query_iterator()

This operation conducts a scalar filtering with a specified boolean expression.

Request syntax{#request-syntax}

def query_iterator(
    self,
    collection_name: str,
    batch_size: Optional[int] = 1000,
    limit: Optional[int] = UNLIMITED,
    filter: Optional[str] = "",
    output_fields: Optional[List[str]] = None,
    partition_names: Optional[List[str]] = None,
    timeout: Optional[float] = None,
    **kwargs,
) -> List[dict]

PARAMETERS:

• collection_name (str) -

  [REQUIRED]

  The name of an existing collection.

• batch_size (int[]) -

  The number of entities to return per iteration. The value defaults to 1000.

• 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.

  • 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.

• 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 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.

    Zilliz Cloud provides three consistency levels: Strong, Bounded Staleness, and Eventually, with Bounded Staleness set as the default.

    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, executes the query only if all entities inserted before this timestamp are visible to query nodes.

    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, calculates the guarantee timestamp by subtracting this from the current timestamp.

    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.

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{#examples}

from pymilvus import MilvusClient

# 1. Set up a milvus client
client = MilvusClient(
    uri="https://inxx-xxxxxxxxxxxx.api.gcp-us-west1.zillizcloud.com:19530",
    token="user:password"
)

# 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 with iterator
res = client.query_iterator(
    collection_name="test_collection",
    filter="",
    limit=5,
) 

while True:
    result = iterator.next()
    if not result:
        iterator.close()
        break

    print(result)
    results += result