upsert()
This operation inserts or updates data in a specific collection.
Request syntax
upsert(
collection_name: str,
data: Union[Dict, List[Dict]],
timeout: Optional[float] = None,
partition_name: Optional[str] = "",
) -> List[Union[str, int]]
PARAMETERS:
collection_name (str) -
[REQUIRED]
The name of an existing collection.
data (dict | list[dict]) -
[REQUIRED]
The data to insert or update into the current collection.
The data to insert or update should be a dictionary that matches the schema of the current collection or a list of such dictionaries.
The following code assumes that the schema of the current collection has two fields named id and vector. The former is the primary field and the latter is a field to hold 5-dimensional vector embeddings.
# A dictionary, or data={ 'id': 0, 'vector': [ 0.6186516144460161, 0.5927442462488592, 0.848608119657156, 0.9287046808231654, -0.42215796530168403 ] } # A list of dictionaries data = [ { 'id': 1, 'vector': [ 0.37417449965222693, -0.9401784221711342, 0.9197526367693833, 0.49519396415367245, -0.558567588166478 ] }, { 'id': 2, 'vector': [ 0.46949086179692356, -0.533609076732849, -0.8344432775467099, 0.9797361846081416, 0.6294256393761057 ] } ]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.
partition_name (string | None) -
The name of a partition in the current collection.
If specified, the data is to be inserted or updated in the specified partition.
This parameter is not applicable to Milvus Lite. For more information on Milvus Lite limits, refer to Run Milvus Lite.
RETURN TYPE:
dict
RETURNS:
A dictionary contains information about the number of inserted or updated entities.
{'upsert_count': 10}
EXCEPTIONS:
MilvusException
This exception will be raised when any error occurs during this operation.
Examples
from pymilvus import MilvusClient
# 1. Set up a milvus client
client = MilvusClient(
uri="http://localhost:19530",
token="root:Milvus"
)
# 2. Create a collection
client.create_collection(collection_name="test_collection", dimension=5)
# 3. Insert records
res = client.insert(
collection_name="test_collection",
data=[
{
'id': 0,
'vector': [
0.37417449965222693,
-0.9401784221711342,
-0.8344432775467099,
0.9797361846081416,
0.6294256393761057
]
},
{
'id': 1,
'vector': [
0.37417449965222693,
-0.9401784221711342,
0.9197526367693833,
0.49519396415367245,
-0.558567588166478
]
},
{
'id': 2,
'vector': [
0.46949086179692356,
-0.533609076732849,
-0.8344432775467099,
0.9797361846081416,
0.6294256393761057
]
}
]
)
# {'insert_count': 3}
# 4. Upsert a record
res = client.insert(
collection_name="test_collection",
data={
'id': 0,
'vector': [
0.6186516144460161,
0.5927442462488592,
0.848608119657156,
0.9287046808231654,
-0.42215796530168403
]
}
)
# {'upsert_count': 1}
# 4. Upsert multiple records
res = client.upsert(
collection_name="test_collection",
data=[
{
'id': 1,
'vector': [
0.3457690490452393,
-0.9401784221711342,
0.9123948134344333,
0.49519396415367245,
-0.558567588166478
]
},
{
'id': 2,
'vector': [
0.42349086179692356,
-0.533609076732849,
-0.8344432775467099,
0.675761846081416,
0.57094256393761057
]
}
]
)
# {'upsert_count': 2}