code: def cosine_similarity(
      query_embeddings: BatchEmbeddings,
      index_embeddings: BatchEmbeddings,
  ) -> np.ndarray:
      """Compute cosine similarity between embeddings using numpy.
  
      Args:
          query_embeddings: Embeddings of the content to be searched.
  
          index_embeddings: Embeddings of the indexed content.
  
      Returns:
          Matrix of cosine similarity values.
      """
      similarity: np.ndarray = np.dot(
          query_embeddings, index_embeddings.T
      )
      return similarity
  
file_path: unisim/unisim/backend/onnx.py
imports: from pathlib import Path
  from typing import Any, Dict, Sequence, Union
  import numpy
  from onnxruntime import InferenceSession
  from ..types import BatchEmbeddings
label_desc: Use image node_id 1 for calling a global function with example usage: cosine_similarity(query_embeddings, index_embeddings) and returns: similarity