User question:
user: I'm refining the data points in my machine learning model and need to compare the similarity of several vector pairs to fine-tune the system. Could you calculate the cosine similarities for the following pairs: [0.5, 0.7, 0.2, 0.9, 0.1] with [0.3, 0.6, 0.2, 0.8, 0.1], [0.2, 0.4, 0.6, 0.8, 1.0] with [1.0, 0.8, 0.6, 0.4, 0.2], and [0.1, 0.2, 0.3, 0.4, 0.5] with [0.5, 0.4, 0.3, 0.2, 0.1]?

Available functions:
name: calculate_cosine_similarity
description: Calculates the cosine similarity of two vectors.
parameters: type: dict
properties: vectorA: type: array
items: type: float
description: The first vector.
vectorB: type: array
items: type: float
description: The second vector.
required: vectorA
vectorB

Ground-truth function call(s):
calculate_cosine_similarity(vectorA=[0.5, 0.7, 0.2, 0.9, 0.1], vectorB=[0.3, 0.6, 0.2, 0.8, 0.1])
calculate_cosine_similarity(vectorA=[0.2, 0.4, 0.6, 0.8, 1.0], vectorB=[1.0, 0.8, 0.6, 0.4, 0.2])
calculate_cosine_similarity(vectorA=[0.1, 0.2, 0.3, 0.4, 0.5], vectorB=[0.5, 0.4, 0.3, 0.2, 0.1])