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  • Python
    • MilvusClient

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,
    **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.

  • 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 limit to enable pagination.

      The sum of this value and limit should 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 offset to enable pagination.

      The sum of this value and offset should 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}]