query()
This operation conducts a scalar filtering with a specified boolean expression.
Request syntax
query(
collection_name: str,
filter: str,
output_fields: Optional[List[str]] = None,
timeout: Optional[float] = None,
partition_names: Optional[List[str]] = None,
**kwargs,
) -> List[dict]
PARAMETERS:
collection_name (str) -
[REQUIRED]
The name of an existing collection.
filter (str) -
[REQUIRED]
A scalar filtering condition to filter matching entities.
You can set this parameter to an empty string to skip scalar filtering. To build a scalar filtering condition, refer to Boolean Expression Rules.
output_fields (list[str] | None) -
A list of field names to include in each entity in return.
The value defaults to None.
notes
Setting this as
output_fields=[“*”]outputs all fields.Setting this as
output_fields=["count(*)"]outputs the loaded entities that match the conditions specified in the filter argument.
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_names (list[str] | None) -
A list of partition names.
The value defaults to None. If specified, only the specified partitions are involved in queries.
This parameter is not applicable to Milvus Lite. For more information on Milvus Lite limits, refer to Run Milvus Lite.
kwargs -
consistency_level (str | int) -
The consistency level of the target collection.
The value defaults to the one specified when you create the current collection, with options of Strong (0), Bounded (1), Session (2), and Eventually (3).
what is the consistency level?
Consistency in a distributed database specifically refers to the property that ensures every node or replica has the same view of data when writing or reading data at a given time.
Milvus supports four consistency levels: Strong, Bounded Staleness, Session, and Eventually. The default consistency level in Milvus is Bounded Staleness.
You can easily tune the consistency level when conducting a vector similarity search or query to make it best suit your application.
guarantee_timestamp (int) -
A valid timestamp.
If this parameter is set, MilvusZilliz Cloud executes the query only if all entities inserted before this timestamp are visible to query nodes.
notes
This parameter is valid when the default consistency level applies.
graceful_time (int) -
A period of time in seconds.
The value defaults to 5. If this parameter is set, MilvusZilliz Cloud calculates the guarantee timestamp by subtracting this from the current timestamp.
notes
This parameter is valid when a consistency level other than the default one applies.
offset (int) -
The number of records to skip in the query result.
You can use this parameter in combination with
limitto enable pagination.The sum of this value and
limitshould be less than 16,384.limit (int) -
The number of records to return in the query result.
You can use this parameter in combination with
offsetto enable pagination.The sum of this value and
offsetshould be less than 16,384.
RETURN TYPE:
list[dict]
RETURNS:
A list of dictionaries with each dictionary representing a queried entity.
notes
If the number of returned entities is less than expected, duplicate entities may exist in your collection.
EXCEPTIONS:
MilvusException
This exception will be raised when any error occurs during this operation.
DataTypeNotMatchException
This exception will be raised when a parameter value doesn’t match the required data type.
Examples
from pymilvus import MilvusClient
# 1. Set up a milvus client
client = MilvusClient(
uri="http://localhost:19530",
token="root:Milvus"
)
# 2. Create a collection and a partition
client.create_collection(
collection_name="test_collection",
dimension=5
)
client.create_partition(
collection_name="test_collection",
partition_name="partitionA"
)
# 3. Insert data
client.insert(
collection_name="test_collection",
data=[
{"id": 0, "vector": [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592], "color": "pink_8682"},
{"id": 1, "vector": [0.19886812562848388, 0.06023560599112088, 0.6976963061752597, 0.2614474506242501, 0.838729485096104], "color": "red_7025"},
{"id": 2, "vector": [0.43742130801983836, -0.5597502546264526, 0.6457887650909682, 0.7894058910881185, 0.20785793220625592], "color": "orange_6781"},
{"id": 3, "vector": [0.3172005263489739, 0.9719044792798428, -0.36981146090600725, -0.4860894583077995, 0.95791889146345], "color": "pink_9298"},
{"id": 4, "vector": [0.4452349528804562, -0.8757026943054742, 0.8220779437047674, 0.46406290649483184, 0.30337481143159106], "color": "red_4794"},
{"id": 5, "vector": [0.985825131989184, -0.8144651566660419, 0.6299267002202009, 0.1206906911183383, -0.1446277761879955], "color": "yellow_4222"},
{"id": 6, "vector": [0.8371977790571115, -0.015764369584852833, -0.31062937026679327, -0.562666951622192, -0.8984947637863987], "color": "red_9392"},
{"id": 7, "vector": [-0.33445148015177995, -0.2567135004164067, 0.8987539745369246, 0.9402995886420709, 0.5378064918413052], "color": "grey_8510"},
{"id": 8, "vector": [0.39524717779832685, 0.4000257286739164, -0.5890507376891594, -0.8650502298996872, -0.6140360785406336], "color": "white_9381"},
{"id": 9, "vector": [0.5718280481994695, 0.24070317428066512, -0.3737913482606834, -0.06726932177492717, -0.6980531615588608], "color": "purple_4976"}
],
)
