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  3. Introduction to Embeddings with the OpenAI API
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Exercise

Sorting by similarity

Now that you've embedded all of your features, the next step is to compute the similarities. In this exercise, you'll define a function called find_n_closest(), which computes the cosine distances between a query vector and a list of embeddings and returns the n smallest distances and their indexes.

In the next exercise, you'll use this function to enable your semantic product search application.

distance has been imported from scipy.spatial.

Instructions

100 XP
  • Calculate the cosine distance between the query_vector and embedding.
  • Append a dictionary containing dist and its index to the distances list.
  • Sort the distances list by the 'distance' key of each dictionary.
  • Return the first n elements in distances_sorted.