search()

This method conducts a vector similarity search.

Invocation

search(data, anns_field, param, limit, expr=None, partition_names=None, output_fields=None, timeout=None, round_decimal=-1, **kwargs)

Parameters

Parameter	Description	Type	Required
data	Data to search with	list[list[Float]]	True
anns_field	Name of the vector field to search on	String	True
param	Specific search parameter(s) of the index on the vector field	Dict	True
limit	Number of nearest records to return	Integer	True
expr	Boolean expression to filter the data	String	False
partition_names	List of names of the partitions to search on. All partition will be searched if it is left empty.	list[String]	False
output_fields	List of names of fields to output	list[String]	False
timeout	An optional duration of time in seconds to allow for the RPC. If it is set to None, the client keeps waiting until the server responds or error occurs.	Float	False
round_decimal	Number of the decimal places of the returned distance	Integer	False
kwargs _async _callback consistency_level guarantee_timestamp graceful_time travel_timestamp	Boolean value to indicate if to invoke asynchronously Function that will be invoked after server responds successfully. It takes effect only if _async is set to True. Consistency level used in the search Milvus searches on the data view before this timestamp when it is provided. Otherwise, it searches the most updated data view. It can be only used in Customized level of consistency. PyMilvus will use current timestamp minus the graceful_time as the guarantee_timestamp for search. It can be only used in Bounded level of consistency. Timestamp that is used for Time Travel. Users can specify a timestamp in a search to get results based on a data view at a specified point in time.	Bool Function String/Integer Integer Integer Integer	False

Return

A SearchResult object, an iterable, 2d-array-like class whose first dimension is the number of vectors to query (nq), and the second dimension is the number of limit (topk).

Raises

• RpcError: error if gRPC encounter an error.
• ParamError: error if the parameters are invalid.
• DataTypeNotMatchException: error if wrong type of data is passed to server.
• BaseException: error if the return result from server is not ok.

Example

search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
from pymilvus import Collection
collection = Collection("book")      # Get an existing collection.
results = collection.search(
    data=[[0.1, 0.2]], 
    anns_field="book_intro", 
    param=search_params, 
    limit=10, 
    expr=None,
    consistency_level="Strong"
)
results[0].ids
results[0].distances