": 0,
#     "data": {}
# }
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