search_iterator()

This operation returns a Python iterator for you to iterate over the search results. It is useful especially when the search result contains a large volume of data.

Request Syntax{#request-syntax}

def search_iterator(
    self,
    collection_name: str,
    data: Union[List[list], list],
    batch_size: Optional[int] = 1000,
    filter: Optional[str] = None,
    limit: Optional[int] = UNLIMITED,
    output_fields: Optional[List[str]] = None,
    search_params: Optional[dict] = None,
    timeout: Optional[float] = None,
    partition_names: Optional[List[str]] = None,
    anns_field: Optional[str] = None,
    round_decimal: int = -1,
    **kwargs,
) -> Union[SearchIteratorV2, SearchIterator]:

PARAMETERS:

• collection_name (str) -

  [REQUIRED]

  The name of an existing collection.

• data (List[list], list]) -

  [REQUIRED]

  A list of vector embeddings.

  Zilliz Cloud searches for the most similar vector embeddings to the specified ones.

• batch_size (int[]) -

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

• anns_field (str) -

  The name of the target vector field of the current search.

• filter (str) -

  A scalar filtering condition to filter matching entities.

  The value defaults to an empty string, indicating that no condition applies.

  You can set this parameter to an empty string to skip scalar filtering. To build a scalar filtering condition, refer to Boolean Expression Rules.

• filter_params (dict) -

  If you choose to use placeholders in filter as stated in Filtering Templating, then you can specify the actual values for these placeholders as key-value pairs as the value of this parameter.

• limit (int) -

  The total number of entities to return.

  You can use this parameter in combination with offset in param to enable pagination.

  The sum of this value and offset in param should be less than 16,384.

  In a grouping search, however, limit specifies the maximum number of groups to return, rather than individual entities. Each group is formed based on the specified group_by_field.

• output_fields (list[str]) -

  A list of field names to include in each entity in return.

  The value defaults to None. If left unspecified, only the primary field is included.

• search_params (dict) -

  The parameter settings specific to this operation.

  • metric_type (str) -

    The metric type applied to this operation. This should be the same as the one used when you index the vector field specified above.

    Possible values are L2, IP, and COSINE.

  • params (dict) -

    Additional parameters

    • radius (float) -

      Determines the threshold of least similarity. When setting metric_type to L2, ensure that this value is greater than that of range_filter. Otherwise, this value should be lower than that of range_filter.

    • range_filter (float) -

      Refines the search to vectors within a specific similarity range. When setting metric_type to IP or COSINE, ensure that this value is greater than that of radius. Otherwise, this value should be lower than that of radius.

    • level (int)

      Zilliz Cloud uses a unified parameter to simplify search parameter tuning instead of leaving you to work with a bunch of search parameters specific to various index algorithms.

      The value defaults to 1, and ranges from 1 to 5. Increasing the value results in a higher recall rate with degraded search performance.

    • page_retain_order (bool) -

      Whether to retain the order of the search result when offset is provided.

      This parameter applies only when you also set radius.

  For details on other applicable search parameters, read Index Vector Fields to get more.

• group_by_field (str)

  Groups search results by a specified field to ensure diversity and avoid returning multiple results from the same group.

• 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) -

  A list of partition names.

  The value defaults to None. If specified, only the specified partitions are involved in queries.

• kwargs -

  • offset (int) -

    The number of records to skip in the search 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.

  • round_decimal (int) -

    The number of decimal places that Zilliz Cloud rounds the calculated distances to.

    The value defaults to -1, indicating that Zilliz Cloud skips rounding the calculated distances and returns the raw value.

RETURN TYPE:

list[dict]

RETURNS: A list of dictionaries that contains the searched entities with specified output fields.

EXCEPTIONS:

• MilvusException

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

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
client.create_collection(
    collection_name="test_collection",
    dimension=5
)

# 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 a search with iterator
search_params = {
    "metric_type": "IP",
    "params": {}
}

# Search with limit
iterator = client.search_iterator(
    collection_name="test_collection",
    data=[[0.05, 0.23, 0.07, 0.45, 0.13]],
    batch_size=50,
    limit=20000,
    search_params=search_params
)

while True:
    result = iterator.next()
    if not result:
        iterator.close()
        break
    
    for hit in result:
        print(hit.to_dict())