insert()

This operation inserts data into the current collection.

Request Syntax

insert(
    data: List | pandas.DataFrame | Dict, 
    partition_name: str | None, 
    timeout: float | None, 
)

PARAMETERS:

data (list | dict | pandas.DataFrame) -

[REQUIRED]

The data to insert into the current collection.

The data to insert should match the schema of the current collection. You can organize your data into:

A list of columns

Each column is a list of values of all entities in that column.

data = [
    [0,1,2,3,4],                         # id
    [                                    # vector
        [0.1,0.2,-0.3,-0.4,0.5],
        [0.3,-0.1,-0.2,-0.6,0.7],
        [-0.6,-0.3,0.2,0.8,0.7],
        [0.6,0.2,-0.3,-0.8,0.5],
        [0.3,0.1,-0.2,-0.6,-0.7],
    ],
]

A pandas.DataFrame

You can form a data frame in any way, as demonstrated in the Example section on this page.

data = pd.DataFrame({
    "id": [5,6,7,8,9],
    "vector": [
        [0.1,0.2,-0.3,-0.4,0.5],
        [0.3,-0.1,-0.2,-0.6,0.7],
        [-0.6,-0.3,0.2,0.8,0.7],
        [0.6,0.2,-0.3,-0.8,0.5],
        [0.3,0.1,-0.2,-0.6,-0.7],
    ]
})

A list of rows or just a row

Each row is a dictionary that represents an entity.

data = [
    {"id": 10, "vector": [0.1,0.2,-0.3,-0.4,0.5]},
    {"id": 11, "vector": [0.3,-0.1,-0.2,-0.6,0.7]},
    {"id": 12, "vector": [-0.6,-0.3,0.2,0.8,0.7]},
    {"id": 13, "vector": [0.6,0.2,-0.3,-0.8,0.5]},
    {"id": 14, "vector": [0.3,0.1,-0.2,-0.6,-0.7]},
]

# or 

data = {"id": 15, "vector": [0.3,0.1,-0.2,-0.6,-0.7]},

partition_name (string | None) -

The name of a partition in the current collection.

If specified, the data is to be inserted into the specified partition.
timeout (float | None)

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

RETURN TYPE:

MutationResult

RETURNS:

A MutationResult object that contains the following fields:

insert_count (int)

The count of inserted entities.
delete_count (int)

The count of deleted entities.
upsert_count (int)

The count of upserted entities.
succ_count (int)

The count of successful executions during this operation.
succ_index (list)

A list of index numbers starting from 0, each indicating a successful operation.
err_count (int)

The count of failed executions during this operation.
err_index (list)

A list of index numbers starting from 0, each indicating a failed operation.
primary_keys (list)

A list of primary keys for the inserted entities.
timestamp (int)

The timestamp at which this operation is completed.

EXCEPTIONS:

MilvusException

This exception will be raised when any error occurs during this operation.

Examples

from pymilvus import Collection, CollectionSchema, FieldSchema, DataType

schema = CollectionSchema([
    FieldSchema("id", DataType.INT64, is_primary=True),
    FieldSchema("vector", DataType.FLOAT_VECTOR, dim=5)
])

# Create a collection
collection = Collection(
    name="test_collection",
    schema=schema
)

# Insert a list of columns
res = collection.insert(
    data=[
        [0,1,2,3,4],                         # id
        [                                    # vector
            [0.1,0.2,-0.3,-0.4,0.5],
            [0.3,-0.1,-0.2,-0.6,0.7],
            [-0.6,-0.3,0.2,0.8,0.7],
            [0.6,0.2,-0.3,-0.8,0.5],
            [0.3,0.1,-0.2,-0.6,-0.7],
        ],
    ]
)

# Insert a data frame
import pandas as pd

res = collection.insert(
    data=pd.DataFrame({
        "id": [5,6,7,8,9],
        "vector": [
            [0.1,0.2,-0.3,-0.4,0.5],
            [0.3,-0.1,-0.2,-0.6,0.7],
            [-0.6,-0.3,0.2,0.8,0.7],
            [0.6,0.2,-0.3,-0.8,0.5],
            [0.3,0.1,-0.2,-0.6,-0.7],
        ]
    })
)

# Insert a list of dictionaries
res = collection.insert(
    data=[
        {"id": 10, "vector": [0.1,0.2,-0.3,-0.4,0.5]},
        {"id": 11, "vector": [0.3,-0.1,-0.2,-0.6,0.7]},
        {"id": 12, "vector": [-0.6,-0.3,0.2,0.8,0.7]},
        {"id": 13, "vector": [0.6,0.2,-0.3,-0.8,0.5]},
        {"id": 14, "vector": [0.3,0.1,-0.2,-0.6,-0.7]},
    ]
)

# Insert a dictionary
res = collection.insert(
    data={"id": 16, "vector": [0.3,0.1,-0.2,-0.6,-0.7]},
)

Related operations

The following operations are related to insert():