# {'insert_count': 10}
# 4. Conduct queries
# Query without any scalar filtering condition
# This query returns entities with their ids from 0 to 4.
res = client.query(
collection_name="test_collection",
filter="",
limit=5,
)
print(res)
# [{'id': 0,
# 'vector': [0.35803765, -0.6023496, 0.18414013, -0.26286206, 0.90294385],
# 'color': 'pink_8682'},
# {'id': 1,
# 'vector': [0.19886813, 0.060235605, 0.6976963, 0.26144746, 0.8387295],
# 'color': 'red_7025'},
# {'id': 2,
# 'vector': [0.43742132, -0.55975026, 0.6457888, 0.7894059, 0.20785794],
# 'color': 'orange_6781'},
# {'id': 3,
# 'vector': [0.3172005, 0.97190446, -0.36981148, -0.48608947, 0.9579189],
# 'color': 'pink_9298'},
# {'id': 4,
# 'vector': [0.44523495, -0.8757027, 0.82207793, 0.4640629, 0.3033748],
# 'color': 'red_4794'}]
# Query with pagination
# This query returns entities with their ids from 5 to 9.
res = client.query(
collection_name="test_collection",
filter="",
offset=5,
limit=5
)
print(res)
# [{'vector': [0.9858251, -0.81446517, 0.6299267, 0.12069069, -0.14462778],
# 'color': 'yellow_4222',
# 'id': 5},
# {'vector': [0.8371978, -0.015764369, -0.31062937, -0.56266695, -0.8984948],
# 'color': 'red_9392',
# 'id': 6},
# {'vector': [-0.33445147, -0.2567135, 0.898754, 0.9402996, 0.5378065],
# 'color': 'grey_8510',
# 'id': 7},
# {'vector': [0.3952472, 0.40002573, -0.5890507, -0.86505026, -0.6140361],
# 'color': 'white_9381',
# 'id': 8},
# {'vector': [0.57182807, 0.24070318, -0.37379134, -0.067269325, -0.6980532],
# 'color': 'purple_4976',
# 'id': 9}]
# Query with a scalar filtering condition
res = client.query(
collection_name="test_collection",
filter="id in [6,7,8]",
)
print(res)
# [{'vector': [0.8371978, -0.015764369, -0.31062937, -0.56266695, -0.8984948],
# 'color': 'red_9392',
# 'id': 6},
# {'vector': [-0.33445147, -0.2567135, 0.898754, 0.9402996, 0.5378065],
# 'color': 'grey_8510',
# 'id': 7},
# {'vector': [0.3952472, 0.40002573, -0.5890507, -0.86505026, -0.6140361],
# 'color': 'white_9381',
# 'id': 8}]
# Query within a partition
res = client.query(
collection_name="test_collection",
filter="id in [6,7,8]",
partition_names=["partitionA"],
)
print(res)
# []
# Query with specified output fields
res = client.query(
collection_name="test_collection",
filter="id in [6,7,8]",
output_fields=["id", "vector"],
)
print(res)
# [{'id': 6,
# 'vector': [0.8371978, -0.015764369, -0.31062937, -0.56266695, -0.8984948]},
# {'id': 7,
# 'vector': [-0.33445147, -0.2567135, 0.898754, 0.9402996, 0.5378065]},
# {'id': 8,
# 'vector': [0.3952472, 0.40002573, -0.5890507, -0.86505026, -0.6140361]}]
# Query with a customized consistency level
res = client.query(
collection_name="test_collection",
filter="",
limit=5,
consistency_level=3,
graceful_time=6
)
print(res)
# [{'color': 'pink_8682',
# 'id': 0,
# 'vector': [0.35803765, -0.6023496, 0.18414013, -0.26286206, 0.90294385]},
# {'color': 'red_7025',
# 'id': 1,
# 'vector': [0.19886813, 0.060235605, 0.6976963, 0.26144746, 0.8387295]},
# {'color': 'orange_6781',
# 'id': 2,
# 'vector': [0.43742132, -0.55975026, 0.6457888, 0.7894059, 0.20785794]},
# {'color': 'pink_9298',
# 'id': 3,
# 'vector': [0.3172005, 0.97190446, -0.36981148, -0.48608947, 0.9579189]},
# {'color': 'red_4794',
# 'id': 4,
# 'vector': [0.44523495, -0.8757027, 0.82207793, 0.4640629, 0.3033748]}]
# Query with outputting all fields
res = client.query(
collection_name="test_collection",
filter="id < 5",
output_fields=["*"]
)
# [{'vector': [0.35803765, -0.6023496, 0.18414013, -0.26286206, 0.90294385],
# 'color': 'pink_8682',
# 'id': 0},
# {'vector': [0.19886813, 0.060235605, 0.6976963, 0.26144746, 0.8387295],
# 'color': 'red_7025',
# 'id': 1},
# {'vector': [0.43742132, -0.55975026, 0.6457888, 0.7894059, 0.20785794],
# 'color': 'orange_6781',
# 'id': 2},
# {'vector': [0.3172005, 0.97190446, -0.36981148, -0.48608947, 0.9579189],
# 'color': 'pink_9298',
# 'id': 3},
# {'vector': [0.44523495, -0.8757027, 0.82207793, 0.4640629, 0.3033748],
# 'color': 'red_4794',
# 'id': 4}]
# Count the loaded entities that match specific conditions
res = client.query(
collection_name="test_collection",
filter="color like \"red_%\"",
output_fields=["count(*)"]
)
# [{'count(*)': 3}]