calc_distance()

This method calculates the distance between vectors.

Invocation

calc_distance(vectors_left, vectors_right, params=None, timeout=None, using='default')

Parameters

Parameter	Description	Type	Required
vectors_left	Vectors on the left of the operator.	Dict	True
vectors_right	Vectors on the right of the operator.	Dict	True
params	Parameters used for calculation. Key-value pair parameters: Key: "metric_type"/"metric"; Value: “L2"/"IP"/"HAMMING"/"TANIMOTO” (default is “L2”). Key: "sqrt"; Value: true or false (default is false - only for “L2” distance). Key: "dim"; Value: Integer (set this value if dimension is not a multiple of 8, otherwise the dimension will be calculated by list length - only for “HAMMING” and “TANIMOTO”).	Dict	True
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
using	Milvus connection used to calculate the distance.	String	False

Vector example

{"ids": [1, 2, 3, .... n], "collection": "c_1", "partition": "p_1", "field": "v_1"}

{"float_vectors": [[1.0, 2.0], [3.0, 4.0], ... [9.0, 10.0]]} or {"bin_vectors": [b'”', b'N', ... b'Ê']}

Params example

{"metric_type": "L2", "sqrt": true}

{"metric_type": "IP"}

{"metric_type": "HAMMING", "dim": 17}

{"metric_type": "TANIMOTO"}

Return

A two-dimensional array indicates the distances.

Example

vectors_left = {
    "ids": [0, 1], 
    "collection": "book", 
    "partition": "_default", 
    "field": "book_intro"
}
import random
external_vectors = [[random.random() for _ in range(2)] for _ in range(4)]
vectors_right = {"float_vectors": external_vectors}
params = {
    "metric": "IP", 
    "dim": 2
}
from pymilvus import Collection
collection = Collection("book")      # Get an existing collection.
collection.load()
from pymilvus import utility
results = utility.calc_distance(
    vectors_left=vectors_left, 
    vectors_right=vectors_right, 
    params=params
)
print(results)