search()

This operation conducts a vector similarity search with an optional scalar filtering expression.

Request syntax

search(
    collection_name: str,
    data: Union[List[list], list],
    filter: str = "",
    limit: int = 10,
    output_fields: Optional[List[str]] = None,
    search_params: Optional[dict] = 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.

• data (List[list], list]) -

  [REQUIRED]

  A list of vector embeddings.

  Milvus searches for the most similar vector embeddings to the specified ones.

• 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.

• 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.

    • max_empty_result_buckets (int)

      This param is only used for range search for IVF-serial indexes, including BIN_IVF_FLAT, IVF_FLAT, IVF_SQ8, IVF_PQ, and SCANN. The value defaults to 1 and ranges from 1 to 65536.

      During range search, the search process terminates early if the number of buckets with no valid range search results reaches the specified value. Increasing this parameter improves range search recall.

  • ignore_growing (str) -

    This option, when set, instructs the search to exclude data from growing segments. Utilizing this setting can potentially enhance search performance by focusing only on indexed and fully processed data.

  For details on other applicable search parameters, refer to In-memory Index and On-disk Index.

• group_by_field (str)

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

• group_size (int)

  The target number of entities to return within each group in a grouping search. For example, setting group_size=2 instructs the system to return up to 2 of the most similar entities (e.g., document passages or vector representations) within each group. Without setting group_size, the system defaults to returning only 1 entity per group.

• strict_group_size (bool)

  This Boolean parameter dictates whether group_size should be strictly enforced. When strict_group_size=True, the system will attempt to fill each group with exactly group_size results, as long as sufficient data exists within each group. If there is an insufficient number of entities in a group, it will return only the available entities, ensuring that groups with adequate data meet the specified group_size.

• 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.

  This parameter is not applicable to Milvus Lite. For more information on Milvus Lite limits, refer to Run Milvus Lite.

• 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 Milvus rounds the calculated distances to.

    The value defaults to -1, indicating that Milvus 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

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 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
search_params = {
    "metric_type": "IP",
    "params": {}
}

# Search with limit
res = client.search(
    collection_name="test_collection",
    data=[[0.05, 0.23, 0.07, 0.45, 0.13]],
    limit=3,
    search_params=search_params
)

# [[{'id': 7, 'distance': 0.4801957309246063, 'entity': {}},
#   {'id': 2, 'distance': 0.3205878734588623, 'entity': {}},
#   {'id': 1, 'distance': 0.2993225157260895, 'entity': {}}]]

# Search with filter
res = client.search(
    collection_name="test_collection",
    data=[[0.05, 0.23, 0.07, 0.45, 0.13]],
    limit=3,
    filter='color like "red%"',
    search_params=search_params
)

# [[{'id': 1, 'distance': 0.2993225157260895, 'entity': {}},
#   {'id': 4, 'distance': 0.12666261196136475, 'entity': {}},
#   {'id': 6, 'distance': -0.3535143733024597, 'entity': {}}]]

# Search with an offset
res = client.search(
    collection_name="test_collection",
    data=[[0.05, 0.23, 0.07, 0.45, 0.13]],
    limit=3,
    offset=3,
    search_params=search_params
)

# [[{'id': 4, 'distance': 0.12666261196136475, 'entity': {}},
#   {'id': 3, 'distance': 0.11930042505264282, 'entity': {}},
#   {'id': 5, 'distance': -0.05843167006969452, 'entity': {}}]]

# Search with output fields
res = client.search(
    collection_name="test_collection",
    data=[[0.05, 0.23, 0.07, 0.45, 0.13]],
    limit=3,
    output_fields=["vector", "color"],
    search_params=search_params
)

# [[{'id': 7,
#    'distance': 0.4801957309246063,
#    'entity': {'color': 'grey_8510',
#     'vector': [-0.33445146679878235,
#      -0.25671350955963135,
#      0.8987540006637573,
#      0.9402995705604553,
#      0.537806510925293]}},
#   {'id': 2,
#    'distance': 0.3205878734588623,
#    'entity': {'color': 'orange_6781',
#     'vector': [0.4374213218688965,
#      -0.5597502589225769,
#      0.6457887887954712,
#      0.789405882358551,
#      0.20785793662071228]}},
#   {'id': 1,
#    'distance': 0.2993225157260895,
#    'entity': {'color': 'red_7025',
#     'vector': [0.19886812567710876,
#      0.060235604643821716,
#      0.697696328163147,
#      0.2614474594593048,
#      0.8387295007705688]}}]]

# Conduct a range search
search_params = {
    "metric_type": "IP",
    "params": {
        "radius": 0.1,
        "range_filter": 0.8
    }
}

res = client.search(
    collection_name="test_collection",
    data=[[0.05, 0.23, 0.07, 0.45, 0.13]],
    limit=3,
    search_params=search_params
)

# [[{'id': 7, 'distance': 0.4801957309246063, 'entity': {}},
#   {'id': 2, 'distance': 0.3205878734588623, 'entity': {}},
#   {'id': 1, 'distance': 0.2993225157260895, 'entity': {}}]]

Related methods

• delete()

• get()

• insert()

• query()

• upsert()